Building a career in data science and analytics

A career in data science can be lucrative and rewarding. But the path to starting or advancing a data science or analytics career is not always linear. Unlike other 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? Three benefits of a data science career

With companies and organizations better able to capture data in a multitude of ways, data-driven decision making is changing the way businesses operate. Powerful analytics tools can model and predict how consumers will behave or markets will respond. Consequently, an understanding of data science is a 21st century job skill that can be beneficial in many different careers.

1. Earn a high salary

Entering into the data science field sets you up for a lucrative long term career. In 2022, the median annual wage for a data scientist was more than $100,000, according to the U.S. Bureau of Labor Statistics (BLS). However, you have even greater earning potential depending on the roles you choose to pursue and the industries you choose to work in. BLS reported that the top 10% of earners in the data science field had annual wages of nearly $175,000.

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.

Whether you are interested in marketing, health, sports, or politics, being able to extract information from data

3. Enjoy job security

Between 2022 and 2032, employment of data scientists is expected to increase by approximately 35%, according to BLS. Growth in this occupation is fueled by the increase in the volume of data that is being collected and the need for professionals to make sense of this data. Whether you are interested in marketing, health, sports, or politics, being able to extract information from data sets can help make you a competitive candidate in the labor force.

Growing your data science career: From analyst to data scientist

There are different ways you can use data science skills to grow data-centric careers. For example, you could become a data science professional, pursuing jobs like a data analyst, database developer, or data scientist. You could also 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.

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. Many of these skills can be learned either from work experience or independently through data analytics boot camps or from online data science courses.

Data scientists versus 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.

As the name suggests, predictive data analysis predicts what is likely to happen in the future based on data from the past, or based on analysis from multiple datasets and sources. The use of neural networks, regression, and decision trees are very common in diagnostic analysis.

Core data science skills

• Big data: Especially large or complex data sets can’t be managed with traditional data processing software. That’s why data scientists can benefit from knowing how to work with 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 are often part of larger data science teams composed 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 adept 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.

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 these 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.

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 BLS, employment of data scientists is expected to increase by approximately 35% between 2022 and 2032.

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?

Although the field is growing, data science jobs require significant technical knowledge. Rapidly evolving tools and technology also require professionals in this field to consistently build 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’s data science courses and programs that can help you get started.

Last updated: January 2024