[1] Dhar, V. (2013). “Data science and prediction”. Communications of the ACM. 56 (12): 64–73. doi:10.1145/2500499. S2CI

To start learning about the programming and the tools needed for data science, one cannot run away from R and/or Python. They are very popular programming languages which are used for data manipulation, visualization and wrangling. The question or R vs Python is an age-old question that deserves another post on its own. My take?

Please keep in mind — down below you’ll find an affiliate link to the courses. That doesn’t mean anything to you, as the price is identical, but I’ll get a small commission if you decide to make a purchase.

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.

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.

My journey with coding in python and R started with the code-along-with-me sites like CodeAcademy, Datacamp, Dataquest, SoloLearn and Udemy. These sites provide you with the self-paced classes organized by languages or packages. Each breaks concepts down into digestible parts, and gives the user with starter code to fill in the blanks. These sites typically walk you through a simple demonstration, and you will get a chance to practice the concept immediately afterwards by exercises. Some offer project-based exercises afterwards.

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about 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.

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

This is close to what we encounter at work as an analyst — we use different techniques that we’ve learnt to extract information from the same database. The following is the entity-relationship diagram of the SQLZoo question ‘Help Desk’. Given this, you’re asked to show the manager and number of calls received for each hour of the day on 2017–08–12. (Try it yourself here!)

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:

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:

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).

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.

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.

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.