Data science is a promising, dynamic field, and skilled data scientists are in demand. But how does one become a data scientist?
Because data science is such a new field, it’s not as simple as choosing which of many established PhD programs best suits your needs. Yes, there are degrees you can get that will help you break into the field, and skills you’ll need to develop if you want to succeed in data science. But the credentials data scientists need haven’t been crystallized yet, so you’ll need to focus on learning core concepts, building key aptitudes, and nurturing the traits that can take you from aspiring to thriving.
You can start to blog about data science topics even if you haven’t yet landed your first data science job – or even if you haven’t yet finished your education. Successful data scientists argue that starting a blog about data science, and updating it religiously, can do a lot for your budding career.
First of all, it gives you a chance to grapple with the important concepts that you’ll encounter on your way to becoming a full-fledged data scientist. Engaging with concepts through writing about them can help you think about them more deeply and understand them more fully, and that’s valuable even if no one reads your blog.
Second, blogging about data science gives you a chance to work on projects that interest you, and get the results of that research out into the world. This lets you practice using software and methodologies that will stand you in good stead once you land a job. Finally, putting this work out into the world can get you noticed, and could even lead to job opportunities or recognition that could send you further on the path to success.
One thing you’ll need as a data scientist is a solid understanding of math and statistics, so make sure to load up on these classes, starting in your undergraduate studies. Physics and engineering majors are useful undergraduate degrees to earn if you hope to break into this field. Undergraduate machine learning, applied mathematics, AI, or computer vision courses are also useful.
Then, go on to earn graduate degrees in data science fields – for example, a master’s degree in business analytics could give you the strong background in statistical analysis and data visualization you’ll need, and could be great preparation for a PhD in a data science field.
Data scientists don’t just spend their time writing code and crunching numbers. They do work with data sets, yes, but they also have to communicate their findings to other professionals, many of which may not have science-rich backgrounds. Learn how to put together effective data visualizations and how to communication well in speech and writing. Communication skills will make it so much easier to work well with others.
You may not think of data science as a creative field, but it’s just as creative as art or music. Why? Well, you can’t make new scientific discoveries if you’re incapable of thinking about the world in new ways. You have to be willing to sit with a question for a long time, make mistakes, correct those mistakes, and care deeply about the whole process. Taking a data set and pulling something useful out of it is a creative process. So is finding the best way to visualize and communicate those results.
As a data scientist, you’ll need to know your way around some key software programs, like Python and R. Other programs, like Tableau, Plotly, Chartio, or even good old Excel will also be useful in helping you deal with data sets and create visualizations. You’ll also need to know how to work with databases, so master at least one form of SQL. Learn to use command lines confidently and beef up your software engineering skills.
Are you interested in helping organizations make sense of the large data sets that are increasingly important in education, business, and modern life? Data science might be the field for you. Getting into data science takes a lot of hard work, but it’s worth it for the chance to carve your own niche in an emerging and important field.