Learning Experience at DataCamp (R & SQL)

5 min readJul 17, 2020


First I noticed the website, DataCamp is when I was trying to build a Support Vector Machine (SVM) by myself for a side project. I googled some tutorial videos and DataCamp showed up. The learning experience is quite unique at the first time. It’s not only video but also combined with practical exercises, which is a great interactive learning process.

Why learn program by DataCamp? Why not start side projects?

Frankly speaking, the fasted way to learn a new language is through conducting projects. Like my IT friends, he used Java, C++, and python coding plenty of functions, even packages. In the beginning, I would like to emulate his learning methods for learning R. Nevertheless, R is a robust object-oriented language which mostly uses for data cleaning, data analysis, and statistical process. Here comes a big issue. If you want to start a side project, you need to find some interesting data. It is rather hard for a person like me who doesn’t have access to most of the databases. Therefore, Datacamp is like an incubator, helping consumers construct data scientist skills without searching practicable accessible databases by themselves.

What is the learning process in Datacamp? Is it suitable for beginner?

The courses in DataCamp include many analysis tools, like Shell, Tableau, Excel…, let alone R and Python. Thus, it only has three language tracks of R, Python, and SQL respectively. In the following paragraph, I would introduce the benefits of tracks. Firstly, I would like to introduce the characteristics of it. DataCamp provided with:

(1) tutorial videos

(2) coding practices

(3) high quality database sources

The learning process is suitable for apprentices of Data Scientist, through tutorial videos to learn the background knowledge, and coding practices to deepen the concepts. High-quality databases prevent from wasting time searching data on the Internet.

Plan smarter, learn more efficient

Course selecting Strategies: Must to be resume-driven

Tracks of Datacamp are also valuable certificates. They clearly indicated which hard skills you seized. I have seen some data analysts they edited tracks of Datacamp on their Linkedin profiles, so I deemed they are useful for building up your resume. It is suggested to accomplish for a maximum of 3–4 tracks on your résumé. Too much would seem to be abused. Furthermore, only choose 1 language as major first, R, or Python. Learning two program language might cause a distraction. If there is spare time, accomplishing a SQL track may benefit for résumé as well. The selection of mine is two R tracks “Data Scientist with R” and “Machine Learning Scientist with R,” and “Data analyst with SQL” for SQL.

How to subscribe a proper plan

Datacamp is a chargeable website. After 1 week free trial, Datacamp would block your courses and ask you to buy a plan. The beginner plans I consider mainly have two types, $25 per month billed yearly(total $300), $29 monthly. I strongly recommend choosing the plan of $29 monthly, because of the short period just like the deadline for homework. It would rocket the productivity and efficiency. Besides, as my former paragraph mentioned, you don’t need too many certificates of track for resume. One month is enough for beginners to go over the 3–4 tracks.

From my learning experience, DataCamp contains…..

Great user experience and interface

Datacamp designed courses with progress bars. Every time you finished the tutorial videos or coding practices, you would get experience point(+XP) and the progress bar would lengthen, giving you a sense of achievement just like playing games. The user experience promotes users to keep into the courses and unconsciously finish them.

Secondly, almost every course has a wrap-up chapter, in order to review all the concepts taught in the course. The recap would not dive deep into the details but give a general idea about why, when, or how to use this code for coping with real problems. It is the most useful part for me overall.

Smooth and connected learning process

It is unnecessary to remember all materials in the course. The attainment of a programmer is to establish the accesses for various methods, algorithms, and codes which may use in your future career. You might concern that attend many courses, but if the courses do not give a chance to review even the accesses to query would be forgotten. However, in Datacamp the same concept would be reviewed in one track many times, depended on its importance. Moreover, different tracks would occasionally overlap with others to some extent. For example, “Data Scientist with R” and “Machine Learning Scientist with R” both cover the knowledge of unsupervised vector machines. And Datacamp also gives authority to practice repeatedly after you complete a course.

Note: If the account expired, it is no longer to practice the courses…..

Difference in difficulty among courses

Due to each of the track which is composed of many courses, and the various teachers for each course, the difficulties are usually inconsistent. The phenomenon somehow has an impact on the learning experience. The former course might be like “Rhaegal” (killed by a scorpion bolt to the neck), but the following course is like “Drogon” in “Game of Throne”, very hard to slaughter. The situation is especially obvious in coding practices. Some practices give vague instructions, too many spaces. These practices usually cost much more time to go back video finding the hints and corresponding code. In addition, the quality of the hint is diverse as well. Sometimes the hints are useless, focusing on the wrong key. Few hints are just identical to the instructions, which make me feel in vain after putting the hint button.

Sporadic bugs in practices

This point is a crucial reason for me. While coding, debugging dominates most of the time. However, after sending answers there are usually two scenarios occurred

(1) Error message: syntax get wrong and the system returns the red instruction inside Console

(2) Incorrect submitted code: submitted answer returns different outcome from the correct answer, and the system would highlight the wrong spot on the code

In some practices, I cannot end them because of incorrect submitted code but ironically I cannot find the bugs made by myself. Based on curiosity, I would click hint until the answer shows up, and surprisingly, sometimes my submitted code is already identical to the answer. This situation occurs not rarely, thwarts me stepping further and wastes my time.

Position of DataCamp in the Data scientist’s world

Datacamp could be a start, bringing you into the mindset of data analysis. Thus, if you already have some experience, you could also leverage on the courses to find out whether the other programmers share similar coding logic with you when you encounter the same problems. Or maybe you would find out someone could use more succinct code than you did. Data Science is fascinating, and there is no end to learning.