Ruby on Rails is easy to modify, migrate (helps you alter your database in a structured and organized manner) and suppor

DataQuest’s content is generally more difficult than those in DataCamp. There were also fewer ‘fill-in-the-blank’ format exercises. Though it took longer, my knowledge retention on DataQuest was better.

With Ruby on Rails it might be problematic to create APIs. It has a varying quality and standard of documentation, a low runtime speed, and can be inflexible. After a while, code lines can get difficult to read and may require greater complexity, as well as more time to rework because of the many ways to code the same issue. With Ruby on Rails, it may be hard to find good documentation, especially for gems that are not so common. Usually, there are test suites that serve as the main source of information for developers. Since reading the official documentation is not an option, developers need to study the code.

Dataquest is very similar to DataCamp. It focuses on using code-along exercises to illuminate programming concepts. Like Datacamp, it offers a wide variety of courses in R, Python and SQL, though it is somewhat less extensive than those in DataCamp. For instance, However, unlike Datacamp, Dataquest does not offer video lectures.

Launched back in 2005, Django is a free open-source web application framework. Django is a high-level Python web framework that encourages prompt development and clean, practical design. It is maintained by the Django Software Foundation (DSF), an American independent non-profit organization. Django was invented to meet hasty newsroom deadlines while satisfying the high expectations and requirements of experienced web developers. The latest official version is 3.1.3, which was released on 2 November 2020.

This book is platform-agnotistic. In other words, it does not focus on any particular software but teaches the general principles of data visualization with enlightening examples. Some of the key pointers you can expect to learn from this book are:

Data visualization is the key to present the insights you drew from your data. After learning the technical skills of creating charts using python and R, I learnt the principles of data visualization from a book, Storytelling with Data by Cole Knaflic.

[1] Dhar, V. (2013). “Data science and prediction”. Communications of the ACM. 56 (12): 64–73. doi:10.1145/2500499. S2CID 6107147. Archived from the original on 9 November 2014. Retrieved 2 September 2015.

After digesting the book, I was able to create a (somewhat) visually pleasing chart that address the police brutality against blacks. One of the main learning points from the book applied here was to draw attention where you want it. This was done by highlighting the African American line with a bright yellow — reminiscent of the BLM color — while ensuring that the rest of the chart remained in the background with duller shades like white and grey.

In this post, I covered the steps I’ve taken in learning programming from scratch. With these courses, you now have the necessary skills to manipulate data! However, there is still a pretty long way to go. In the next posts, I will cover

[1] Dhar, V. (2013). “Data science and prediction”. Communications of the ACM. 56 (12): 64–73. doi:10.1145/2500499. S2CID 6107147. Archived from the original on 9 November 2014. Retrieved 2 September 2015.

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t taking your partner for granted. But what I mean is that you somehow, effortlessly, manage to coexist without ever getting into each other’s way. The manners in which you deal with your daily proceedings in a limited space just flow symbiotically.

Personally, I started my R education with Data Science in R, which provided a rather detailed introduction to the tidyverse in R, which is a collection of incredibly useful data packages to organize, manipulate and visualize data, which most notably includes ggplot2 (for data visualization), dplyr (for data manipulation) and stringr (for string manipulation).

One part I love about DataCamp is the up-to-date courses that are organized into career paths in SQL, R and python. This takes away the pain of planning your curriculum — now you only need to follow your path of interest. Some of the paths include:

However, I do have my complaint about DataCamp — that is the poor retention of information after completing DataCamp. With the fill-in-the-blank format, it is easy to guess what is needed in the blank without really understanding the concept. When I was a student on the platform, I tried completing as many courses as I could in the shortest possible time. I skimmed through the code and filled in the blanks without understanding the bigger picture. If I could restart my learning on DataCamp again, I would take my time in digesting and understanding the code better as a whole, not just the parts that I was asked to fill.

Another great feature about the DataQuest is the monthly call with a mentor who will review your resume and provide technical guidance. While I did not personally get in touch with a mentor, I would have in hindsight, since it would definitely have helped me progress much faster.