One of the most frequently asked questions for me – how to become a data scientist or what are the best courses/platforms to learn data science & machine learning? Well, for the first time in 2017, Kaggle, one of the leading platforms for data science, machine learning, predictive modeling, and analytics, conducted an industry-wide survey to establish a comprehensive view of the state of data science and machine learning. The survey included the responses from more than 16,716 data professionals from 171 countries. In this post, we will look into the top platforms & resources to learn data science and machine learning as per the insights from the Kaggle survey.
Top Platforms & Resources to Learn Data Science and Machine Learning – Survey Summary of 16,716 Data Professionals by Kaggle
As per the IDC forecasts, 2018 will see a six-time growth in the big data & analytics job market. According to IBM, the number of jobs for all US data professionals will increase by 364,000 openings to 2,720,000 by 2020. So, plenty of opportunities are out there for the aspiring data scientists and machine learning professionals. So, how to break into the data science field?
There are many ways to acquire data science skills, including online courses, blogs, textbooks, trade books, YouTube videos and more. But, which approach should an aspiring data professional use to learn data science skills?
The Kaggle survey provided great insights on how to learn cutting-edge data science skills and/or how to become a data scientist. Additionally, the survey also demonstrated the top trends in data science and machine learning across industries.
Top Platforms & Resources to Learn Data Science & Machine Learning in Terms of Usefulness
As you can see from the above figure, the top platforms and resources to learn data science are:
- Kaggle (40% used this resource)
- Online courses (36%)
- Stack Overflow Q&A (34%)
- YouTube Videos (32%)
- Personal Projects (29%)
- Blogs (29%)
- Textbook (25%)
- College/University (20%)
- Arxiv (15%)
- Official documentation (14%)
As per the survey, any data professionals have three platforms/resources (median) to learn data science skills. Professionals with job titles of Data Scientist, Machine Learning Engineer, Predictive Modeller, Researcher or Scientist/Researcher were found to be using four or more platforms. Whereas, the folks with job titles such as Computer Scientist, Data Miner or Programmer used only two platforms.
How Useful are the Top Platforms and Resources for Learning Data Science & Machine Learning?
If you refer to the figure 2 (above), you can see that all platforms got pretty good reviews. Most of the platforms received the rating of either very useful or somewhat useful. Below is the snapshot.
- Personal Projects (74% very useful)
- Online courses (70%)
- Stack Overflow Q&A (63%)
- Kaggle (62%)
- Tutoring/Mentoring (58%)
- Textbook (55%)
- College/University (55%)
- Arxiv (55%)
- Official documentation (52%)
- Non-Kaggle online communities (49%)
My Advice for the Students & Early Stage Professionals a Career Adviser & Admission Consultant
Data Science and Machine Learning are extremely complex, evolving and vast fields. You cannot master everything in one go. You need to start with the basics. More importantly, you must get your hands dirty by implanting your learning on real-world projects.
There is no single/best platform, resource or course. You have to refer to multiple platforms and resources. It’s just like school life – one standard textbook is not enough; you have to read other reference books.
Data science, in particular, is a very broad field and covers a variety of domains from business to bioinformatics. There is no fixed path to becoming a data scientist. You will come across a lot of advertisements for online courses and graduate (MS) programs. But, believe me, one course/program will never be enough to learn data science.
Read: Demystifying Neural Networks, Deep Learning, Machine Learning, and Artificial Intelligence
Graduate Programs vs MOOC Courses
The main job of a data scientist is coming up with a new meaningful way to interpret the data. So, it’s up to you how to do your job. There is no clear winner between online course (MOOC) and a full-time program (e.g. MS in Data Science). It really depends on your background, existing skillset and career stage.
Even if you get admitted to an MS in Data Science program at a top university, you will need to take a few online courses as well. Similarly, online courses are good to get started. But, getting a few online certifications will not be enough to become a data scientist.
You need to focus on the skills and techniques. An XYZ Certified Data Scientist or MS in Data Science Graduate from the ABC University will not make you stand out in the job market. It’s all about skills and understanding. You can have skills without degrees, and degrees without skills. No matter what, if you are lacking in the understanding and skills, no one can help you.
Additionally, you need to have solid domain knowledge. Domain knowledge can be gained only through real-world experience. So, the key takeaway – gather quality work experience before breaking into the data science field.
The demand for data scientists and machine learning is definitely growing. There is a clear shortage of data science talent everywhere. But, getting a job in data science is different from having the skills to do it well.
A 6–12 weeks classroom-based program or online courses by leading MOOC platforms (EdX, Coursera, Udemy, Lynda, SimpliLearn, Udacity, UpGrad, SkillWise etc.) makes it easy to get “data science” keyword on your CV. But, acquiring the skills so that you can implement on projects is not that straightforward.
Finally, there is no end to learning data science. If you think getting 3 degrees will somehow ‘complete’ your ‘required’ education, you are completely wrong. Technology changes way too fast; a particular technique or methodology that is uber hot now, might not remain 12 months down the line.
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My Career Journey
I am from Pharmacy & Pharmacology background. Of course, I had to start with a full-time Masters degree. But, during my biomedical research days, I went through few courses and workshops on molecular biology, biostatistics, and predictive analytics. The purpose was not to add certifications to my resume. My objective was to get better in my research job – to become a better biomedical scientist.
At present, I am very much involved in digital marketing. Again, it’s a very vast field, and the technologies change way too rapidly. I started by reading blogs and watching YouTube tutorials. Later, I signed up for a few courses on Udemy, HubSpot & Coursera. I applied some of the learning and techniques on the Stoodnt platform.
Recently, I also signed up for the CDMM course by Digital Vidya. So, I am basically trying to upgrade my knowledge and skills. I could have signed up for the Digital Vidya program straightaway as well. However, I felt my existing skills were not up to the mark. So, I decided to cover some basics before enrolling for an advanced and intensive course.
Again, all these online courses and certifications are not just to make my CV look nicer; though it’s definitely a bonus. But, I felt my job and career demand it. So, I am doing it. But, I need to keep learning, which is also one of the key suggestion by all instructors. I am slightly going behind in terms of coursework. But, I am picking up things, and implementing them in my projects as well.
As the saying goes, learning never stops. Happy Learning 🙂
You might also like my other blog posts:
How to Get Data Science, Machine Learning & AI Jobs
Top 12 Data Science Experts and Best Data Science Career Articles on LinkedIn
Information and Communications Technology (ICT) Jobs in India
Blockchain Technology Beyond Cryptocurrencies and Financial Services
MBA vs MS Business Analytics vs MS Data Science
Image Sources: Syracuse University, University of Virginia, SimpliLearn, and Alteryx.