Data Science and Machine Learning for Beginners: DataCamp versus the Academic Approach

Data Science, Machine Learning, and Analytics are considered to be among the hottest career paths. The demand for skilled data science practitioners in industry, academia, and government is rapidly growing. The ongoing “data rush” is therefore attracting so many professionals with diverse backgrounds such as physics, mathematics, statistics, economics, and engineering. The job outlook for data scientists is very positive. The IBM predicts the demand for data scientist to soar 28% by 2020:

With so many professionals interested in data science, what is the best way to learn the fundamentals of data science?

Data Science is such a broad field that includes several subdivisions like data preparation and exploration; data representation and transformation; data visualization and presentation; predictive analytics; machine learning, etc. For beginners, learning the fundamentals of data science can be a very daunting task especially if you don’t have proper guidance as to the necessary training required, or what courses to take, and in what order?

I started learning about data science about a year ago. It was quite challenging from the beginning, but let me share with you the approach that worked for me.

Tips for learning data science and machine learning

A) Do not be in a rush

If you have not read this article: “Teach Yourself Programming in Ten Years” by Peter Norvig (Director of Machine Learning at Google), I encourage you to do so. Here is a link to the article:

The point here is that you don’t need ten years to learn the basics of data science, but learning data science in a rush is certainty not helpful.

B) DataCamp versus the Academic Approach

DataCamp ( is certainly a good website where you can learn lots of different skills from basic programming concepts to advance skills such as data science and machine learning. However, I think DataCamp uses an approach that is in a rush, and therefore too superficial. DataCamp courses are crash courses, with little or no level of depth. Most of the assessment questions are quite easy and non-challenging. If you are interested in the academic approach of learning data science, I would suggest you take courses from edX and Coursera. You can audit classes for free on edX, while still having access to all course materials and assessment exercises. Coursera has a few free courses, but a majority requires purchase of verified certificates. DataCamp courses also require a subscription fee. edX and Coursera courses provide a significant level of depth, compared to DataCamp courses. They follow the academic approach, which means course content is identical to what one would have if they took the same course from a university.

If you are interested in in-depth knowledge of data science and machine learning concepts, I would recommend the following courses from edX and Coursera that will give you a very solid foundation in data science (the academic approach requires an enormous amount of time commitment and dedication, but it is worthwhile):

(i) Professional Certificate in Data Science (HarvardX, through edX):

Includes the following courses, all taught using R (you can audit courses for free or purchase a verified certificate):

  1. Data Science: R Basics;
  2. Data Science: Visualization;
  3. Data Science: Probability;
  4. Data Science: Inference and Modeling;
  5. Data Science: Productivity Tools;
  6. Data Science: Wrangling;
  7. Data Science: Linear Regression;
  8. Data Science: Machine Learning;
  9. Data Science: Capstone

(ii) Analytics: Essential Tools and Methods (Georgia TechX, through edX):

Includes the following courses, all taught using R, python, and SQL (you can audit for free or purchase a verified certificate):

  1. Introduction to Analytics Modeling;
  2. Introduction to Computing for Data Analysis;
  3. Data Analytics for Business.

(iii) Applied Data Science with Python Specialization (University of Michigan, through Coursera):

Includes the following courses, all taught using python (you can audit most courses for free, some require purchase of verified certificate):

  1. Introduction to Data Science in Python;
  2. Applied Plotting, Charting & Data Representation in Python;
  3. Applied Machine Learning in Python;
  4. Applied Text Mining in Python;
  5. Applied Social Network Analysis in Python.

C) Learning from a Textbook

This book provides a great introduction to data science and machine learning, with code included: “Python Machine Learning”, by Sebastian Raschka. This book is easy to follow for those who already have a basic understanding of programming.

D) Network with other Data Science Aspirants

From my personal experience, I have learned a lot from weekly group conversations on various topics in data science and machine learning by teaming up with other data science aspirants. Network with other data science aspirants, share your code on github, this will really help you to learn a lot of new concepts and tools within a short period of time. You also get exposed to new ways of doing things, as well as new technologies.

E) Apply Knowledge to Real Data Science Problems

After establishing a strong foundation in data science, you may seek an internship or participate in Kaggle competitions where you get to work on real data science projects.

In summary, being able to work with data is an extremely useful skill in this 21st century. Learning the basics of data science can be quite daunting. Through dedication and hard work, you can learn the fundamentals. To enjoy the art and beauty of data science, please don’t be in a rush. By investing a sufficient amount of time, you will accumulate a lot of tools in your data science toolbox. Keep in mind that the field of data science is an ever-evolving field, so learning will be lifelong, but the tips I have shared here will equip you with the necessary foundation that you need.

Thanks for reading.

Physicist, Data Science Educator, Writer. Interests: Data Science, Machine Learning, AI, Python & R, Personal Finance Analytics, Materials Sciences, Biophysics

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