How to become a data scientist

What to expect from a career in data science

Data scientists are digital detectives who interpret the stories hidden within collected data. They blend their math and statistical know-how with programming skills to help companies make smarter business decisions.

Data scientists help identify trends, fine-tune algorithms, and analyze customer usage. They commonly work in industries like technology, finance, marketing, healthcare, biotechnology, retail, commerce, and even professional sports.

According to the Bureau of Labor Statistics (BLS), data scientists earn a median annual salary of $112,590 as of May 2024.

How to become a data scientist: A step-by-step guide

While there's no one-size-fits-all path to becoming a data scientist, these steps can make the move easier:

Step 1: Earn a relevant degree

Many data scientist positions require formal education. Nearly all data scientists have at least a bachelor's degree in computer science, statistics, mathematics, or a related field. As competition gets tighter for entry-level roles, however, many employers seek applicants with a master's or doctorate degree in more specialized areas like artificial intelligence, machine learning, data science, or analytics.

If you'd like to brush up on essential math for data science, or you're still exploring artificial intelligence and machine learning, online courses can help you learn the basics and build practical skills.

Step 2: Learn key programming languages

The most widely used programming languages for data scientists are Python and R. It may be best to begin with Python, as it's more beginner-friendly and has excellent tools for data work. Then, consider learning R for its statistical analysis abilities.

You'll also want to get familiar with Pandas for optimizing datasets and NumPy for crunching numbers.

Step 3: Build your data analysis toolkit

Data scientists have many tools of the trade to help them collect, analyze, interpret, and visualize data, including:

• SQL: Store and manage data
• Tableau or PowerBI: Visualize large datasets
• Jupyter Notebooks: Explore and interact with data
• Git: Track of changes in source code
• Cloud platforms (AWS, Google Cloud, or Azure): Build and deploy machine learning models
• Github: Showcase your skills through an online portfolio

Step 4: Understand machine learning fundamentals

Machine learning is an essential skill for data scientists that requires both advanced math and computer science skills. Consider starting simple with basic supervised learning, where you essentially teach a computer to make predictions. Then, let computers find patterns on their own through unsupervised learning.

Deep learning will require tools like scikit-learn and TensorFlow. For each learning stage, practice by using real-life data from Kaggle.

Step 5: Gain practical experience and build your portfolio

Knowing the theory is nice, but data scientists must prove they can actually do the work. Here are some ways to gain hands-on experience:

• Contribute to open-source projects to practice coding in real-world scenarios
• Enter Kaggle competitions to build your skills through friendly, motivating challenges
• Build projects that solve real problems, which is often what employers look for
• Share your work on platforms like Medium or LinkedIn to showcase your expertise and grow your professional presence
• Network with other data scientists to gain insights into their daily work and learn from their experiences

Start your data science journey with edX

Whether you're starting with a course or pursuing a degree, edX offers flexible data science programs to fit your schedule and reach your career goals.