If you’ve noticed the term data science being thrown around quite a bit on tech blogs and developer communities lately, you aren’t alone. Even beginning developers have probably started to hear about applications of the language they are studying in data science already. That’s because data science (a field focused on the efficient collection, organization, analysis, and application of data) is easily one of the most exciting areas of technology these days. It’s also why the demand for developers fluent in data science tools and principles has only continued to explode over the past few years.
While understanding what data science is involves some learning of its own, breaking into the data science field involves some practical steps for you to take as you pursue your coding career. That’s because a career in data science starts with the very fundamentals of programming: how databases work and interact with front-end websites and software, how particular programming languages and their libraries offer better solutions for data science needs, and how cybersecurity plays an essential role in protecting the immense and often sensitive data used by data scientists to power a wide array of modern industries.
For newer coders out there, pursuing a career in data science should be viewed as a marathon, not a sprint. There are a number of steps to take throughout your ongoing education in coding that can put you on the right path. With that in mind, let’s run down how to break into data science by highlighting the most important of these steps — so you can adjust your goals and programming education now, to make your path into data science that much easier.
Step 1: Understand The Various Specialties Of Data Science
As noted above, data science is all about how to develop the best tools and methods for collecting and analyzing large sets of data. But within the arena of data science, there are also different types of specialties that require specific knowledge and practical skills to enter into. Here are some of the sub-categories of data science professionals you should know as you figure out what you want to do in the future:
Data Science Developers
This could also be worded as “developer with a specialty in data science software”. Data science developers focus on the most useful languages for data science software, namely Python and R among others, to become skilled programmers at building both the front-end and back-end side of data science tools
Data Visualizers & Analysts
These individuals are responsible for using software to run queries and employ algorithms to identify trends in a data set and then transform those trends into graphics and recommendations for clients and customers. This specialty involves more people skills, as well as the ability to teach and explain complicated math and data concepts to lay persons.
Project Managers
Just as a project manager in software engineering is responsible for running and maintaining a team of specialists below them, data science project managers handle everything within a given project: working with a client to develop a question or problem that requires data to solve, ensuring developers and analysts build and employ the tools needed to realize the client’s vision, and following up with the client to analyze results and figure out future needs for research.
Learn The Most Useful Data Science Programming Languages
No matter which of the specialties in data science you want to pursue, your career will be significantly more productive if you know the fundamentals of the most popular data science programming languages. While the list of languages that have applications in data science is lengthy, there are definitely a few best choices:
Python
Python, thanks to its wide range of applications, is widely considered the best choice for a range of tasks for important domains in data science like machine learning, deep learning, artificial intelligence and the Internet of Things. The needs of these industries are made easier thanks to Python’s powerful data science libraries, most notably Keras, Scikit-Learn, matplotlib, and tensorflow. Python also offers support for essential processes of data science including data collection, analysis, modeling, and visualization.
R
R is a scalable and relatively easy to understand language that fosters an environment ideal for statistical computing and graphics, as well as being a powerful scripting language. R has also gained plaudits from developers for its ability to skillfully handle large data sets, which are a hallmark of fields like machine learning and AI. Its rapidly expanding developer community promises an even larger network of resources and tools over the coming years.
JavaScript
One of the most popular programming languages, period, it’s no surprise that JavaScript also has a variety of benefits for data science. Truthfully, you should think of JavaScript as more of an “add-on” for data science than a fundamental language, but the side benefit of learning JavaScript is all of the things you’ll be able to do outside of data science if you want to explore other areas of programming later in your career.
Step #3: Select An Educational Path
Alongside your coding classes, learning data science involves knowledge of complicated mathematical principles that go beyond what you might have learned in high school. While there are always do-it-yourself options for learning mathematical fields like algebra, calculus, and statistics, the truth is that pursuing a specialized education in data science at an institute of higher learning is probably still the safest choice for learning what you need to learn.
Fortunately, as the field has exploded in importance over the past few years, more and more major universities have begun to invest in and expand their data science courses and degrees. Prestigious universities like Harvard and Stanford now offer dedicated data science majors and the chance to learn and work alongside the leading experts in the field. Beyond the elite schools, even online colleges and trade schools have begun to expand their data science offerings with math courses integrated into the principles of the field. Whether you want to pursue a full-time degree in data science, or simply dabble with some classes at night or over your vacations, coupling your programming knowledge with the fundamentals of math needed to manipulate large sets of data is another important step to break into the field.
Step #4: Supplement Your Learning With Data Science Media And News Resources
One of the most exciting aspects of data science is the rapid and groundbreaking innovations happening in its different sub-sectors every week. From driverless cars to weather forecasting, pandemic prevention surveillance software to drones, the applications for data science are expanding even as you read this article. That’s why staying abreast of where the industry itself is going over the coming years will be as important to your education now as the concepts you are learning in your coding classes.
While there are hundreds of data science blogs, forums, and dedicated media outlets to choose from, here are some of the best ones to add to your reading to stay on top of trends in the industry:
- Data Science Central
- SmartData Collective
- No Free Hunch
- insideBIGDATA
- Simply Statistics
- Datafloq
- Data Science 101
The truth is that no one quite knows the limits and potential of current trends in data science, and so it’s impossible to truly predict everything you need to know to be in great shape for a career in the field a decade from now. However, by keeping in touch with the pulse in data science, you can pursue internships, first jobs, and educational opportunities that can help you continue to position yourself as a talented professional in the field later in your career. Now, go learn those data science basics in your coding class first!
