How to become a data scientist

Are you thinking about jumping into the in-demand field of data science? If you're looking to upskill or change careers entirely, this guide will walk you through the most important steps to become a data scientist.

What does a data scientist do?

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 $108,020 as of May 2023.

Steps to become a data scientist

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, and data science or analytics.

If your math skills are a bit shaky or you're still figuring out artificial intelligence or machine learning, online courses can help break down these concepts and teach you how to apply them.

Step 2: Master essential 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: Develop your data analysis toolkit

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

• SQL: to store and manage data
• Tableau or PowerBI: to visualize large datasets
• Jupyter Notebooks: to explore and interact with data
• Git: to keep track of changes in source code
• Cloud platforms (AWS, Google Cloud, or Azure): to build and deploy machine learning models
• Github: to showcase your skills through an online portfolio

Step 4: Learn 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's a potential game plan:

• Jump into open-source projects (It's like a practice arena for coders.)
• Enter Kaggle competitions (Friendly competition can be a strong motivator.)
• Build projects that solve real problems (This is often what employers want to see.)
• Share your work on Medium or LinkedIn (This can let you show off your expertise.)
• Network with other data scientists (Get the inside scoop on their day-to-day.)

Get started on edX

Whether you're looking to dip your toes in through an introductory course or go all-in on a degree program, edX's learning opportunities in computer data sciences will fit your schedule and help you reach your career goals as a data scientist.