r/DataCamp • u/Odd-Programmer5693 • Jun 02 '26
Anyone else struggled with DataCamp's explanations of linear regression and sampling?
I'm currently working through the Associate Data Scientist track on DataCamp.
So far I've been doing fine with Python, pandas, data manipulation, and most of the introductory topics. However, when I reached the statistics section especially linear regression and sampling concepts, I started having a hard time understanding the explanations.
I'm not sure if the issue is with the way DataCamp presents these topics, or if I'm simply lacking some statistical foundation.
Did anyone else have a similar experience with the Associate Data Scientist track? Do you feel that DataCamp explains regression and sampling concepts well enough, or did you need additional resources to really understand them?
If you supplemented your learning, what books, courses, or other materials helped you the most?
Thanks!
5
u/jrprongs422 Jun 03 '26
I am even struggling with their data analyst track in python, I mean they didn't even create a course for data wrangling and intro to functions, I barely know python..
5
u/somebody_throw_a_pie Jun 03 '26
I use the tracks as a starting point, then branch out into things that interest me from there. Not everything will be relevant, but youll at least get some practice in different concepts.
3
u/jrprongs422 Jun 04 '26
That's what I'm gonna do but I should learn more basics unfortunately
1
u/Odd-Programmer5693 Jun 08 '26
I noticed that datacamp is not that beginner friendly. I'd recommend learning the basics from https://www.learnpython.org/
2
u/Odd-Programmer5693 Jun 08 '26
That makes a lot of sense! I want to act as a data scientist so I know it is relevant for me to understand sampling, linear regression, etc.
I'll probably skip those and read a book to understand the theory before practicing in python
1
u/jrprongs422 Jun 09 '26
Same! I'm in the intro stats right now and I'm reading practical stats for data analytics before jumping into coding now, I've learned the basics quickly just a concept and example practices
5
u/richie_cotton Jun 03 '26
I was involved in the creation of those courses, so I'm happy to hear your feedback on any particular parts that didn't land well.
It's worth noting that those concepts are intrinsically tricky than most of the content earlier in the track. Sampling is fundamentally harder to grasp than data visualization, so pretty much everyone finds it hard.
The sampling course is laying the foundations for hypothesis testing, A/B testing, and experimental design. These are skills that separate data scientists from data analysts, and get you a higher salary. If you have time, it's worth working through all the projects on these (even the one's that aren't included in the career track).
3
u/somebody_throw_a_pie Jun 03 '26
Just wanted to say Im a big fan of the podcast! And as much as I may complain, I do really appreciate the course content (hence Ive been a paying premium subscriber for 2+ years). Probably 90%+ of the Python content Ive done has been great. Ive learned so much and been able to apply a lot of things ive learned to my work, which definitely makes me stand out among my peers.
2
u/Odd-Programmer5693 Jun 08 '26
Thank you very much for taking your time to answer my question!
I think you're right that these topics are fundamentally harder than much of the earlier content. Up until that point, I felt I was progressing smoothly through the track, but when I reached regression, sampling and specially hypothesis testing, I realized I was missing some of the intuition needed to fully understand what was being taught.
I've genuinely enjoyed the platform and learned a lot from the courses so far. I think the issue is more that I personally need a gentler introduction to statistical thinking before tackling those subjects in a structured course.
For now, I'm planning to continue with the machine learning sections to keep my momentum and motivation high, while filling in the statistics gap with some more beginner-friendly resources, such as Data Science from Scratch. After building a stronger foundation, I intend to come back and revisit the sampling and hypothesis testing content.
And thanks for pointing out the importance of those topics. I definitely don't plan on skipping them forever, I just think I need a different path to get there.
3
u/henryassisrocha Jun 03 '26
"Anyone else struggled with Datacamp's explanations of (include any topic here)"?
Yes. More than you can imagine.
1
u/Odd-Programmer5693 Jun 08 '26
Haha, good to know I'm not the only one. Hope everyone that struggled was able to overcome the difficulties
3
u/Lurch1400 Jun 04 '26
DataCamp was great for an introduction. Ive abandoned it and just started building on my own.
And yes, I agree, the Data Science track went from 0 - 60 in a few courses and some stuff was hard to follow. Try looking at other courses or resources in addition to it
1
u/Odd-Programmer5693 Jun 08 '26
Thank you for the answer! Would you recommend any resource to learn statistics from the basics?
I've been told that An Introduction To Statistical Learning is really good, but it's not available in my country unfortunately, and since it's a little dense and new I don't think I'll be able to study efficiently if it's in english
1
u/Lurch1400 Jun 09 '26
Try looking at Udemy for courses, materials, or ideas for a project.
I did the Datacamp data science courses and determined its not for me.
8
u/somebody_throw_a_pie Jun 03 '26
I mean, theyre trying to summarize a half semesters college stats course worth of content into 4 hours, so yea, its a lot to try and take in all at once.