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How to Become a Data Analyst (with or Without a Degree)

If you enjoy working with numbers and solving puzzles, a career as a data analyst could be a good fit.

Data analysts gather, clean, and study data to help guide business decisions. If you’re considering a career in this in-demand field, here's one path to getting started:

Get a foundational education.

Build your technical skills.

Work on projects with real data.

Develop a portfolio of your work.

Practice presenting your findings.

Get an entry-level data analyst job.

Consider certification or an advanced degree.

Let's take a closer look at each of those seven steps.

How do I become a data analyst? A step-by-step guide

You can find data analytics jobs in all sorts of industries, and there’s more than one path toward securing your first job in this high-demand field. Whether you’re just getting started in the professional world or pivoting to a new career, here are some steps toward becoming a data analyst.

Learn more: What Does a Data Analyst Do? A Career Guide

1. Get a foundational education.

If you’re new to the world of data analysis , you’ll want to start by developing some foundational knowledge in the field. Getting a broad overview of data analytics can help you decide whether this career is a good fit while equipping you with job-ready skills.

It used to be that most entry-level data analyst positions required a bachelor’s degree. While many positions still do require a degree, that’s beginning to change. While you can develop foundational knowledge and enhance your resume with a degree in math, computer science, or another related field, you can also learn what you need through alternative programs, like professional certificate programs, bootcamps, or self-study courses.

2. Build your technical skills.

Getting a job in data analysis typically requires having a set of specific technical skills. Whether you’re learning through a degree program, professional certificate, or on your own, these are some essential skills you’ll likely need to get hired.

R or Python programming

SQL (Structured Query Language)

Data visualization

Data cleaning and preparation

Take a look at some job listings for roles you’d like to apply for, and focus your learning on the specific programming languages or visualization tools listed as requirements.

In addition to these hard skills, hiring managers also look for workplace skills, like solid communication skills—you may be asked to present your findings to those without as much technical knowledge—problem solving ability, and domain knowledge in the industry you’d like to work.

3. Work on projects with real data.

The best way to learn how to find value in data is to work with it in real world settings. Look for degree programs or courses that include hands-on projects using real data sets. You can also find a variety of free public data sets you can use to design your own projects. 

Dig into climate data from the National Centers for Environmental Information , delve deeper into the news with data from BuzzFeed , or come up with solutions to looming challenges on Earth and beyond with NASA open data. These are just a few examples of the data out there. Pick a topic you’re interested in and find some data to practice on.

Tip: For more inspiration, check out Coursera’s library of data analysis Guided Projects —a series of guided, hands-on experiences you can complete in under two hours.

4. Develop a portfolio of your work.

As you play around with data sets on the internet or complete hands-on assignments in your classes, be sure to save your best work for your portfolio. A portfolio demonstrates your skills to hiring managers. A strong portfolio can go a long way toward getting the job.  

As you start to curate work for your portfolio, choose projects that demonstrate your ability to:

Scrape data from different sources

Clean and normalize raw data

Visualize your findings through graphs, charts, maps, and other visualizations

Draw actionable insights from data

If you’ve worked on any group projects through the course of your learning, consider including one of those as well. This shows that you’re able to work as part of a team.

If you’re not sure what to include in your portfolio (or need some inspiration for project ideas), spend some time browsing through other people’s portfolios to see what they’ve chosen to include.

Tip: Sign up for a GitHub account and start posting your projects and code to the site. It’s an excellent spot to network with a community of data analysts, show off your work, and possibly catch the eye of recruiters.

5. Practice presenting your findings.

It can be easy to focus only on the technical aspects of data analysis, but don’t neglect your communication skills. A significant element of working as a data analyst is presenting your findings to decision makers and other stakeholders in the company. When you’re able to tell a story with the data, you can help your organization make data-driven decisions. 

What is data-driven decision-making (DDDM)?

Data-driven decision-making, sometimes abbreviated to DDDM), can be defined as the process of making strategic business decisions based on facts, data, and metrics instead of intuition, emotion, or observation.

This might sound obvious, but in practice, not all organizations are as data-driven as they could be. According to global management consulting firm McKinsey Global Institute, data-driven companies are better at acquiring new customers, maintaining customer loyalty, and achieving above-average profitability [ 1 ].

As you complete projects for your portfolio, practice presenting your findings. Think about what message you want to convey and what visuals you’ll use to support your message. Practice speaking slowly and making eye contact. Practice in front of the mirror or your classmates. Try recording yourself as you present so you can watch it back and look for areas to improve.

6. Get an entry-level data analyst job.

After gaining some experience working with data and presenting your findings, it’s time to polish your resume and begin applying for entry-level data analysts jobs. Don’t be afraid to apply for positions you don’t feel 100-percent qualified for. Your skills, portfolio, and enthusiasm for a role can often matter more than if you check every bullet item in the qualifications list.

If you’re still in school, ask your university’s career services office about any internship opportunities. With an internship, you can start gaining real world experience for your resume and apply what you’re learning on the job.

7. Consider certification or an advanced degree.

As you move through your career as a data analyst, consider how you’d like to advance and what other qualifications can help you get there. Certifications, like the Certified Analytics Professional or Cloudera Certified Associate Data Analyst, might help qualify you for more advanced positions at higher pay grades.  

Tip: Consider pursuing your data science degree online from an accredited university so you can continue working (and earning a paycheck) as you learn.

The University of Michigan School of Information’s online Master of Applied Data Science (MADS) degree is designed for aspiring data scientists to learn and apply skills through hands-on projects. You’ll learn how to use data to improve outcomes and achieve ambitious goals.

If you’re considering advancing into a role as a data scientist, you may need to earn a master’s degree in data science or a related field. Advanced degrees are not always required, but having one can open up more opportunities.

Learn more: Data Analyst vs. Data Scientist: What’s the Difference?

In this video, practicing data professionals offer their best advice for aspiring data analysts.

How to become a data analyst without a degree

A degree isn’t always necessary to get hired as a data analyst. Data analysts are in demand, and employers want to know that you have the skills to do the job. If you don’t have a degree, focus on making your portfolio shine with your best work. 

Get started with Coursera

If you’re looking to build job-ready data analyst skills without spending the time or money on a degree, consider the Google Data Analytics Professional Certificate through Coursera.

Learn how to clean and organize data with SQL and R, visualize with Tableau, and complete a case study for your portfolio—no prior experience or degree required. Upon completion, you can start applying for entry-level jobs directly with Google and more than 130 other US employers.

How to become a data analyst without experience

Often employers will want you to have experience working with data before taking a role as a data analyst. Luckily, you don’t have to wait to get hired to start gaining experience. Data is all around us. 

If you’re switching to data analysis from another field, start to develop your experience by working with data. Many degree programs, certificate courses, and online classes include hands-on projects with real data sets. You can also find free data sets on the internet (or scrape your own) to gain experience collecting, cleaning, analyzing, and visualizing real data.

Frequently asked questions (FAQ)

How many years does it take to become a data analyst ‎.

It can take anywhere from several months to several years to become a data analyst. The amount of time it takes you will depend on your current skill set, what type of educational path you choose, and how much time you spend each week developing your data analytics skills.

Learn more: Is Data Analytics Hard? Tips for Rising to the Challenge ‎

Can I be a data analyst without a degree? ‎

Yes, though a degree in a relevant field will likely improve your chances. While many positions will list a bachelor’s degree as a job requirement, it is possible to get hired with the right set of skills and experience. If you don’t have a degree (or a degree in a related field), be sure to spend extra time developing your portfolio to validate your abilities. ‎

Are data analysts in demand? ‎

Demand for skilled data analysts is growing — the World Economic Forum Future of Jobs 2020 report listed this career as number one in terms of increasing demand [ 2 ]. And hiring data analysts is a top priority across a range of industries, including technology, financial services, healthcare, information technology, and energy. ‎

What qualifications do you need to be a data analyst? ‎

Data analytics is a skill-based profession. Many positions will look for candidates with proficiency in SQL, Microsoft Excel, R or Python programming, data visualization, and presentation skills. Check some job listings in the industry you’re planning to apply to for more specific qualifications. ‎

Article sources

McKinsey & Company. " Five facts: How customer analytics boosts corporate performance , https://www.mckinsey.com/business-functions/marketing-and-sales/our-insights/five-facts-how-customer-analytics-boosts-corporate-performance." Access March 15, 2022.

World Economic Forum. " Data Science in the New Economy , http://www3.weforum.org/docs/WEF_Data_Science_In_the_New_Economy.pdf." Accessed March 15, 2022.

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This content has been made available for informational purposes only. Learners are advised to conduct additional research to ensure that courses and other credentials pursued meet their personal, professional, and financial goals.

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Lesson 6 of 10 By Avijeet Biswal

Table of Contents

Reviewed and fact-checked by Sayantoni Das

With the increase in data generation, the term "data analyst" has found its prominence today. However, to become a data analyst , there are a set of required data analyst skills and a few steps that have to be followed. In this article on how to become a data analyst, you will get an in-depth understanding of what you must do to become a data analyst, unearth the skills required to bag this position and you’ll get a step closer to your dream of becoming a data analyst.

To fulfill the responsibilities a data analyst must possess a vast and rich skillset:

1. Degree and Domain Expertise 2. Knowledge of Programming 3. Knowledge of Data Analysis Tools 4. Understanding of Statistics and Machine Learning Algorithms 5. Knowledge of Data Visualization Tools

The Ultimate Ticket to Top Data Science Job Roles

Who is a Data Analyst? 

A data analyst collects, processes, and performs analysis of large datasets. Every business, be it small or big, generates, and collects data. This data can be in the form of customer feedback, accounts, logistics, marketing research, and so on. 

A data analyst is a professional who takes this data and figures out numerous measures, such as how to improve customer experience, price new materials, and how to reduce transportation costs, to name a few. Data Analysts deal with data handling, data modeling , and reporting. 

Once you know who is a data analyst, it's paramount to know the roles and responsibilities of a data analyst.

Read More:  Looking for a better career growth, Ankit Dongare decided to switch careers from Operations to Data Science. His search for learning about data in detail took him to our Data Analyst Master’s Program. Check out his experience with our program and his career growth in his Simplilearn Data Analyst Course Review .

Why Start a Career as a Data Analyst?

