I frequently ask young people, particularly undergraduates, what they plan to do with their future. I am often less than enthused with the responses which sound something like this:
- I hope to get a job doing statistics.
- I just want to work with computers.
- I want to be a data scientist.
- I just want a job.
The responses are typically vague and void of direction. Most responses involve waiting for someone else to provide the guidance. You do not have to wait. You can get started today.
If you are just interested in getting a job, the rest of this post is not for you. If you want to make an impact with your data science career, the remainder of this post is for you.
Below is an explanation of numerous specialties in data science. You don’t need to learn them all. Just pick one and follow the first step. You will learn more along the way. Don’t stress about which one to pick, there is no wrong answer. Just pick one and start building.
Data visualization is all about telling a story with data. Do you have a keen eye for color and design? Can you summarize complex data in a few simple charts? If you answer yes to those questions, then you just might be a good fit for data visualization.
First Step: Go to Data.gov and make an infographic
Data Science Educator
Are you the person always explaining your homework to others? This specialty might be for you. You can take a few different paths. One is the traditional university faculty approach. Another is more of a corporate training professional. The world needs both. Plus, if you are entrepreneurial, there are ample opportunities to consult as a data science educator. Businesses realize they need to know data science, and they are looking for training.
First Step: Start a video or blog with tutorials
A data engineer is typically more interested in systems than just the machine learning. Data engineers are typically strong with computer science fundamentals. They love to build things that themselves and others can use. A good data engineer can also spend a lot of time cleaning data as well.
First Step: Build a solution (hint: Cortana Intelligence Solutions)
Do you love to program? If so, you just might fall into this category. Data science has many needs for programmers. Everything from cleaning data to building data products needs programming.
First Step: Be on Github
Statistical Modeling (Machine Learning)
Some people just love the statistical modeling and machine learning. They love to tune models and squeeze the last bit of predictive power from a data set. If you love talking about regression, trees, random forests, AUC, cross-validation and boosting; then this specialty is most likely for you.
First Step: Enter Kaggle competitions.
Data Science Manager
If you are bossy, it does not mean you will make a good manager. The best managers know how to build strong teams and get out of the way. Managers will provide help and overall direction for projects. Plus, he/she should have a solid understanding of how data can help shape a team’s decisions.
First Step: Organize a group to help a non-profit analyze data (Similar to what DataKind does)
Data Science Researcher
A researcher is interested in pushing the boundaries of data science. Are you interested in creating your own machine learning algorithms? Do you want to build the next great data framework? Do you think data science can achieve something no one else has thought to try? If so, being a researcher is for you.
First Step: Go to graduate school
Data Science Unicorn
A data science unicorn is someone that knows all the specialties above and more. A unicorn understands all the topics of data science. Being a unicorn is not attainable for everyone, but a few people have become unicorns. If you think you can be a unicorn, go for it.
First Step: Start at visualization above
Simple: Pick a specialty and Go Make a Difference!