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Building a career in data science and analytics

A career in data science is lucrative and rewarding. But the path to starting or advancing a data science or analytics career is not always linear. Unlike more traditional jobs, you don’t necessarily need a technical bachelor’s degree or a master’s degree to become a data science professional. You simply need the right skills and experience. 

In this guide, you’ll learn the ins and outs of data science and analytics career pathways and skills. Plus, take away tips on how to decide which data science career is right for you.

Why Build a Career in Data Science or Analytics? 3 Top Benefits of a Data Science Career

Over the past decade, the availability of data and demand for data science skills and data-driven decision making has skyrocketed. Pushed further into the spotlight by the drastic shift in business operations and consumer behavior caused by the COVID-19 pandemic, analytics and data science are now cemented as essential navigational tools across industries and functions.

“Data science is a 21st century job skill that everybody should have,” says Eric Van Dusen, curriculum coordinator for data science education at the University of California (UC), Berkeley. “Every field. I tell students, you all need to come out with this set of skills. You’re going to be a lot more powerful in whatever career you go into.” 

A field in the spotlight, data science offers high salaries and big opportunities. 

1. Earn a High Salary 

According to data from Robert Half, the median starting salary  for data scientists is $95,000, almost double the U.S. median salary average. At about $70,000, even the average salary for data analysts, a more entry-level role, is considerably higher  than the median salary in the U.S.


According to a study by Burtch Works , work experience is the largest factor in data science salaries. Mid-career data science professionals who have at least seven years of experience can expect to earn an average of $129,000. Highly experienced data scientists who hold managerial roles can earn upwards of over $250,000. However, education, company size, and sector are also important factors when determining data science salaries. 

2. Solve Complex Problems

If you enjoy solving complex, real-world problems, you’ll never be bored as a data science professional. The primary responsibility of your job is to find answers and insights by analyzing and processing vast amounts of raw data. A few examples of business problems that you’ll get to solve are:

  • Finding ways to increase sales

  • Discovering features that distinguish a target audience segment.

  • Finding potential opportunities in disparate data sets.

  • Identifying unrecognized problems in current business operations.

  • Building infrastructure that helps an organization ingest and centralize all the data.

“The famous John Tukey said, ‘the best thing about being a statistician is that you get to play in everyone’s backyard," said Philippe Rigollet, associate professor in the MIT mathematics department and Statistics and Data Science Center. “This is true of data science: whatever your field of interest is, I can assure you that there is data to make it better. Being able to extract information from data is actually a very powerful position to be in with data being collected in all aspects of society, ranging from marketing to health and even to sports and entertainment." 

3. Avoid Job Automation

Data science roles, particularly data analysts, are at very low risk for automation for a few reasons:

1. The demand for data science roles is growing at an average rate of 50% .

2. Very few platforms can produce sophisticated analyses.

3. Data scientists are the ones who are doing most of the automating.

Growing Your Data Science Career: From Analyst to Data Scientist 

There are two primary ways you can use data science skills to grow data-centric careers: become a data science professional—pursuing jobs like data analyst, database developer, or data scientist—or transition into an analytics-enabled role like a functional business analyst or a data-driven manager. Both career paths require foundational skills and knowledge in data analytics, programming, data management, data mining, and data visualization.


Despite the two tracks, the evolving nature of the relatively new field means career paths are flexible. Data science professionals like data analysts can lean into a data science or data system developer role depending on where they deepen their expertise. By expanding knowledge in artificial intelligence, statistics, data management, and big data analytics, a data analyst can transition into a data scientist role.By building on existing technical skills in Python, relational databases, and machine learning, a data analyst can become a data system developer. Much of these skills can be learned either from work experience or independently through data analytics boot camps or from online data science courses. In this guide, we focus primarily on the data science jobs track.

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Data Scientists vs. Data Analysts: What’s the Difference?

The skills and job responsibilities of entry-level data science roles and data analysts often overlap. Both roles require statistical knowledge and the ability to program. However, there is a clear difference in the focus.

What Does a Data Scientist Do?

Data scientists answer questions about the business from the context of data. They leverage data to create new product features and tend to do more modeling and open-ended research. They’ll spend a lot of time cleaning data to make sure that it is usable for their models and their machine learning algorithms. When you watch Netflix and see a personalized list of recommended shows, that’s machine learning algorithms and data science at work.

Additionally, a subset of data science work is predictive analytics. "Predictive data analysis involves more complexity, because, as the name suggests, it predicts what is likely to happen in the future based on data from the past, or based on doing a data crossover between multiple datasets and sources," said Rafael Lopes, a Partner Solutions Architect at Amazon Web Services and instructor for Getting Started with Data Analytics on AWS. "In a nutshell, it tries to predict the future based on actions from the past. The use of neural networks, regression, and decision trees are very common in diagnostic analysis."

Core Data Science Skills
  • Big data: All data is large or complex data sets that can’t be managed with traditional data processing software. That’s why data scientists must know Apache Hadoop or Apache Spark, which is an open-sourced distributed processing system.

  • Data modeling: data modeling is the process of formatting specific data into a database.

  • Data visualization: data visualization is the graphic representation of data used to show trends and insights.

  • Machine learning: machine learning is a series of techniques used to predict and forecast data.

  • Programming: knowing programming languages such as Python and R are critical if you want to automate data manipulation.