The demand for data analysts is at an all-time high. According to the US Bureau of Labor Statistics, the employment of data analysts is projected to grow much faster than the average for all occupations in the coming years . It is due to the fact that as companies undergo digital transformation, they are generating large amounts of data that they may need to learn how to process, manage, or use properly. It is where data analysts provide valuable insights and interpretations of data that can benefit the business.

Starting a career as a data analyst can be an excellent choice for several reasons:

• Growing Demand: With the increasing use of data in business decision-making, there is a growing demand for professionals with skills in data analysis. According to the US Bureau of Labor Statistics, the employment of data analysts is projected to grow much faster than the average for all occupations in the coming years.
• High Earning Potential: Data analysts are often well-paid, with salaries varying depending on experience, industry, and location. Data analysts can often earn competitive salaries right out of college.
• Variety of Opportunities: Data analysts can work in various industries, from healthcare and finance to marketing and retail. It means many different types of jobs and career paths are available for those with data analysis skills.
• Challenging and Interesting Work: Data analysis can be difficult and rewarding, requiring problem-solving skills and attention to detail. Analysts are often tasked with finding insights in large and complex data sets, which can be exciting and intellectually stimulating.
• Transferable Skills: The skills developed in data analysis, such as statistical analysis, data visualization, and data management, can be applied to various other fields and industries. It means that data analysts can build valuable transferable skills that can be useful throughout their careers.

Your Data Analytics Career is Around The Corner!

Types of Data Analysts

Depending on your interests and skill set, you can pursue several types of Data Analyst roles. Some common types of Data Analysts include:

Business/Data Analyst

A Business/Data Analyst is responsible for collecting, analyzing, and interpreting complex data sets to help companies make informed decisions. They work closely with stakeholders to identify business requirements and design supporting data models. They may also develop reports and dashboards to present data insights to decision-makers.

Marketing Analyst

A Marketing Analyst uses data to help companies understand their customers and develop marketing strategies. They analyze customer behavior, demographic data, and market trends to help companies effectively target their marketing efforts. They may also build marketing performance metrics to track the success of marketing campaigns.

Financial Analyst

A Financial Analyst uses data to help companies make financial decisions. They may analyze financial data such as revenue, expenses, and profitability to help companies identify areas for improvement or growth. They may also develop economic models to forecast future performance and inform strategic planning.

Healthcare Analyst

A Healthcare Analyst uses data to help healthcare organizations improve patient outcomes and reduce costs. They may analyze healthcare data such as patient records, clinical trials, and insurance claims to identify trends and patterns. They may also develop predictive models to help healthcare providers make more informed decisions.

Data Scientist

A Data Scientist is responsible for designing and developing complex algorithms and models to solve data-driven problems. They work with large, complex data sets and use advanced analytical techniques to extract insights and develop predictive models. They may also work with other Data Analysts to develop data-driven solutions for businesses.

Become a Data Scientist With Real-World Experience

Becoming a data analyst involves a series of steps that encompass education, skill development, and practical experience. Here's a comprehensive guide to help you on your journey to becoming a data analyst:

1. Education and Prerequisites

• Educational Background: A bachelor's degree in a related field such as statistics, mathematics, computer science, economics, engineering, or information technology is often required. Some positions might even require a master's degree.
• Mathematics and Statistics: Develop a strong foundation in mathematics and statistics, including concepts like probability, linear algebra, calculus, and inferential statistics. These skills are fundamental to data analysis.
• Programming Languages: Learn programming languages commonly used in data analysis, such as Python or R. Python is particularly popular due to its adaptablity and extensive libraries for data manipulation and analysis.
• Database Skills: Gain familiarity with databases and SQL (Structured Query Language), as data analysts often work with large datasets stored in databases.

2. Develop Essential Skills

• Data Manipulation and Cleaning: Learn how to clean, preprocess, and transform data to ensure it's ready for analysis. This involves dealing with missing values, outliers, and data inconsistencies.
• Data Visualization: Master data visualization techniques using tools like Matplotlib, Seaborn (Python), or ggplot2 (R) to create meaningful and informative graphs, charts, and visual representations of data.
• Statistical Analysis: Acquire proficiency in applying statistical techniques like hypothesis testing, regression analysis, and descriptive statistics to draw insights from data.
• Machine Learning Basics: Familiarize yourself with basic machine learning concepts to understand how to apply predictive and classification models.
• Critical Thinking and Problem Solving: Develop strong analytical and critical thinking skills to approach complex problems and derive actionable insights from data.

3. Gain Practical Experience

• Projects and Portfolio: Work on personal or collaborative data analysis projects to showcase your skills. Create a portfolio that highlights your ability to handle real-world data and provide insights.
• Internships or Entry-Level Positions: Seek internships or entry-level positions that allow you to apply your skills in a professional setting. These experiences provide valuable exposure and networking opportunities.

Future-Proof Your AI/ML Career: Top Dos and Don'ts

4. Master Additional Tools and Techniques

• Advanced Data Analysis Tools: Explore more advanced data analysis tools and libraries, such as Pandas, NumPy, scikit-learn (Python), or dplyr, tidyr (R), to deepen your analytical capabilities.
• Big Data Technologies: Familiarize yourself with big data technologies like Hadoop and Spark, as dealing with large datasets is a crucial aspect of data analysis.

5. Continuous Learning and Growth

• Stay Updated: Data analysis is a rapidly evolving field. Keep pace with cutting-edge methods, tools, and trends through book exploration, conference engagement, and online course participation.
• Online Courses and Certifications: Enroll in online data analyst courses like Simplilearns Data Analyst Mater's Program or Post Graduate Program in Data Analytics .

6. Networking and Professional Development

• Network: Attend industry events, workshops, and meetups to connect with other professionals in the field. Networking can open up opportunities for learning, collaboration, and job prospects.
• LinkedIn and Online Communities: Create a strong LinkedIn profile to showcase your skills and connect with potential employers. Engage in online forums and communities related to data analysis to learn from others and share your insights.

7. Job Search and Career Growth

The world of data analytics offers diverse job opportunities for those with data skills. Some popular roles that require data analysis expertise include Data Analyst, Business Analyst, Data Scientist , Data Engineer, Data Architect , and Marketing Analyst.

However, the field of data analytics is constantly evolving, and new applications for data analysis are being developed all the time. As a result, job titles and responsibilities may also continue to change and expand.

Given the high demand for data professionals, consulting firm McKinsey & Co. predicts that demand may outpace the projected supply of data professionals by 50 or 60 per cent in the coming years. Those with solid data analysis skills will have many opportunities to build a successful career in this field.

To increase your chances of success, staying up-to-date with the latest data analysis tools and technologies and continually developing your skills through practice projects and real-world experience is essential. By doing so, you can build a strong portfolio of work and position yourself as a highly skilled and sought-after data professional.

Data Analyst Salary

How to become a data analyst is the real question, however, there’s one motivation that will surely push you forward to becoming one - and that salaries potential!

According to Glassdoor, a data analyst in the United States can earn a minimum salary of $43K to a maximum of $95K per year.

Fig: Data Analyst Salary in the U.S

In India, a data analyst can earn an average salary of 500K per annum.

Fig: Data Analyst Salary in India

The average salary of a data analyst in Australia is around A$83K per annum.

Fig: Data Analyst Salary in Australia

In the United Kingdom , a data analyst earns an average salary of £30K per annum. 

Fig: Data Analyst Salary in the U.K

Data analysts have a lot of scope in today's times, as companies are on the lookout for professionals who can efficiently and effectively handle their data. 

The companies hiring for the position of data analysts are Amazon, Microsoft, Capital One, Walmart, PayPal, Google, Facebook, Twitter, Bloomberg, and Apple, to name a few. 

Fig: Companies hiring Data Analysts

Are you overwhelmed with all the information and cannot figure where and how to start your learning journey? We have your back. Here’s how Simplilearn can help you learn Data Analytics and make you a seasoned Data Analyst.

Data Analyst Qualifications and Prerequisites

There are a few key qualifications and prerequisites that data analysts should possess in order to be successful in their role. Firstly, data analysts should have strong mathematical skills and be able to effectively analyze data sets. Secondly, they should be well-versed in using statistical software packages such as SAS, R, or SPSS. Finally, data analysts should also have strong communication skills in order to be able to effectively present their findings to clients or other stakeholders.

Possessing these qualifications and prerequisites will put data analysts in a good position to excel in their roles and provide valuable insights that can help organizations make better decisions.

Join The Ranks of Top-Notch Data Analysts!

What Is the Workplace/Work Environment of a Data Analyst Like?

A Data Analyst's workplace and work environment can vary depending on their industry and company. Data Analysts may work in a range of settings, including traditional office environments, remote work, or a combination of both.

Data Analysts may work independently or as part of a team, collaborating with other data professionals such as Data Scientists, Business Intelligence Analysts , or Data Engineers. They may also work closely with other departments, such as marketing, finance, or operations, to provide insights and recommendations.

Data Analysts typically spend a lot of their time analyzing and manipulating data using various tools and software. They may also be responsible for data cleaning and preparation, building and testing models, and creating data visualizations or reports to communicate their findings.

In some cases, Data Analysts may be required to work with large datasets that require specialized hardware or cloud-based computing resources. In such cases, they may work closely with IT professionals to set up and manage these resources.

In short, the work environment of a Data Analyst is typically fast-paced, detail-oriented, and focused on problem-solving. Data Analysts are expected to be self-motivated, highly analytical, and able to communicate their findings effectively to stakeholders.

How Can Simplilearn Help You?

If you're looking to become a data analyst, then our  Data Analyst master course, is a great choice. The course will cover Introduction to Data Analytics, Business Analytics with Excel, Tableau training, and Power BI. You will also learn Programming Basics and Data Analytics with Python, R programming, and finally, you will get to work on a Capstone project.

This program will help you become a data analyst pro. And now you know the best way to become a data analyst seamlessly. The tools covered in this program are Excel, NumPy, Pandas, SciPy, IBM Watson, Power BI, Tableau, Python, and R. 

Now that you know what data analytics is, who a data analyst is, the responsibilities of a data analyst, and how to become a data analyst, you’re all set to start your learning journey and prepare for a data analyst interview . So what are you waiting for? Your dream job role is a click away. Start learning now!

