Self-Learning Data Science Resources with Jonathan Cornelissen
So you’ve decided you want to learn more about data science. Perhaps you’re seeking a career transition into the field or maybe you just want to be able to better understand the data professionals with whom you work. Whatever the reason, your path can be greatly helped by a guide to resources and topics you can study to learn aspects of this interesting and fast-growing field. We’ve compiled the following overview along with a look at the work of Jonathan Cornelissen, the founder of DataCamp, to do just that. Read on to help find your own path forward in data science.
Let’s first take a quick look at the work engaged in by Jonathan Cornelissen and the company he founded to help spread knowledge about his field. He first conceived of his company during his time pursuing his Ph.D. in Econometrics. Through his own studies, he encountered the difficulty of learning R without access to supporting resources. When he was teaching courses where his students were also required to learn the programming language, he witnessed the same difficulty in their efforts as well. He realized that the process could benefit from the advent of an educational platform designed to help in this area.
That idea would eventually lead to the creation of DataCamp, the educational service that has helped over 4 million learners improve their data skills to date. The platform showcases the capacity of such resources to help individuals grow their ability to work with data in a professional environment. Beyond simply teaching a programming language, the focus of the platform also allows students to improve data fluency and better understand the output of more experienced data scientists. In a world that is fast becoming more and more dependent on data, improving these skills can be a worthwhile endeavor for any modern professional.
Improving math skills
For those who are looking to actually transition into the world of data science, a firm foundation in mathematics is a must. Such a foundation allows data scientists to better understand their data and make useful extrapolations from it to help predict the future. Key areas of math that are important in this regard include linear algebra, probability, calculus, and statistics. It’s important to note that a foundation in this area doesn’t mean you need to be an expert, far from it. It does, however, require a degree of familiarization that will allow you to know what is required of you in a given circumstance.
In this regard, the advice that is generally given is to undertake an honest assessment of your math skills and formulate a plan for further knowledge based on that. Even if you’ve taken some related courses before, depending on how long it’s been since you were exposed to the material, you may want to revisit the topics as a refresher. This type of reintroduction can be accomplished through online courses, videos, or self-learning textbooks. Whatever route you choose, be sure to continuously relate the knowledge you’re gaining back to your end goal of becoming more proficient in data science. In other words, make sure you’re staying cognizant of how everything you’re learning will benefit you in the long run.
Explore a programming language
Beyond math, your journey into data science can be greatly helped along by a firm understanding of a relevant programming language. Most data scientists utilize either R or Python as their primary language, and will often use SQL as a supplemental language. Similarly to what Jonathan Cornelissen found in his own studies, you may be helped along on your journey by seeking out online or printed resources that can teach you one of these languages in practice.
However, understanding a programming language for data science goes beyond what is often used for, say, a software developer. Data scientists often make use of specialized toolsets within a programming language to help them accomplish their work and engage in analysis. For instance, popular libraries and visualization tools for Python include Pandas, Numpy, and Matplotlib. If you don’t already have a strong command of these or similar tools, then it can be a good idea to take steps to learn their usage. In this way, you’ll not only be proficient in the programming language of your choice, but also a number of additional tools relevant to data science.
Once you’ve improved your abilities in math and programming, you’ll want to dive further into data-specific topics. One relevant field that’s garnering plenty of attention these days is machine learning. This subset of artificial intelligence uses data and neural networks to generate algorithms that can expand on their own and essentially “think” in new and different ways. The field rests on an algorithm’s ability to build upon itself as it’s fed increasing amounts of data. Beyond the connection to data science, the applications of this field are vast, including self-driving cars and more efficient online search methodologies.
For a broader suggestion of a path forward, it can be helpful to follow in the footsteps of Jonathan Cornelissen and actually set out to build a project of your own. In much the same way the entrepreneur saw a need for data learning resources, you can also identify projects of interest and engage in them in order to further your knowledge base. Such projects have the added benefit of demonstrating your newfound abilities to potential employers and might even help you get a job somewhere down the line. Regardless, they can be a great way to explore the field in a way that is interesting and engaging.
If you’re at the beginning of your data science journey, the field might appear overwhelming. While there is certainly plenty of work to be done by the self-learner in order to attain the same level of knowledge as someone with a formal education, the task is accomplishable with determination and hard work. Looking to resources such as the one created by Jonathan Cornelissen can help put you on the path to eventual success in this regard. The more you commit to growing your abilities in this area, the faster you may find a number of opportunities in the field of data science opening up to you.