Data Science is one of the fastest growing fields, and the data shows this trend will continue into the near future. Data Science has become the backbone of many fields - it is data science that helps us make sense of the information we collect during marketing campaigns, and it is data science that helps us construct economic models that predict macroeconomic trends. It’s a field bustling with technological innovation, and people studying it will be at the forefront of multiple industries in the years and decades to come.
If you are someone who wants to join the ranks of data scientists, you have multiple ways of achieving your goals, including going to a University, taking online data science courses, and lastly self-learning. Which of these approaches is the best one? Is it still necessary to go to University to have the best prospects of landing a job? This article will answer these questions and help you decide how to approach this exciting new field.
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Why you might still need a university
The days that universities were predominantly for diving into academic studies are long gone. The recent advances in technology and the plethora of online resources has made it extremely easy for motivated individuals to learn on their own.
Instead, the university is a place for you to socialize and network with influential people from your field of study. While we like to think we live in a meritocracy where people succeed by skill alone, that has never been true. It is not only about what you know; it is about who you know.
Your university will give you numerous chances for presenting yourself and your skills to eminent professors and influential people who’d be able to help you start a successful career. It is much easier to jumpstart your career when you have direct access to employers instead of being one of the hundreds of online resumes they receive each day.
The difficulty of getting the fundamentals right without an academic setting
Not all academic fields are created equal when it comes to online teaching platforms. There are certain fields of study like computer science and language studies that rely mostly on a passive intake of information, and that makes them excellent subjects to learn online.
Other subjects like philosophy and mathematics require methodological approaches and engaging extensively with professors and classmates, and these present significant hurdles for a self-learner. They’ll have to try harder to learn the concepts and follow the material if they want to learn these subjects, and many online learners aren’t motivated to do so.
While data science is looked at as a subfield of computer science, it requires a good grounding in the fundamentals of Calculus and extensive knowledge of statistics and probability. Due to the field’s heavy reliance on maths, an online learner might have trouble handling the subjects.
New Approaches Making Universities Obsolete
While self-study textbooks and online video courses have been on the market for decades now, a wave of innovations in teaching methods is starting to threaten our traditional institutions, and the top two approaches, which might prove to be more effective than universities, are interactive learning platforms and gamified learning:
Interactive Learning Platforms
These were developed in the hopes of making the online learner more proactive. Studies have shown that passively listening to online courses without participation isn’t an effective method of learning.
If you use these platforms, you won’t just learn what a piece of computer code does, but you’ll be asked to use it to solve a problem. You won’t just be told about price equilibrium in Economics, but the platform will tell you to explain a system using the theory. This way you will be able to immediately apply the knowledge you’ve acquired, which makes learning the fields like economics and mathematics much easier.
Gamified Learning
One thing the last decade has shown us is how effective games are in capturing people’s attention and gluing them to their seats. That’s why some educators and psychologists have done extensive research to help bring over some aspects of gaming to education.
Correct uses of gaming principles in a learning system will make it easier for you to focus on learning more, retain more of the information, and feel less fatigue after long studying sessions. While this method is still in its infancy, it is already showing great promise.
Show don’t tell: how can you start a career as a Data Scientist
While choosing to opt out of enrolling in a university might prevent you from networking, and it is really hard for online resumes to help you stand out, there are new ways and platforms where you can show your skills!
Competition Sites
Competition sites like Kaggle provide excellent training ground for budding data scientists to show their skills. They provide competitions from diverse fields from economics to computer vision. The people who come up with the best algorithms not only get monetary rewards, but they have a great chance of getting job offers. Most employers will be impressed if you achieve good results in these competitions as it shows a practical understanding of the field beyond academics.
Github and Jupyter Notebooks
Github and Jupyter Notebook allow you to present data analyses in a readable and concise format. Instead of boring old CVs, employers are more receptive to a rich portfolio. Thanks to the tools being completely free and intuitive to use, you’re only limited by your skills when it comes to the projects you tackle. You can build an amazing portfolio from the comfort of your home.
Conclusion
This is a companion discussion topic for the original entry at https://blog.datasciencedojo.com/p/de18c191-27ee-45eb-99f9-542e33a58233/