  • Statistics: although you don’t have to be a statistician, you must know some form of applied statistics to interpret data.

  • Teamwork: data scientists don’t work in silosーthey’re often part of larger data science teams comprised of data engineers, software developers, and others.

What Does a Data Analyst Do?

Data analysts are responsible for answering questions about data. Unlike data scientists, data analysts are not concerned with using data to find trends or figuring out the business’s future. Their job is to analyze historical data, create and run A/B tests in product, and even design systems. Data analysts need to be proficient at data storing, warehousing, and utilizing tools such as Tableau.

Core Data Analyst Skills
  • A/B testing: A/B testing is a statistical approach used to compare two versions of a variable in a controlled environment. A/B testing is employed to determine which variable version performs better.

  • Domain knowledge: you can think of domain knowledge as specialization. For example, if you have significant experience working specifically in the retail sector, you have domain knowledge in retail.

  • Excel: Microsoft Excel is often used to manage small data sets.

  • Data Visualization: like data scientists, data analysts must know how to use data visualization tools such as Tableau to tell stories to stakeholders with data.

  • Programming: data analysts should have competent programming skills in languages like R and Python.

  • SQL: SQL is a database language used for data management and building database structures. SQL is often used instead of Excel because it’s more apt at handling large datasets.

  • Reporting: as a data analyst, you need to report your data insights, which means you should also have excellent communication and presentation skills.

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Which Data Science Career is Right For You?

Deciding whether a career in data science is right for you is more than asking if you like working with data or not. It’s about asking yourself if you like working on complex, ambiguous problems and figuring out if you have the aptitude and patience to build your skillset. To determine if a data science career is right for you, ask yourself: 

  • Are you committed to learning technical subjects?

  • Are you willing to learn applied statistics and other types of advanced mathematics like linear algebra?

  • Do you enjoy storytelling with data? 

  • Are you a self-starter and willing to generate new projects to work on independently? 

  • Do you enjoy computer science and programming? 

If you said yes to at least three or more of the above questions, then you may have what it takes to succeed as a data science professional—but which type of role makes sense?

Are You a Data Analyst?

Data analysts are generalists, which means they get to work in different teams and roles. They enjoy working on clearly defined, structured problems. They use data to extract and produce reports that are valuable to a business. Successful data analysts generally enjoy some level of complexity, but not as much as data scientists. Here’s how you can tell if you are fit to become a data analyst:

  • You are a generalist.

  • You enjoy working cross functionally.

  • You enjoy solving concrete problems.

Are You a Data Scientist?

Data scientists love complexity. They enjoy answering questions that are broad and amorphous. They thrive on project-based assignments, and get excited about delivering insights. Data scientists are less likely to work on a wide variety of assignments in comparison to data analysts. Therefore, you might be a good fit for a career as a data scientist if: 

  • You enjoy complexity.

  • You enjoy ambiguity.

  • You like delving into a single question.

  • You’re okay with not finding an answer to a problem.

Are You a Data Engineer?

Data engineers are very technical. They essentially organize and give structure to raw data in order for the data scientists and data analysts to execute their work. A good data engineer enjoys building data pipelines and likes software development. They have an advanced understanding of programming languages such as Java, SQL, or SAS. Therefore, you’ll be an ideal candidate for data engineering if: 

  • You enjoy highly technical roles.

  • You like building and managing data infrastructures.

  • You enjoy software development.

Women in Data Science

According to a 2020 study by the Boston Consulting Group, only 15% of data scientists are women . That lack of diversity is a serious issue, the study says: "AI algorithms are susceptible to bias, so building them requires a team that includes a wide range of views and experiences." 

edX Head of Analytics L. Sam Bishop agrees, and urges women to join the field, despite some of the traditional tech perceptions and barriers that block or dissuade women and other underrepresented groups from pursuing data science and analytics careers. “The most wonderful thing about data is that data is power,” says Bishop. “Nobody has more power than the person with the data. Even if you feel like you have imposter syndrome, well-analyzed data is your confidence-booster.” 

Start Building Your Data Science and Analytics Career

Data is more important than ever in a world full of uncertainty. As businesses continue to transform, they’ll be looking for employees with data science and analytical skills to help them optimize resources and make data-driven decisions. Whether you want to explore data science for the first time, gain valuable analytics skills that can be applied to careers in many industries, or earn a degree, there’s a path at edX for you.

Data Science Career FAQs

1. What is a data scientist?

A data scientist is a professional who has a multidisciplinary skill set and works with large amounts of data to find insights and answers to business problems. Data scientists typically have a postgraduate degree in a technical subject such as computer science or statistics. 

2. Is data science a good career?

Data science is an excellent career choice. According to the U.S. Bureau of Labor Statistics , data science is one of the fastest growing and highest-paid fields in the country. 

3. What kinds of jobs can you get with data science?

You can get a data science job in virtually any field. From retail to finance and banking, almost every industry needs the help of data science professionals to collect and process insights from their datasets. 

4. Is it hard to get a data science job?

Getting a data science job can be hard because the data science field is very new. Because of that, the field is constantly changing, so you need to stay on top of new skills and knowledge.

5. How do I start a career in data science with no experience?

There are a few ways you can start a career in data science if you have no experience. One way is to incrementally build fundamental data science skills and knowledge such as applied statistics, data modeling, data management and warehousing, and deep learning. Explore edX courses and programs that can help you get started. 

Last updated: April 2021