In case you have any questions on this article ‘How To become a Data Analyst’? mention them in the comment section below Our experts will get back to you as soon!

Data Analyst FAQs

1. what does a data analyst do.

A: Data analysts collect, organize, and analyze data to help businesses make better decisions. They use their skills in math and statistics to find trends and insights in data sets, then communicate their findings to decision-makers.

2. What skills do I need to be a data analyst?

A: To be a successful data analyst, you need strong math and analytical skills. You must be able to think logically and solve problems, and have attention to detail. Additionally, you must be able to effectively communicate your findings to those who will make decisions based on your analysis.

3. What education do I need to be a data analyst?

A: Some data analysts have a bachelor’s degree in math, statistics, economics, computer science, or another quantitative field. However, with the right skills and experience, it is possible to become a data analyst without a college education in this area. More employers are also favoring candidates who have earned certifications with a sole focus in data analysis.

4. What are some common software programs used by data analysts?

A: Data analysts commonly use spreadsheet programs such as Microsoft Excel or Google Sheets to organize and analyze data. They may also use statistical analysis software, such as SAS or SPSS, to conduct more complex analyses. Additionally, data analysts may use database management software, such as MySQL or Oracle, to work with large data sets.

5. What are some common challenges faced by data analysts?

A: Data analysts may face several challenges in their work. First, they may need to clean or “wrangle” data sets that are messy or incomplete. Second, they may need to find trends or insights in large data sets, which can be difficult and time-consuming. Third, they may need to effectively communicate their findings to those who make decisions based on their analysis. Finally, data analysts may need to stay up-to-date on new analytical methods and software programs.

6. How long does it take to become a data analyst?

The time it takes to become a data analyst can vary depending on your prior experience and education, the amount of time you can commit to learning, and the specific skills and knowledge you need to acquire. A bachelor's degree in a related field like statistics, computer science, or mathematics is required to become a data analyst. Obtaining a bachelor's degree can take around four years of full-time study.

However, learning the necessary skills through self-study or a boot camp-style program is also possible. These programs can range from a few weeks to several months, depending on the intensity and duration of the program. Once you have the necessary education or skills, it may take some time to build up experience in the field before securing a full-time data analyst position.

7. What tools do data analysts use?

Data analysts use a wide range of tools to analyze and manipulate data. Here are some of the most common tools used by data analysts:

• Microsoft Excel
• Google Analytics
• Adobe Analytics

8. Is data analysis a good career?

Yes, data analysis is generally considered a promising career with solid job prospects and competitive salaries. The demand for data analysts is growing rapidly as more and more companies realize the importance of data-driven decision-making. With the right skills and experience, data analysts can advance to more senior roles such as data scientist or analytics manager. Additionally, the skills required for a data analyst role are transferable across industries, allowing for a range of career opportunities.

9. Can anyone become a data analyst?

Having a background in computer science, mathematics, statistics, or related fields may make it easier to become a data analyst, but anyone with the right skills and determination can become a data analyst. The key is to gain proficiency in the necessary technical skills like programming languages (such as Python, R, and SQL), data visualization tools (like Tableau and PowerBI), database management tools, and the ability to analyze and draw insights from data.

Soft skills like communication, problem-solving, and critical thinking are also essential. While formal education in data analysis or a related field can be helpful, many online resources, courses, and boot camps are available for anyone interested in learning data analysis. With dedication and a willingness to learn, anyone can become a data analyst.

10. Is data analyst a coding job?

Yes, data analytics often requires coding skills. While some data analysis tools allow for visual manipulation of data without codings, such as Tableau, Power BI, or Excel, proficiency in programming languages like Python, R , SQL, and Java can be highly beneficial in performing advanced analysis and building custom data models. In addition, knowledge of programming and scripting languages can help automate repetitive tasks and streamline data analysis processes.

11. Is data analyst an IT job?

A Data analyst role is not necessarily an IT (information technology) job but requires working with IT tools and systems. Data analysis involves using statistical and computational techniques to derive insights from data, which can be applied in various industries such as healthcare, finance, marketing, and more. While data analysts need to have a basic understanding of IT tools, such as programming languages and database systems, their primary focus is on analyzing data and interpreting results to drive business decisions.

12. What is the difference between a data analyst and a data scientist?

Data analysts and data scientists are both data professionals, but there are some critical differences in their roles, skills, and responsibilities:

• Role: Data analysts primarily work with structured data to derive insights and make data-driven decisions. Data scientists use structured and unstructured data to develop and implement predictive models and algorithms.
• Skills: Data analysts require strong skills in data management, statistical analysis, data visualization, and business domain knowledge. Data scientists need advanced machine learning, programming, and data engineering skills.
• Responsibilities: Data analysts typically focus on data cleaning, data analysis, and reporting, while data scientists focus on developing and deploying predictive models and algorithms that can drive business outcomes.

13. Can I become a data analyst without a degree?

Yes, it is possible to become a data analyst without a degree. While having a degree in a related field, such as computer science, statistics, or mathematics, may be preferred by some employers, it is only sometimes a requirement.

To become a data analyst without a degree, you can gain the necessary skills through self-learning, online courses, boot camps, or relevant certifications. You can learn programming languages like Python or R, SQL, and data visualization tools like Tableau or PowerBI .

Creating a strong portfolio of personal projects and participating in online communities or hackathons can also help demonstrate your skills to potential employers. Additionally, networking and reaching out to professionals in the field can help you learn more about the industry and increase your chances of finding job opportunities.

14. Can you learn data analytics on your own?

Yes, it is possible to learn data analytics on your own. Many online resources are available for learning data analytics, including tutorials, courses, and online communities. 

In addition to online learning, you can gain practical experience by working on personal projects, participating in data analysis competitions, or contributing to open-source projects. Networking with other data analysts and attending industry events can also help you gain knowledge and insights into the field.

However, it's important to note that while it is possible to learn data analytics on your own, a formal education or relevant work experience can provide valuable credentials and networking opportunities.

15. Is data analytics math-heavy?

Yes, data analytics is a math-heavy field. A solid understanding of mathematics, including statistics, is essential for data analysis. Data analysts need to be able to work with large datasets, use statistical methods to analyze the data and apply mathematical models to interpret the results. They may also need programming languages like Python and R to write and run statistical models and algorithms. While a foundation in math is essential, it's also important to note that data analysts can use various tools and software to help with the heavy lifting, such as Excel, Tableau, and PowerBI.

16. Is data analyst a high salary job? 

Data analyst positions can offer competitive salaries, especially with experience and expertise. However, actual salaries may vary depending on the industry, location, and company size.

17. Can a fresher become a data analyst? 

Yes, fresh graduates with relevant degrees and analytical skills can start their careers as entry-level data analysts. Gaining experience through internships, projects, or certifications can enhance their chances of landing a data analyst position.

Find our Data Analyst Online Bootcamp in top cities:

About the author.

Avijeet is a Senior Research Analyst at Simplilearn. Passionate about Data Analytics, Machine Learning, and Deep Learning, Avijeet is also interested in politics, cricket, and football.

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Sep 19, 2023 by Corey Ginsberg

Each day, huge amounts of data are being created. This has led to a corresponding need for those with specialized training to find methods to make sense of it all so that patterns and insights can be found amid the numbers. More than ever, Data Analysts are in demand in industries across the US.

In 2021, Glassdoor named data science as the second-most popular job in America. If you are considering a career in data analytics, it’s important to have the proper qualifications and skill-set to get the attention of employers. This article will explore what kind of training a Data Analyst needs, as well as which degrees are appropriate not just to secure a job in this field, but to advance professionally as a Data Analyst.

What College Majors are Most Applicable to Data Analytics?

A bachelor’s degree in data analytics isn’t offered at all universities. For those who are interested in launching a career as a Data Analyst, the following majors can provide relevant training:

• Computer Science: Those pursuing this commonly available major receive training in statistical and analytical skills. Some programs offer courses in programming, algorithm design, and artificial intelligence as part of a computer science major, all of which can be applied to the field of data analytics.
• Statistics/Applied Mathematics: Unlike general degrees in mathematics that tend to prepare students for academic careers, focusing on statistics or applied mathematics offers learners real-world training, which can be used in the business sector for making data-driven decisions. Some curricula even have classes that teach students programming or statistical software packages.
• Data Science: This major is growing in popularity, as the need for data professionals continues to increase. Students pursuing a degree in data science typically study statistics, mathematics, and computer science. The skills learned in this major have direct applications for those hoping to specialize in a field such as healthcare, finance, economics, or business.
• Psychology: Although this doesn’t at first seem to be an obvious major for someone interested in data analytics, those with a degree in psychology gain a wide range of skills that have applications in many industries. Trained Psychologists work with data to explain and predict patterns in human behavior. Most colleges and universities that offer this major require students to take math and statistics classes, which are helpful for a career in data analysis.
• Management Information Systems: This degree teaches learners how databases function. Coursework often includes topics like data management, business theory, and database design. Programs offering this track sometimes allow students to focus specifically on data management, business intelligence, or even data analytics.
• Economics/Finance: For those looking to become Business Analysts or Financial Analysts, a degree in finance or economics is a logical choice. Classes typically cover analysis, statistics, and even business analytics.

Regardless of the major you select, most employers seek out candidates who have a strong background in mathematics for entry-level data analytics positions.

Is Graduate Study Necessary for a Career in Data Analytics?

For entry-level data analytic jobs, a bachelor’s degree is a common qualification. However, some businesses or companies prefer candidates who have a master’s or even doctorate degree in data analytics or one of the fields directly related to it, such as computer science or math. Like most vocations, having a higher-level degree corresponds to higher pay.

While it is not a requirement to have an undergraduate or even graduate degree in a field related to data analytics in order to pursue a career as a Data Analyst, the industry-specific training these degrees provide can help gain the attention of employers and scouts.

Non-Academic Requirements for Those Interested in Becoming Data Analysts

In addition to college, university, or certificate coursework, which can provide the necessary training for aspiring Data Analysts, most employers also value soft skills that don’t necessarily come from academia. These transferable skills are becoming an increasingly popular consideration for employers, as they hope to find well-rounded applicants whose knowledge and training extend beyond the classroom. Here are a few soft skills employers seek out in Data Analysts:

• Communication
• Optimistic outlook
• Critical thinking skills
• Entrepreneurship

The bottom line is that there is no path set in stone that must be followed to be guaranteed a job as a Data Analyst. Those who can demonstrate their technical and soft skills to potential employers have the best shot at landing an entry-level job, regardless of their academic background. However, for candidates hoping to climb the professional ladder and be hired for senior-level data analytic roles, educational training at a college, university, or school such as Noble Desktop opens many professional doors.

