Getting into Data Science: A Guide for Students and Parents
What do you want to be one you grow up? A data scientist, of course! Though ultra popular, the modern field of data science is relatively new. It’s still developing, which makes it incredibly hard for youngsters to get into it early. Kids can take coding courses to see if they want to work with computers, or writing courses to see if they are burgeoning journalists, but data science is more confusing. Data science requires no single skill set, and most degree programs are very new and sometimes even of questionable value. So how can excited students get into the field of data science?
In order to get into data science, it’s paramount to first understand its history. While “big data” is a modern day buzzword, data science isn’t entirely new or revolutionary. Students of data science aren’t taught strictly “data science” skills, rather they must become skilled in a variety of disciplines. Early data scientists didn’t have degrees in Big Data. They were computer scientists and statisticians who filled a new gap created by emerging technologies and possibilities. Data scientists must not only be knowledgable in code, they must be able to understand complex algorithms and love solving problems.
Learn Data Science Skills On Your Own
Going into data science means acquiring several useful skill sets that can be employed across several sectors, from pharmaceuticals and research to marketing and technology. But it also requires a lot of commitment. Luckily, there’s plenty of online courses to start the fire. Try testing out an Introduction to Data Science course on an online education platform like Coursera or Udemy. It’s hard not to get excited about data science after seeing all the possibilities! Follow that up by trying your hand at programming, either in R or Python. In school, be sure to take statistics, perhaps even an advanced class. If all these topics still interest you, you may be looking at a career in data science. You can also try courses in linear algebra or machine learning to further test the waters.
Prepare For a Degree Program
Will you need a degree to become a data scientist? Probably. In fact, 88% of data scientists have a master’s and 46% have a PhD. Most of these scientists, however, never took specialized “data science” courses. Many of them started in related fields and then turned their skills toward data science. The real question is what should you study to become a data scientist?
The answer is heavily dependent on the individual. More surprisingly, employers and working data scientists hold a lot of skepticism over specialized “data science” degrees. Not every degree program is worthwhile, and many programs are simply repackaged existing courses with no deeper understanding of data science. Some insiders recommend getting a BA in statistics to create a solid theoretical foundation for your career. Many more suggest supplementing traditional statistical and computer science studies with online courses in data science topics like SQL, NoSQL or Hadoop.
When choosing a university program, it’s key to choose based on the quality of the curriculum and professors rather than just the title. A degree in data science is useless if it doesn’t include the skills required for the job. When choosing a bachelor’s program, be sure it will enable you to pursue a master’s. Even if it seems far off or impossible, a degree in computer science, mathematics, statistics or engineering may be paramount to getting into the field more easily.
Perfect the Soft Skills
The term “soft skills” refers to abilities that are personal rather than learned skills like coding. In data science, soft skills are actually much more important than they appear. This has a lot to do with the career trajectory of data scientists. A degree or certain skill isn’t necessarily a “fast track” into real data science work. On top of powerful skill sets, data scientists must be adaptable and prepared to use their abilities in a variety of ways. It’s important to prove you have not only theoretical knowledge, but practical. Building a portfolio is just as important as going to class. More importantly, students can build portfolios completely on their own.
After grasping the basics of data science and analytics, students can play around with data tools and create real results. There are several open source tools available to mine data, to analyze it or create visualizations. Try asking a question and use data to find the answer. Data can be found all over the internet, often in nice downloadable collections. Try mining Twitter for information on what’s popular or who’s saying what. Learn from Wikidata and put your findings into visualizations. Open source programs and open data are all free to use. Technology will always be changing, but it’s good to become acquainted with popular programs and how they work. Degrees may teach skills, but doing data science is the only way to get good at data science.
Get Comfortable With Data and Have Fun!
While data science is full of theoretical skills that can be tough and time-consuming to learn, don’t forget about the fun aspects of data. The internet is full of great datasets and visualizations, so get inspired by what’s out there! Check out TedTalks on data usages to see how people are using data in real life. Read up on the history of data science to understand where it comes from and what it means. Try to understand all the different ways data science is used.
If you’re getting stuck on the lingo, try the Big Data Dictionary. Or read up on the three most important algorithms and find out what they really do. Tune into a data podcast on your drive to school. There’s no one path to becoming a data scientist, so find out what part of data excites you, follow it, and make your way into data science.
image credit: Francisco Osorio
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