So you’ve decided to enter the burgeoning field of data science, or at least incorporate it into your current position. For a full-time position in data science, chances are you’ll need an advanced degree.
Most graduate programs in data science will require a bachelor’s degree in engineering, computer science, physics, statistics, math, economics or a related field. With an undergraduate degree in one of these disciplines, you’ll already have a range of the skills needed in data science.
But it won’t hurt to bone up on your statistics and programming knowledge—two of the three pillars of the data science master’s program at Colorado School of Mines.
Data science students at Mines take three modules, each consisting of three 3-credit courses: data modeling and statistical learning; machine learning, data processing and algorithms and parallel computation; and domain-specific coursework.
It won’t hurt to crack open those statistics and calculus textbooks if you haven’t reviewed them in a while.
On the computer science side, consider becoming familiar with the languages and systems commonly used in data science today: R and Python, SQL databases, Hadoop and Apache Spark, to name a few. Fortunately, there are many free resources online, from YouTube videos to complete courses.
For both, you can also learn by doing. Sign up to work on a research project that requires statistical analysis or programming in Python. Volunteer to help clean and organize data for experiments.
The third module in Mines’ program is tailored to your interests and is about directly applying what you’ve learned in the first two modules. For example, you might take courses on signal processing for electrical engineering, or satellite remote sensing for geophysics. This is where industry experience comes in: knowing the specific problems you could solve in your domain using data science.