Hands-On Data Analytics Classes

Noble Desktop’s data analytics classes provide students with timely and relevant training about how to manage, analyze, and visualize data. These courses are offered in New York City, as well as in the live online format, in topics like Python, Excel, and SQL, and can help you break into a career in data analytics.

In addition, a variety of analytics classes are currently available from top providers. More than 130 live online data analytics courses are listed, in topics like FinTech, Excel for Business, and Tableau, among others. Courses range from three hours to six months and cost from $219 to $27,500.

Those who are committed to learning in an intensive educational environment may also consider enrolling in a data analytics or data science bootcamp . More than 90 bootcamp options are available for beginners, intermediate, and advanced students looking to master skills and topics like data analytics, data visualization, data science, and Python, among others. These rigorous courses are taught by industry experts and provide timely, small-class instruction.

Are you looking for a data analytics class nearby? If so, Noble’s Data Analytics Classes Near Me tool provides an easy way to locate and browse approximately 400 data analytics classes currently offered in in-person and live online formats. This convenient tool makes it easy to find the class that’s a match for your professional goals and budget. Course lengths vary from three hours to 36 weeks and cost $119-$27,500.

• Columbia Engineering Boot Camps
• Data Analytics

How to Become a Data Analyst (With or Without a Degree)

Almost every sector makes use of data to solve problems and gain insight into trends and developments. But raw data isn’t enough to work with — organizations need talented data analysts to extract meaning from the information they have to work with. If you’re a solutions-oriented individual with a knack for finding patterns, a career in data analysis may be for you.

The following steps may help you become a data analyst:

• Reviewing your fundamental math
• Getting an education
• Boosting your soft skills
• Considering further education
• Preparing for the job search
• Applying for jobs

In this article, we’ll not only cover the above steps in more detail, but we’ll also consider what a career path to becoming a data analyst might look like along with the skills required for it. Every data analyst’s career path will look slightly different depending on their chosen industry and specific role. However, the following steps are highly recommended for anyone who wishes to become a data analyst.

1. Reviewing Your Fundamental Math

As with any scientific career, data analysts require a strong grounding in mathematics to succeed. It may be necessary to review and, if necessary, improve your math skills before learning how to become a data analyst. Check out the list below for a few key areas of study!

As arguably the most important area of expertise for a data scientist, the field of statistics underpins every aspect of data analysis. Having an understanding of statistics means being able to parse extensive data sets for high-level insights. Common statistical concepts to know include probability distributions, statistical features, and Bayesian statistics.

College-level algebra is frequently used in data analytics. In particular, linear algebra is necessary for any professional who aims to work with machine learning and/or AI, as most algorithms make use of it. Given the upswing in machine learning and AI integration in data science, these fundamentals are likely to become increasingly important over time, too.

Similar to algebra, calculus is necessary for working with AI, and can be used to help teach pattern recognition to machines. Functions like gradient descent or derivatives are among the most common found in data science.

Discrete mathematics

Discrete mathematics is the backbone of the computer systems used in data analytics, making understanding it a necessity. The study of discrete mathematics requires abstract thinking and knowledge of the reasoning that comes with mathematical thought. Relevant areas of study include logic, proofs, and data structures.

Additional Resources

• Calculus 1 — Khan Academy
• Head First Statistics — Dawn Griffiths
• Discrete Mathematics — Oscar Levine

2. Getting an Education

Regardless of career path, employers will expect a certain level of education from incoming data analysts. There are several ways for a learner to fulfill these data analyst education requirements. While a college degree is the most common — with a master’s in data science or doctoral degree expected for high-level data analysts — other paths are also available if you’re willing to put in the work and dedication. Data science bootcamps can often provide a quicker turnaround on building foundational skills and greater flexibility for analytics professionals seeking an alternative to a traditional education.

College degrees, whether achieved online or in-person, offer comprehensive coverage of data science and analytics principles. An increasing number of colleges and universities are offering courses that include specializations in fields such as analytics, data engineering, or artificial intelligence. However, for some entry-level positions, even a degree in mathematics, statistics, economics, or computer science can serve as a good starting point.

That said, some aspiring data analysts may not have the time, resources, or inclination to pursue a four-year degree and could be better served by an alternative educational route. If you’re wondering how to become a data analyst without a degree, you may want to consider a data analytics bootcamp.

A data analytics bootcamp is a faster and often less expensive option when compared with formal degrees. Though fast paced and rigorous, bootcamps help prepare you with job-ready skills and take as little as three to six months to complete. Learning from one of these courses means getting a grounding in the most in-demand data skills on the market and benefiting from a supportive and collaborative learning environment . In addition, most bootcamps offer flexible attendance options, such as online classes, for those who wish to keep working while they learn how to become a data analyst.

Get Boot Camp Info

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Neither path is strictly the “correct” one — both have advantages for different lifestyles, skill levels, and learning preferences. For instance, someone with existing experience seeking a quick career pivot may be better suited for the rapid job readiness that a bootcamp provides.

In the end, the decision will hinge on what’s best for you and your situation.

3. Boosting Your Soft Skills

No one can get by on technical savvy alone.

The often holistic nature of data analysis requires aspiring analysts to hone their soft skills as well. A data professional may be able to extract useful information from a massive volume of data, but this means nothing if they are unable to make use of it in a professional environment.

No matter where you are in your career, you should be able to explain your findings to a variety of audiences. Data analysts work with information that impacts all levels of an organization, so being able to convey findings to colleagues who possess varying levels of technical knowledge is a must.

Data analysts should also be critical thinkers and problem solvers who can see all angles of a problem and find a fitting solution. With this comes a thorough understanding of the organization, its needs, and what can be done to improve operations. A curious nature is also an ideal trait for those seeking data analyst careers; after all, at its core, this field centers on asking questions and uncovering information.

Though it may seem counterintuitive, soft data analyst skills can be taught. Some online courses can provide everything from a basic overview to an intensive study of business-relevant soft skills. Seeking mentorship or feedback from a professional colleague can also give you a trajectory for improvement specific to your skills.

• Data Science and the Art of Persuasion — Harvard Business Review
• Tell Stories With Data: Communication in Data Science — Towards Data Science
• 10 Soft Skills Every Data Scientist Should Have — Tableau

4. Considering Further Education

Even if you already have a job as a data analyst, your education may not be over. As previously mentioned, senior-level positions in the field may require a master’s degree or even a doctorate. Such positions may also require three to six years of additional learning. Of course, this added investment comes with a strong return — having more experience and education can significantly boost your earning potential and growth (PDF, 1.9 MB) in your data analyst career path.

Of course, growth rate and requirements may vary depending on the industry. While a graduate or postgraduate degree may open more opportunities for you, it may not be necessary when pursuing an entry-level role.

5. Preparing for the Job Search

Wondering how to get an entry-level data analyst job?

Well, once you’ve completed an educational program that fits your goals, learning preferences, and lifestyle, you can kick your job search into gear. Data analysts are in demand in almost every industry you can think of — from healthcare and marketing to big tech and finance. Your opportunities for employment as a data professional are near-endless.

But before you start sending out applications, it’s worth laying some groundwork.

If you’ve completed projects as part of your education or elsewhere, you may want to consider compiling your best work in a portfolio. Update it frequently as you complete more work and include it in your applications — interested employers will often be more compelled by the practical work you can do than any cover letter.

You should also think about networking in the data analytics field. Depending on your location, some cities may offer meetups for current professionals in the industry, and large conferences are a great way to learn about industry updates and establish more contacts.

You’ll also want to update your resume and online presence with the most recent possible information, including any education or data analyst certifications you’ve recently received. If you have the time, it can be worth combining all of this information into a personal website to send to prospective employers.

• How to Write a Winning Data Science Resume — Towards Data Science
• How to Build an Awesome Data Science Portfolio — freeCodeCamp
• The Data Science Interview Study Guide — KDnuggets

6. Applying for Jobs

It can be intimidating to take your first steps into such a competitive industry, but there are plenty of opportunities for a skilled data analyst, even if you’re new to the workforce! Go into your job hunt with goals in mind for your desired salary, company culture, and industry. Are you willing to earn more data analyst certifications to stand out? Can you relocate if needed? Being able to answer these questions decisively can be appealing to employers.

When searching, tailor your materials to the companies you apply for. Certain roles may place emphasis on specific skills, so don’t be afraid to highlight those when applying. This is especially important considering the breadth of expertise required in the field of data analytics.

Most importantly, don’t be deterred by rejection! If you’re passionate about data analytics and have the education to perform well, you’ll likely find an opportunity that fits your needs. Even if you feel underqualified for a position, apply anyway — any bit of practice can help you land the next interview.

What Do Data Analysts Do?

While most data analysts share a common set of proficiencies, an analyst’s specific responsibilities and daily schedule may look different depending on their industry and specific role.

What does a day at work look like for a data analyst?

Though used broadly to indicate an assortment of data science roles , “data analyst” is a job title often used for entry-level positions in the field. An analyst may spend their day accessing and cleaning data, finding ways to answer business questions and solve problems, and communicating their findings to other teams.

As you advance in your career as an analyst and gather more data analyst skills, you may end up advancing into a data scientist role. As a data scientist, you would work with algorithms to develop data-based predictions. Other data analyst careers include business analysis, which centers on turning insights into strategic action, or software engineering, which combines programming knowledge and data science to facilitate analytics-recommended changes to company software.

What tools do data analysts use?

Every aspiring data analyst will need to have command over a variety of industry-specific tools. Below, we’ve put together a brief data analyst skills checklist that analysts in training should include in their academic agenda.

Programming Languages

Most data analytics positions require a grounding in one or more industry-relevant programming languages. (These are explained in more detail in the next section.)

Data Visualization Tools

Useful for analysts and laymen alike, data visualization tools can help you organize the data you work with and impart your findings to others in your organization.

