How to Become a Data Scientist

You might have heard terms like Neural Networks, Machine Learning, and AI, being tossed around more frequently, and it’s starting to sound like something right up your alley. You’ve done your research and already know what data science is, in fact, you might know that data science roles are in high demand and only growing. 

But what exactly is the first step in snagging one of these jobs? How do you become a Data Scientist? Here are some steps you can take to pursue a successful career in data.

Develop the Right Background 

First, ensure you have the right background to succeed. You should be comfortable with Python and SQL. These languages are essential to working with and organizing raw data. It doesn’t hurt to be familiar with Tableau as well, a tool you’ll use often to create visualizations. Keep an eye out for opportunities to help you start thinking like a Data Scientist. 

Many Data Scientists started out in engineering, IT, or data analysis. However, some come from completely different industries such as finance or baseball scouting! Of course, experience with data within your career will definitely take you further faster but is not necessary to begin your career in data science. 

Take Data Science Courses  

Data Scientists traditionally have a graduate-level or higher degree in education. It’s also a great alternative to earn a Data Science Diploma at an accredited training school, like BrainStation. We’ve written about how to prepare for a Data Science Diploma program, but here’s some of what you can expect from a high-quality curriculum:

Collecting and Storing Data

Data Scientists need to effectively use databases to collect and store data. Familiarity with excel and querying languages such as SQL is also crucial. 

Analyzing Data

Learn how to use Python and other relevant packages to analyze data sets with statistical methods and run tests. 

Visualizing and Presenting Data

When training as a Data Scientists, you should learn to use tools like Tableau and PowerBI to highlight findings with engaging visualizations. It’s also crucial you learn to integrate these tools into your workflow so that you’re able to prepare dashboards that non-analysts can decipher. 

Modeling Data

Get acquainted with modeling techniques that can be used across a wide range of data types from financial to e-commerce. By the end of your training, you should be able to use Python and R to build models that analyze behavior and predict unknowns. 

Develop a Data Science Portfolio

Alright, so you’ve done your research and gotten the training. Now is the time to develop a portfolio that will land the dream job – this may actually be the key component of your job hunt. In fact, BrainStation’s Data Science Diploma Program is designed to be a project-based experience, helping students build an impressive portfolio of completed, real-world projects.  

When applying for a Data Science role, consider displaying your work with GitHub instead of a website. GitHub easily shows your process, work, and results. But don’t stop there, your portfolio is your chance to show your communications skills and demonstrate that you can do more than just crunch the numbers. Accompany your data with a compelling narrative and demonstrate the problems you’re working to solve so the employer understands your merit.

We’ve also listed what you shouldn’t do when prepping your data portfolio. 

Find a Job in Data that Speaks to You

There are many roles within the Data Science field. Some people go on to become Data Engineers, Data Analysts, or Machine Learning Engineers. Find out what a company prioritizes, what they’re working on and make sure that it suits you and what you see yourself doing down the line. Cities like Boston, Chicago, and New York are experiencing a dearth in technical talent, so keep your eye out for the next opportunity!