Are you interested in the Data Scientist profession, but don't know how to choose your Data Scientist training course? In this article, we've listed the most frequently asked questions and suggested a few criteria to guide you in your choice.
How do I become a Data Scientist?
Are online videos enough to become a Data Scientist?
If you've never done data before, taking MOOCs and online courses isn't enough. But don't worry, these courses still provide useful skills that will be valuable additions to your LinkedIn profile and CV.
The problem comes with training courses, which for the most part don't confront you with real problems that companies have to face.
To make your profile more attractive, you'll need to find a training course that offers you the opportunity to confront these types of problems, to show that you can handle complex situations or situations with data that is messy, unreliable or difficult to access. Your training program must offer immersive and collaborative experiences to develop your technical skills, but also your social skills, which will be at the service of collaborative work. As the Data Scientist is in constant interaction with the various players, both internal and external, in the value chain, the development of soft skills is essential.
How to choose the right course?
To ensure the best possible learning experience, it's important to choose a course with a large proportion of hands-on practice. Indeed, machine learning requires a lot of programming, and this can only be learned by practicing again and again until it is mastered.
What's more, it's essential to choose a course that includes projects. Once again, the point is to practice in order to learn better: what is commonly called Learning by doing. The advantage of projects is that you can code the models you've learned yourself, come across errors when testing the models and correct them.
Another tip is to choose, if possible, a course where the trainers can evaluate your work. The advantage is that the trainers can see areas for improvement in your work and explain how you can progress.
In addition to technical skills, training must enable the development of soft skills. Critical thinking, communication skills, proactivity and intellectual curiosity are all essential to solving the problems associated with the Data Scientist profession. Make sure that your training program offers a project-based approach to developing your soft skills.
Lists of online MOOCs on Edx, Coursera and Udacity
While online courses are no substitute for the practice and feedback of direct training, they can be useful for practice! Here is a non-exhaustive list of MOOCs surveyed by KDnuggets.
How to get the best experience?
There are no quick fixes. What may be a good experience for one person may be a bad experience for another. So we're going to suggest a few avenues you can explore at your leisure.
A first avenue to explore is the internship. Investing in a good internship with a good company can have a huge positive impact on your career. However, when we say "a good company", we're not talking about its popularity, but rather its ability to focus on supporting its interns and sharing its experience to help them develop their skills effectively.
A second avenue to explore is on-the-job training. This opens many doors for your career and allows you to learn quickly. Also, the "Choose your professional future" law of September 2018 incorporates a new training modality, the AFEST, which enables companies to value learning experiences in a formal way and broaden the skills of its employees.
These first two tracks will enable you to learn from your peers and their experiences, which we believe is the best way to learn.
Another approach is todevelop personal projects. Companies are looking for candidates who are able to think like a data scientist and are therefore able to program. They want to see the work you've done, the problems you've solved, the data you've used and the code you've used to solve those problems.
If you've already got an idea of the industry in which you want to become a data scientist, we recommend you visit Kaggle to find industry-related databases to work on. Alternatively, more personal projects, such as an app that predicts the results of matches in your favorite sport is also a good thing. In both cases, it's all about having projects to talk about during interviews.
Before becoming a Data Scientist
Before you take the plunge and become a data scientist, a few things need to be said. Learning Data Science and programming involves a relatively high learning curve, and slow learners are likely to have a hard time before they get the hang of it.
What's more, a data scientist MUST always be keen to learn more, and NOT BE AGAINST change, as he or she works in an environment that is constantly evolving, and is quickly hit by skills obsolescence.
Let's face it, it's not easy, or even frustrating at times. So before you invest months of your life, BE SURE you really want to become a data scientist.
But rest assured, whether you become a data scientist or not, we strongly believe that the knowledge you acquire in data science will benefit you in the future, and give you a better understanding of the technologies we live with.
At DATAROCKSTARS we train you to become a Data Scientist with our Data Full Stack Bootcamp!
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This article was inspired by : How to make a successful switch to data science career by Julia Di Russo!