• Google Charts
• Datawrapper

Checklist of Skills Required to Become a Data Analyst

Though companies may seek out professionals with slightly different skill sets, particularly across different positions, several data analyst skills reign supreme among employers.

SQL and NoSQL

Despite being created in 1970, SQL remains one of the most popular database query languages in use today. Though other languages have since come into prominence in the data science field, SQL is still widely used enough to be an important tool in any analyst’s arsenal.

Learning NoSQL is also important. NoSQL subverts the usual relational setup on a SQL database, and as such, there is no standard NoSQL framework. This flexibility tends to make NoSQL databases better at handling massive data troves than their relational counterparts — and thus, more attractive to businesses that need to manage large amounts of data.

MATLAB is a programming language and computing environment that is increasingly favored among businesses for its facilitation of quick data cleaning, organization, visualization, and compatibility with any machine learning model. The efficiency gains provided by MATLAB make it sought after among those working with Big Data. As such, MATLAB proficiency can be a good addition to your resume.

R is a programming language with structure and syntax designed to support analysis. It includes several common data organization commands by default and appeals to larger businesses for its ability to handle large or complex stores of data.

Python is a more general-purpose programming language that is a must-learn for its versatility . Many of Python’s libraries are relevant for the use of artificial intelligence, and as such, makes the language increasingly important to a data analytics industry that relies on AI. Some ancillary programs for Python include Pandas and NumPy.

Data visualization

As previously mentioned, data visualization helps bridge the gap between the technical silo of data analytics and non-technical business stakeholders. Being data literate means being able to share complex ideas with business decision-makers and have them understand at a glance. There’s an element of storytelling with data visualization — you need to be able to take your insights and create an actionable narrative for others in your organization.

Data cleaning

Not all data is valuable — or even accurate. Before they can use gathered data, analysts must sift through and “clean” it to ensure that only accurate, reliable information is under consideration. Even with a sophisticated algorithm, the intervention of a data analyst is necessary to make proper use of a machine learning system. Many data analysts spend a significant portion of their time cleaning data.

Machine learning

Even if you don’t specialize in working with machine learning and AI, understanding it is important given its outsized presence in the data science industry. A familiarity with AI-relevant tools and concepts can give you an edge in a job search.

Career Paths in Data Analysis, Visualization, and Science

Not sure where you want to take your career in data analytics? Any of the following jobs are options for someone who has recently completed a data analytics college program or bootcamp.

• Data Analyst: Data analysis is a common, entry-level role responsible for processing large amounts of information to generate insights, often through the use of assorted analytical tools.
• Data Scientist: Data scientists hone machine learning systems and algorithms to model recent developments and predict new ones.
• Business Analyst: A business analyst makes use of data to answer questions about business strategy, often visualizing data through tools such as Excel and Powerpoint.
• Software Engineer: This role centers around the intersection of data science and programming. Software engineers design new software systems and maintain or improve existing ones.

How to get an entry-level data analyst job

Your first job in data analytics will depend heavily on your background. If you’re entering the field with experience in programming, a job as a software engineer might make the most sense. Alternatively, previous experience in business operations and planning may make you better suited to a role as a business analyst.

Whatever your path, it’s important to research what employers are looking for and tailor your learning in a direction that will allow you to thrive professionally. Though a wide range of skills is necessary for data analytics, specializing empowers you to better compete in your field of choice.

Once you’ve tailored your skills to your general career path, you can start looking for specific jobs. To find opportunities, comb through job boards like Indeed or ZipRecruiter and reach out to your network. You may also want to spend some time updating your resume and portfolio to ensure that potential employers receive an up-to-date snapshot of your skills and applicable work experience.

Whatever you do, don’t get discouraged! Job searches take time and effort; with enough of both, you can find and land your dream job.

Data analysis certifications

Certifications offer a method for data analysis professionals to build their resumes and expand their skill sets. A good certification is a vote of confidence for your skills in a certain industry or even with a certain vendor. Some common certifications include:

• Associate Certified Analytics Professional (aCAP) — A certification for entry-level work
• Certification of Professional Achievement in Data Science — A skill-oriented certification offered through Columbia University
• Certified Analytics Professional (CAP) — A more thorough certification than aCAP that focuses on understanding the entire analytics process

While bootcamps don’t offer certifications on their own, they can provide the skills necessary to pursue them later on.

How Much Do Data Analysts Make? Job Outlook and Salaries

Given both the prevailing need for data analysts to interpret the data processed by analytics systems and the increased popularity of Big Data, the average data analyst salary and job outlook is good (PDF, 3.9 MB) .

How much does a data analyst make?

According to the Bureau of Labor Statistics, the median pay for a market research analyst was $63,790 per year in 2019, with the market expected to grow by 18% by 2029, much faster than average. The outlook is even better for an operations research analyst , with a median pay of $84,810 per year in 2019 and an expected 25% growth by 2029.

California, Texas, Virginia, New York, and Florida stand out as burgeoning states for careers in data analytics. In particular, New York claims the highest mean salary for operations research analysts , at $104,890 per year. In addition, recent trends toward remote work make data analytics a flexible career to break into regardless of your geographic location.

Should You Become a Data Analyst?

Starting as a data analyst means getting your foot in the door of one of the fastest-growing industries, with ample opportunities and multiple points of entry. If you have a head for numbers, a love of problem-solving, and a knack for critical thinking, data analytics could be an ideal career for you.

Columbia Engineering Data Analytics Boot Camp is an excellent alternative for those who are not seeking a data analytics or data science degree and are interested in an accelerated, real-world learning experience. Whether you’re just starting work or thinking about changing careers, our boot camp is a great starting point to build your skills and empower you to find your own path forward.

The Best Data Analytics Certification Programs

There has never been a better time to add data analytics to your skillset.

Data analysts are essential to most organizations in operation today, and experts are increasingly concerned about the talent gap within the field.

Still, if you’re thinking about investing in a data cert, you’ll want to know: What are the best data analytics certification programs to consider? As well as that, you might wonder is a data analytics course really worth it in 2024? We’re here to help.

Keen to try your hand at data analytics before committing to a full program? Work your way through this free introductory short course first. In the space of five tutorials, you’ll try some basic data analysis—a great insight into the kinds of things a data analyst does in their day-to-day.

In this post, we’ll consider the benefits of becoming a certified data analyst. We’ll then share our list of the best courses and programs available right now, based on extensive market research.

Here’s what we’ll cover. If you want to skip ahead to a particular option, just use the clickable menu:

• CareerFoundry Data Analytics Program
• Springboard Data Analytics Career Track 
• Wharton Business Analytics Online Certificate Program
• Cloudera Data Analyst Certification
• BrainStation Data Analytics Certificate
• Harvard University Business Analytics Course
• Thinkful Data Analytics Immersion Course
• General Assembly Data Analytics Course
• MIT Sloan School of Management Applied Business Analytics Certificate
• Cornell Data Analytics Certification Program
• Google Data Analytics Certificate
• Turing College Data Analytics Certificate
• DataCamp Data Analyst Certification

Benefits of a data analytics certification

• How to choose the right data analytics certification program for you
• What skills do you need to become a data analyst?
• Frequently asked questions

1. Which data analyst certificate is best?

So, without further ado, here’s our round-up of the best data analytics certifications.

All prices will be listed in U.S. Dollars (USD).

1. CareerFoundry Data Analytics Program

The CareerFoundry Data Analytics Program at a glance:

• Mode of study: Online
• Ideal for: Beginners and career-changers
• Duration: 4 months full-time (30-40 hours per week) or 8 months part-time (15 hours per week)
• Price: $7,505–7,900

Who is it for?

The CareerFoundry Data Analytics Program is ideal for beginners looking to learn how to become a data analyst from scratch. This is one of the most comprehensive certification programs on the market, offering a hands-on curriculum, a dual mentorship model, a job guarantee , career coaching, and an active student community.

What’s more, the program is delivered entirely online, making it an accessible option if you’re working or juggling other commitments. You can even try a free, introductory five-day short course .

How does it work?

This program will see you taking a very hands-on approach to learning all the techniques and tools of the trade. Upon enrollment, you’ll be assigned a one-to-one mentor and a tutor—two industry experts who will provide regular guidance and feedback.

The course content is accessed through the online learning platform, and you’ll work your way through practical exercises as well as portfolio projects. You’ll graduate with a professional data analytics portfolio and a certification. You can learn more about how the program works on this page .

How long does it take to complete?

The program takes four months to complete while studying 30-40 hours per week, or eight months to complete while studying about 15 hours per week. It’s flexibly paced; you can work largely to your own schedule, but you will need to meet certain deadlines in order to stay on track for a timely finish.

How much does it cost?

The CareerFoundry Data Analytics Program costs $7,900 (or $6,555 if you’re able to pay in full up-front)  but the cost of the tuition is dependent on your location and is competitively priced. A range of flexible payment options include paying upfront, or getting a small course discount. Contact CareerFoundry to find out your local pricing and if there are any partial scholarships available.

Useful links

• Curriculum details
• Graduate outcomes
• Speak to a Career Advisor
• Meet some of the CareerFoundry data analytics mentors

2. Springboard Data Analytics Career Track

The Springboard Data Analytics Career Track at a glance:

• Ideal for: Career-changers with some experience
• Duration: 6 months (15-20 hours per week)
• Price: $11,300–13,618 

The Springboard data analytics certification is designed for people who can demonstrate an aptitude towards critical thinking and problem solving, and who have two years of work experience.

This is a fully online, mentored course that offers both flexibility and a job guarantee—ideal if you’ve already got some industry know-how but want to switch to a career in the field. If you don’t meet the requirements for the Data Analytics Career Track, Springboard also offers an Intro to Business Analytics course ; however, this one doesn’t come with a job guarantee.

When you enroll, you’ll be matched with a mentor based on your availability and the kinds of skills you want to develop. You’ll learn completely online, applying your newly acquired theory to case studies and project-based assignments.

To complete the course, you’ll work on a final project which is reviewed by your mentor. You’ll graduate with a certificate of completion which you can display on your LinkedIn profile.

This is a six month program, with most students dedicating 15-20 hours per week.

The program costs $11,300 (and you’ll get a 25% discount if you’re able to pay the full tuition up-front). Other payment plans are available, with various effects on the total cost.

• How it works
• Meet the mentors

3. Wharton Business Analytics Online Certificate Program

The Wharton Business Analytics Program at a glance:

• Ideal for: Managers and leaders looking to upskill
• Duration: 3 months (6-8 hours per week)
• Price: $2,750 

This online program is ideal for managers and leaders who wish to learn how data analytics can help improve their decision-making processes.

If you’re looking to excel in your current role and lead your team to success (rather than make a career-change into data analytics), this is a flexible, low-intensity way to learn the fundamentals of data analytics for business.

The course comprises nine modules, which will take you through the different types of data analysis as well as the key methods and tools.

The course content is delivered via a combination of video and live online lectures. At the same time, you’ll work on individual assignments and receive feedback. Upon completion, you’ll come away with a digital Wharton certificate.

The course takes three months to complete, and you’ll need to dedicate between six and eight hours per week.

The course costs $2,750, making it one of the more affordable options on our list.

4. Cloudera Data Analyst Training Course and Certification

The Cloudera Data Analyst Training Course at a glance:

• Mode of study: Classroom-based, virtual classroom, on demand, or private instruction available
• Ideal for: Professionals with some knowledge of SQL
• Duration: Four days
• Price: $2,235–$3,195 (plus $295 for the CCA Data Analyst Exam)

If you’re already working in a technical or analytical capacity, this course will help you take your data skills to the next level. The course is designed for data analysts, business intelligence specialists, developers, system architects, and database administrators who want to learn how to work with big data and get their skills certified.

You’ll need some knowledge of SQL, as well as basic Linux command-line familiarity.

The course is based on instructor-led discussions and interactive, hands-on exercises. There are a range of study options available, allowing you to choose when and how you learn.

Both the in-person classroom and virtual classroom options take place on set days (with start dates available every two months), while the on-demand option enables you to work through the course content at your own pace for up to 180 days. Upon completion of the course, you are eligible to register for the CCA Data Analyst exam in order to receive your certification.

The course takes four full days to complete, but you can work at a more flexible pace with the on-demand option.

If you opt for the virtual classroom, it’ll cost you $3,195. The on-demand option is slightly cheaper at $2,235. The CCA Data Analyst exam costs an additional $295.

• The CCA Data Analyst exam

5. BrainStation Data Analytics Certificate

The BrainStation Data Analytics Course at a glance:

• Mode of study: Online or in-person (U.S., Canada, and UK)
• Ideal for: Beginners looking for an introductory course
• Duration: 10 weeks part-time
• Price: $3,950

Spanning just ten weeks on a part-time basis, the BrainStation course is one of the less time-intensive options on our list—ideal if you’re not quite ready to commit to a lengthy program.

This course will teach you all the fundamentals of data analytics, equipping you to apply what you’ve learned to your existing role or to embark on further training. It’s important to note that the BrainStation course is less focused on career change than some of the other options on the market, so you won’t benefit from extensive mentorship or career coaching.

The course is available both in person—with venues across the U.S., Canada, and the U.K.—and online via Synapse, their data-driven learning platform.

Whether onsite or via engaging live lectures, you’ll focus on three core topic areas: building and managing databases, identifying trends in data, and visualizing and presenting insights. To earn your data analytics certificate, you’ll complete a project on a dataset of your choosing.

The course takes ten weeks to complete on a part-time basis. The exact scheduling and duration depends on whether you attend in person or complete the course online.

The course costs $3,950, with a range of flexible payment plans and scholarship opportunities available.

• About BrainStation
• Reviews and testimonials
• Synapse learning platform

6. Harvard University Business Analytics Course

The Harvard University Business Analytics Course at a glance:

• Ideal for: Beginners and professionals looking to upskill
• Duration: 8 weeks (5-6 hours per week)
• Price: $1,750

This introductory course is ideal for anyone looking to learn the fundamentals of data analytics.

Whether you’re a college student or graduate preparing for the world of business, a mid-career professional looking to develop a more data-driven mindset, or if you’re considering a more comprehensive course in data analytics and simply want to strengthen your analytical skills beforehand.

Offered entirely online, at a flexible pace, and at a relatively reasonable price, this is a good option if you want to dip your toes in without investing too much time and money.

The course content is delivered online via the Harvard Business School platform. The course is divided into five modules, starting with describing and summarizing data and progressing to more complex concepts such as hypothesis testing and regression analysis .

You’ll work with various case studies and complete practical exercises for each module. To get your certificate of completion, you’ll need to complete all five modules and achieve an average quiz score of at least 50%.

The course takes eight weeks to complete, comprising around 40 study hours in total.

The course costs $1,750.

7. Thinkful Data Analytics Immersion Course

The Thinkful Data Analytics Immersion Course at a glance:

• Ideal for: Beginners and career-changers with a bigger budget
• Duration: 4 months (full-time)
• Price: $13,475

The Thinkful program is an immersive, full-time course that promises to take you from complete beginner to job-ready data analyst in just four months.

If you’re keen to start a career in data analytics—and have the time and money available to invest—this is undoubtedly one of the most in-depth programs on the market.

However, if you are focused on starting a career in the field, bear in mind that the Thinkful course does not offer a job guarantee.

With instructor-led learning in a virtual classroom environment, you’ll master all the key concepts of data analytics, starting with an introduction to data analytics in modern business and finishing with a deep dive into Python and statistics.

Most importantly, you’ll work on real-world projects which will help you build your portfolio—a crucial tool when it comes to finding a job in the field. Unlike many online programs, the Thinkful course follows a structured schedule, so you’ll need to stay on track each week.

The Thinkful course takes four months to complete on a full-time basis (around 50-60 study hours per week).

The course has increased in cost in 2023 to a heft $13,475. However, there are a range of different payment options available, including loans, income share agreements, and stipends.

• How the Thinkful learning model works
• Independent reviews

8. General Assembly Data Analytics Course

The General Assembly Data Analytics Course at a glance:

• Mode of study: Online or on campus (U.S., Europe, Asia, and Australia)
• Ideal for: Beginners looking to upskill
• Duration: 10 weeks part-time (4 hours per week) or a one-week intensive

If you’re not necessarily looking to work as a data analyst but do want to master some of the key skills and tools, the General Assembly course offers a solid introduction at just four hours per week.

This is an entry-level program suitable for career-starters and career-changers who want to build a practical skillset, for marketers and product managers who want to advance in their careers, as well as for data analysts who want to formalize their skillset.

For part-time students, classes take place twice per week in the evenings or all day on Saturdays, depending on your availability. Alternatively, you can opt for an intensive one-week course.

Either way, the program will see you getting hands-on with data and using your newfound skills to solve real-world problems. You’ll complete a capstone project based on all the techniques you’ve learned throughout the course, as well as two additional smaller projects that reinforce what you’ve covered in each unit.

You’ll finish with a portfolio-ready project and a certificate of completion.

The course takes 10 weeks to complete at a pace of four hours per week. Alternatively, you can opt for the one-week intensive approach. It’s important to note that most of your project work will be done independently outside of classroom hours.

The course costs $3,950, slightly increased in price from last year.

• About General Assembly
• Campus locations

9. MIT Sloan School of Management Applied Business Analytics Certificate

The MIT Sloan Applied Business Analytics course at a glance:

• Ideal for: Non-technical business professionals
• Duration: 6 weeks (4-6 hours per week)
• Price: $2,714

The MIT Sloan course has been designed to appeal to non-technical professionals who want to learn how to apply data analytics to business.

Offered entirely online and requiring just four to six hours of study per week, this is a highly flexible option if you’re working full-time and managing a busy schedule. This is also one of the more accessible courses on the market in terms of pricing.

The curriculum is based on a series of case studies which demonstrate how real companies use data analytics to their advantage.

You’ll also learn through interactive discussions, hands-on exercises, and optional code snippets for R and Python if you’re keen to take a more technical approach.

Upon completion, you’ll receive a verified digital certificate from MIT Sloan School of Management. The program also counts towards an MIT Sloan Executive Certificate .

The course takes six weeks to complete, requiring between four and six hours of study per week.

The course costs $2,714, up slightly from 2022’s price. Flexible payment options are available.

10. Cornell Data Analytics Certificate Program

The Cornell Data Analytics Certificate Program at a glance:

• Ideal for: Stats and Excel-savvy learners
• Duration: 9 weeks (3-5 hours per week)
• Price: $3,900

Longer than MIT’s course and with a price to match, this certification program is also aimed at those already working in business looking to add some data training to their portfolio—with a certification from Cornell University to show for it.

The online program is split into three separate courses of three weeks each: Understanding and Visualizing Data, Implementing Scientific Decision-Making, and Using Predictive Analysis.

Learning happens via video tutorials and hands-on exercises.

It takes nine weeks in total to complete, with only 3-5 hours of work per week, ideal to build around those with busy work schedules.

Total price for the program is $3,900, but there is also an option to pay $1,350 per month, should your schedule demand it.

11. Google Data Analytics Certificate with Coursera

The Google Data Analytics Certificate at a glance:

• Ideal for: Absolute beginners taking their first steps on a budget
• Duration: Approx 6 months (10 hours per week)
• Price: $234 or $49 per month

Just like their UX Design Certificate, this offering from Google seeks to educate as many people as possible on the basics of data analytics and what it’s like to work as a data analyst at Google. However, it’s important not to think that this is a substitute for a full program—it’s more of a whistle-stop tour of analytics.

As a result, this is perfect for those who want to learn about the basics of data analytics to get started out, before building on their learning with other courses and more mentored programs.

Further reading: Google Data Certificate vs CareerFoundry Data Analytics Program

The online certificate is split into eight separate courses on basic topics including data processing, learning the programming language R, and a final capstone project.

You’ll learn within the Coursera platform, with all lectures, readings, and assignments based on it. Assessments are graded via a peer-feedback system.

You can take this at your own pace, but typically completing the nine courses will take six months, if you study 10 hours per week. If you’d like to take longer that’s perfectly fine, provided you keep paying the Coursera access fee.

If you wrap up the whole data analyst certification in six months it should cost you $234, or $49 per month.

11. Turing College Data Analytics Program

The Turing College Data Analytics Program at a glance:

• Ideal for: Those looking for an online program with human support
• Duration:   6-10 months (15-30 hours per week)
• Price: $4,421

If you’re looking to gain a solid foundation in the basics of data analysis and then build upon it, as well as learn how to work in the same sprint cadence as a pro does, then Turing College (not to be confused with Turing School)’s offering will be for you.

On top of that, you’ll get some face-time with a data professional who’ll serve as your tutor, reviewing your projects.

Similar to CareerFoundry’s (but without the dual mentorship system and career support) in design, the fully online, fully flexible program goes through the foundation of data analytics.

Then, students will be able to add a specialization in areas such as marketing analytics or product analytics.

Once accepted, you’ll be studying at your own pace, meaning that depending on how much time you dedicate to it, you can finish in between six and ten months.

The cheapest of their programs, the Data Analytics Program costs $4,421. Top of their other offerings is their Online Data Science Masters Degree, which will set you back $12,964.

12. DataCamp Data Analytics Certification

The DataCamp Analytics Certification at a glance:

• Ideal for: Self-guided learners looking for certification
• Duration: Flexible
• Price: $25 per month

Long known for offering engaging bite-sized courses helping budding and professional analysts build and brush up on their data skills, DataCamp is an established name.

But did you know that you can get a full certification from them to put on your resume, attesting to your data skills proficiency?

To receive your data analyst certification from them, you’ll be assessed on six areas, which they will teach you on their platform:

• Data management
• Exploratory analysis
• Data communication
• Coding for production
• Analytics fundamentals
• Statistical experimentation

That’s up to you—the speed at which you go through the six topics and successfully pass the certification exam depends on your own pace of learning.

You can access the first chapter of the course for free under the Basic plan, but to get the certification you’ll need the Premium plan, which is $25 a month. For those whose companies are encouraging them by investing in their professional development, there is also a similarly priced Team plan.

2. Is it worth getting a data analytics certificate?

In a word, yes. Data analytics isn’t just a buzzword; it has huge implications for business.

Data analytics enables companies like Netflix and Spotify to personalize their offerings down to the very last detail. It allows marketers to understand customer behavior like never before. It’s transforming education, healthcare, travel, and politics—to name just a few.

Those who can work with and leverage data are seen as extremely valuable, and companies both big and small are turning to data analysts to help them secure a competitive edge.

But can’t you just teach yourself?

It’s true that you can master at least some data analytics tools and techniques yourself—in fact, you probably already have. If you use Excel or Google Analytics, for example, you’ll understand some of the basic concepts.

However, there’s a big difference between performing very simple analyses and being able to call yourself a qualified data analyst. With that in mind, there are many advantages of completing formal training in the field and obtaining a certification.

Here’s just a short roundup of the different advantages having a cert listed on your data analyst resume.

Credibility

When hiring a data analyst, employers are looking for a thorough understanding of the key concepts, together with the ability to apply your knowledge to real-world problems. While self-taught analysts may well know their way around a dataset, a certification demonstrates that you’ve been through extensive, hands-on training.

That’s not to say that self-taught analysts aren’t as qualified as those who have completed a program or course, but a certification from a reputable institution instantly sets you apart as a credible, professionally trained analyst. Employers can see instantly that you’ve mastered the necessary skills —immediately putting you in the running for any data analyst role.

If you’re brand new to the field, a certification program will take you right from A-Z, leaving you with the skills, confidence, and credentials needed to start your career as a data analyst. Even if you don’t necessarily want the data analyst job title, a certification will undoubtedly prove valuable in your current role.

As the list of top courses earlier has shown you, data analytics certifications are typically based on a comprehensive curriculum that covers all of the essential data analysis skills and tools. This ensures that you are getting a well-rounded education and that you are prepared for the demands of the job.

Your employer might be willing to pay for it

As more and more companies recognize the value of data analytics, those who can apply it to their jobs are an excellent asset for any team. As a result, companies are more willing to invest their professional development or employee training budgets in certification courses for their employees. The right certification program will allow you to expand your skillset and excel in your current position, or transition to another one in the same firm.

Mentorship and support

Many data analytics certification programs offer mentorship and support from experienced professionals. This can be invaluable for learning new skills, troubleshooting problems, and getting yourself situated in the data analytics world. For career-changers in particular, the guidance and support provided by a data mentor can be massively valuable.

Networking opportunities

Simply put, you won’t be studying alone. Data analytics certification course can also provide you with opportunities to network with other data analysts and professionals in the field.

As well as your fellow learners, your community will also involve those who’ve come before you and are already working in the industry. This can help you to learn about new job opportunities and to build relationships that can benefit your career in the long-run.

In short, a data analytics certification will equip you with some of the most in-demand skills in today’s business world—and provide you with an instantly recognizable qualification.

However: Not all data analytics certifications are created equal! Before you invest, it’s important to do your research and ensure you find a course that matches your budget, availability, skill level, and aspirations.

3. How to choose the right data analytics certification program for you

With so many data analytics certifications on the market, choosing the right one for you can be a daunting task. In this section, we’ll cover a few factors for you to consider.

How much can you spend on a data analytics course?

We’ll start with what’s likely the greatest deciding factor for many people: cost. Just from our previous list, you can see that tuition costs for recognized data analytics certifications range from the four-to-five figure range.

However, many institutions will offer flexible payment options, such as paying in installments, authorized loans, government-funded options, and even scholarships in some cases. 

How and where do you want to study?

Some data analytics certifications offer online-only study, some offer in-person, and some may offer a combination of the two.

For those who are looking to change careers into data analytics while still actively working in another field, part-time study is likely to be the most suitable option for you. Those with more time available for studying naturally have the most flexibility when it comes to this deciding factor.

With regards to in-person study, check to make sure what options for data analytics certifications are available in your area.

What type of data analytics certification do you need for your career?

When considering a career in data analytics, it helps to do some research into potential future employers.

Why? Because some companies may only allow certain certifications from particular institutions, which would narrow down your options for study greatly. Check out careers pages or do some research on data analytics discussion boards—there are many industry experts using those boards, providing an invaluable resource for those looking to get into the field.

While there are many other factors to consider—some more relevant or personal to you than others—we think the three we’ve mentioned are great starting points for your research. 

Once you’ve narrowed down your search to a few choices, it would be wise to spend some time speaking to an advisor from each institution to see how else the certification can benefit you—you may find out more about the course through them than you could have through solo research.

4. What skills do you need to become a data analyst?

A high-quality data analytics certification will ideally set you up to succeed in most entry-level jobs in the field.

So what does that mean, exactly? It means you’ll learn about how to deal with every aspect of the data analysis process : from data cleaning to creating visualizations , and everything in between. 

When working as a data analyst, you’ll need to have some knowledge or experience in the following hard skills/tools:

• Programming languages, such as Python
• Querying languages, such as SQL or SAS
• Data visualization tools, such as Tableau
• Microsoft Excel (or Google Sheets)
• Decision making
• Building data sets
• Data optimization
• Machine learning algorithms
• Predictive modeling
• Statistical analysis
• Data engineering
• Regression analysis

Some required skills of a data analyst will be things that you may have learned in other fields of work—such as communication skills or problem-solving skills. If you haven’t got much experience in these, a good data analytics certification will help you.

For a more in-depth look at the skills required for a career in data analytics, check out this guide: What Are The Key Skills Every Data Analyst Needs?

5. Frequently asked questions

To round up, we’ll answer some of the most frequently asked questions about data analytics certification programs. 

Is a data analytics certification worth it?

If you’re looking for a career in data analytics: yes! Gaining a certification in data analytics will equip you with all of the necessary skills and know-how required to land a job as a data analyst.

Throughout your chosen course, you’ll complete practical projects that apply your learned skills, giving you material for your data analytics portfolio. You may also gain invaluable industry contacts and advice that would be nigh on impossible if you were coming into the industry without a certification. 

How do I get certified in data analytics?

You could take one of the data analytics courses we’ve listed in this article, or continue your research and look for another industry-recognized data analytics certification.

Enquire, enroll, learn, study, complete projects, become certified! Depending on the course you land on, there may be some other steps in-between, but that’s the general process you can expect to follow. 

Which certification is best for data analytics?

Unfortunately, we can’t answer that for you, as it depends entirely on your own personal circumstances and your future career goals.

We’ve given some ideas on how to narrow down what is surely a long list of potential data analytics educational providers, but ultimately, you’ll need to decide which one is best for you based on your personal needs, circumstances, and goals.

How long does it take to become a certified data analyst?

Starting as a total beginner, becoming a certified data analyst can take as little as 4 months on a dedicated program, or as long as 4 years on a university program.

As an upskiller, learning additional data analytics skills can take anywhere from a few days to a few months, depending on the extent that you want to upskill! Take a look at the courses on our list—they run the gamut of courses for beginner through to intermediate data analysts.

Will a data analytics certification help me get a job in tech?

Many advertised job openings for data analysts will feature a data analytics certification requirement. Some companies will go a step further and list specific certifications that they will accept, with all others not being counted.

We recommend doing some research into the field of data analytics you’d be interested in entering—such as healthcare data analytics , or marketing analytics—and even saving some listed job descriptions, and make note of which certifications will suit your future needs best.

How much does a data analytics certification cost?

It depends on the type of course you choose to take. Going by our list, you’ll find yourself spending at least a few thousand dollars on a certification, but there are usually ways of delaying payment or receiving assistance on the cost of tuition.

However, this will depend on the institution providing the course, as well as other qualifying factors.

If you’re not in a position to enroll in a paid program, you’ll find a list of free data analytics courses for beginners here .

In this post, we’ve covered eleven of the best data analytics certification programs available on the market. Hopefully you found something that fits both your budget and your learning requirements!

Now it’s time to delve a little deeper with your research and run some thorough background checks on your shortlisted programs.

Before you commit to anything, read through independent reviews and graduate testimonials, and consider the course content in detail.

Then figure out which skills you’ll need to learn for any ideal prospective employers, and speak to an advisor from your chosen institution to ask any questions you might have.

If you’re still have more questions about a career in data analytics and how CareerFoundry’s Data Analytics Program sets you up for it, then have a chat with one of our program advisors and they’ll clear it up, or attend one of our free online data events .

Keen to learn more about starting a career as a data analyst? Check out the following:

• Am I a Good Fit for a Career as a Data Analyst?
• What’s It Actually Like To Be A Data Analyst?
• What is the Typical Data Analyst Career Path?

How to become a data analyst

Is becoming a data analyst right for me.

The first step to choosing a career is to make sure you are actually willing to commit to pursuing the career. You don’t want to waste your time doing something you don’t want to do. If you’re new here, you should read about:

Still unsure if becoming a data analyst is the right career path? Take the free CareerExplorer career test to find out if this career is right for you. Perhaps you are well-suited to become a data analyst or another similar career!

Described by our users as being “shockingly accurate”, you might discover careers you haven’t thought of before.

How to become a Data Analyst

Becoming a data analyst involves a combination of education, skills development, and practical experience. Here's a guide to help you pursue a career as a data analyst:

• Obtain a Bachelor's Degree: Start by earning a bachelor's degree in a relevant field such as mathematics , statistics , computer science , information technology , engineering , economics , or a related discipline. Many data analysts have degrees in quantitative fields that provide a strong foundation in mathematics and statistics.
• Develop Analytical and Technical Skills: Enhance your analytical and technical skills by gaining proficiency in programming languages like Python or R. Learn how to work with data analysis libraries and tools such as Pandas, NumPy, SQL, and data visualization tools like Tableau or Power BI. Familiarity with Excel and statistical software is also beneficial.
• Gain Practical Experience: Acquire practical experience through internships, co-op programs, or freelance projects. Practical exposure allows you to apply your skills in real-world scenarios, enhances your resume, and provides valuable networking opportunities.
• Pursue Advanced Education (Optional): Consider pursuing an advanced degree or specialized certification in data analysis, statistics, or related fields. Advanced degrees can provide in-depth knowledge and make you a competitive candidate for more specialized or senior roles.
• Build a Portfolio: Create a portfolio showcasing your data analysis projects, including details about the problems you solved, the methods you used, and the insights you gained. A well-documented portfolio demonstrates your skills and practical experience to potential employers.
• Networking and Professional Development: Attend industry events, seminars, and workshops related to data analysis. Join professional organizations like the American Statistical Association (ASA) or the Data Science Association to network with professionals in the field. Engaging with the community can provide valuable insights and job opportunities.
• Apply for Entry-Level Positions: Look for entry-level data analyst positions or internships in various industries such as finance, healthcare, e-commerce, or technology. Tailor your resume and cover letter to highlight your relevant skills, coursework, and projects.
• Prepare for Interviews: Prepare for interviews by reviewing common data analyst interview questions, practicing problem-solving exercises, and demonstrating your analytical skills. Be ready to discuss your past projects and how you approached data analysis challenges.
• Continuous Learning: Stay updated with the latest trends, tools, and techniques in data analysis. Continuous learning is crucial in this rapidly evolving field. Enroll in online courses, read industry blogs, and participate in webinars to enhance your knowledge.
• Apply and Adapt: Apply to a wide range of positions and be open to entry-level roles that allow you to gain experience. Once you secure a position, be adaptable and willing to learn on the job. Data analysis roles can vary, so be prepared to adapt your skills to meet the specific requirements of your employer.

Certifications Obtaining certifications can significantly enhance your credibility and marketability as a data analyst. Here are some well-recognized certifications for data analysts:

• Microsoft Certified: Data Analyst Associate: This certification from Microsoft demonstrates your expertise in analyzing data with Power BI and Excel. It covers data preparation, modeling, visualization, and analysis techniques. Achieving this certification showcases your proficiency in Microsoft's data analysis tools.
• Google Data Analytics Professional Certificate: Offered by Google on Coursera, this certificate program teaches data analysis skills using Google's tools. It covers data cleaning, analysis, visualization, and the basics of machine learning. Completing this program provides a strong foundation for data analytics careers.
• SAS Certified Data Scientist: This certification is designed for professionals who perform statistical analysis and predictive modeling using SAS and open-source tools. It covers topics like data cleaning, feature engineering, model assessment, and deployment. SAS certifications are widely recognized in the analytics industry.
• IBM Data Science Professional Certificate: This certification program on Coursera, offered by IBM, covers data science methodology, data analysis, machine learning, and more. It provides hands-on experience with tools like Jupyter notebooks, RStudio, and Watson Studio. Completing this program demonstrates proficiency in various data science techniques.
• Cloudera Certified Data Analyst (CCA Data Analyst): This certification validates your skills in preparing, transforming, and analyzing data in Cloudera's CDH environment. It covers topics like data exploration, cleaning, and transformation using Pig, Hive, and Impala. Cloudera certifications are particularly relevant for those working with big data technologies.
• DASCA Data Science Certifications: The Data Science Council of America (DASCA) offers certifications like Associate Big Data Engineer and Senior Data Scientist. These certifications cover various data analysis and data science topics, showcasing your expertise in the field.
• Tableau Desktop Specialist and Tableau Desktop Associate: Tableau offers certifications for different skill levels. The Desktop Specialist certification demonstrates foundational skills in Tableau Desktop, while the Desktop Associate certification validates your ability to work with more complex data and visualizations using Tableau.
• AWS Certified Data Analytics – Specialty: This certification from Amazon Web Services (AWS) validates your skills in designing, building, and maintaining analytics solutions. It covers topics like data collection, storage, and visualization on AWS. If you work with AWS services, this certification can be valuable.

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How to Become a Data Analyst: Education & Skill Requirements

Today’s companies have access to a massive amount of data. Artificial intelligence (AI) and Internet of Things (IoT) technologies are two contributing factors to data growth. However, this data is only useful if it can be transformed into actionable insights.  

According to international consulting firm Deloitte, this growing data generation is why the tech industry has increasingly sought employees skilled in analysis. In fact, according to the U.S. Bureau of Labor Statistics, data analyst jobs are projected to see a 20% increase from 2018 to 2028. 

Now, more than ever, companies need professionals who can organize and analyze data to make strategic business decisions. With high-paying job opportunities in a variety of thriving industries—including information technology, healthcare, finance, retail, insurance and professional services—there’s never been a better time to pursue a career in data analytics. 

Let’s explore the skills and education you need to succeed in this growing field.

What Skills Are Required to Be a Data Analyst? 

Data analysts need a unique combination of technical and interpersonal skills to excel. However, finding sought-after professionals with these skills has become difficult.

The tech industry is often at the forefront of the shifting demand for technical skills. This shift could signal a larger movement toward analysis as a core skill across all industries.

However, what does it mean to have analytical skills? Let’s break down the technical skills required to become a data analyst.

Essential Technical Skills for Data Analysts

• Data preparation skills give you the ability to manage large unstructured data sets and turn them into organized and usable data.
• Statistical analysis is core to the work of data analysts and used extensively in predictive analytics. Data analysts are expected to be proficient in foundational statistical methodologies as well as current SAS (a statistical software suite) technologies.
• Applied machine learning acumen is one of the most increasingly sought-after abilities for data analysts. Data analysts can use applied machine learning to automatically detect patterns and trends in the data, expediting the process of harnessing meaningful insights. 
• Programming languages , especially R & Python, are essential for creating algorithms that help data analysts manipulate and interpret data.
• Data visualization helps data analysts clearly display data and translate insights for a nontechnical audience. Tableau is one of the prominent data visualization tools used in industry today, making it a core competency for data analysts.

These technical skills are absolutely essential for becoming a data analyst. Interpersonal skills also play a critical role in the success of a data analyst within an organization. In order to be a skilled data analyst, you must also possess these interpersonal skills:

Get a FREE roadmap that includes insider information to help you maximize the many opportunities in the fast-growing field of Big Data.

Essential interpersonal skills for data analysts.

• Critical thinking is essential for data analysts. Diving into the details, while also maintaining a big picture perspective, will make you successful at translating data into actionable insights.
• Creative problem solving is core to the role of a data analyst. You must be able to look at a problem from multiple perspectives and be willing to try different approaches to solve it.
• Communication and presentation skills make data analysts valuable as team members and leaders within an organization. Data analysts must be able to collaborate across departments and help executives turn their findings into effective business strategies.
• Intellectual curiosity helps data analysts constantly explore new methods and technologies to extract meaning from data. A pure desire to learn will help data analysts maintain relevance in this ever-evolving field.

Do I Need a Degree to Become a Data Analyst?

Yes, a bachelor’s degree is the minimum requirement for nearly all data analyst positions.  

The field of data analytics has rapidly advanced in the past two decades, resulting in more degree programs dedicated to this specialty. However, data analytics is a relatively new degree in higher education and not all institutions will have dedicated data analytics degrees.

At the bachelor’s level, if an institution doesn’t offer a data analytics major, there are other programs that will prepare you for this career. For example, a bachelor’s degree in computer science, statistics, or information systems can give you the foundational technical skills you need as a data analyst.

As data collection, management and analysis becomes more complex and technology advances, many employers are in search of candidates with master’s degrees. For those looking to excel in this field, pursuing an advanced degree can be one of the best ways to prepare.

What Are The Best Degrees To Stand Out For Data Analyst Jobs?

If you want to compete for top-paying positions at competitive companies, a master’s degree is becoming the gold standard. 

According to a 2020 Burtch Works study, 50% or more of data scientists and predictive analytics professionals hold a master’s degree. Many of these professionals hold advanced degrees in statistics, business or engineering. With the rise of dedicated data analytics degrees at the master’s level, you have the opportunity to combine the most beneficial aspects of these areas of study into one specialized degree. 

A master’s degree in data analytics will prepare you with advanced skills, including:

• Creating visualizations and interactive dashboards from multiple data sets.
• Applying appropriate data analytics methods to solve real-world business challenges.
• Articulating assumptions, analyses and interpretations of data in a variety of modes.
• Evaluating appropriate ethical use of data.
• Applying best practices in securely managing data.

Top data analytics master’s degree programs will also help you gain industry-recognized credentials that can further set you apart. For example, graduates from Franklin University’s master’s program in data analytics can distinguish themselves as top-notch talent with a SAS Tier 2 Academic Specialization in Data Analytics. 

Find The Right Master’s Degree in Data Analytics

If you’re a working professional who is looking to advance or change your career, you need a university tailored to your unique needs. 

Franklin University’s flexible, online data analytics program is designed for working adults . The M.S. in Data Analytics at Franklin combines best-practice methodologies with hands-on learning and instruction from industry professionals to get you career-ready in as few as 19 months. 

Explore how the M.S. in Data Analytics degree can take your career to the next level and help you advance in the exciting field of data analytics.

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