This course bridges the gap between introductory and advanced courses in Python. While there are many excellent introductory Python courses available, most typically do not go deep enough for you to apply your Python skills to research projects. In this course, after first reviewing the basics of Python 3, we learn about tools commonly used in research settings. This version of the course includes a new module on statistical learning.
Using a combination of a guided introduction and more independent in-depth exploration, you will get to practice your new Python skills with various case studies chosen for their scientific breadth and their coverage of different Python features.
Week 1: Python Basics
Review of basic Python 3 language concepts and syntax.
Week 2: Python Research Tools
Introduction to Python modules commonly used in scientific computation, such as NumPy.
Weeks 3 & 4: Case Studies
This collection of six case studies from different disciplines provides opportunities to practice Python research skills.
Week 5: Statistical Learning Exploration of statistical learning using the scikit-learn library followed by a two-part case study that allows you to further practice your coding skills.
Sabine S completed this course, spending 16 hours a week on it and found the course difficulty to be medium.
The good things:
If I only had to evaluate the video lectures, the course would get 5 stars: excellent lecturer, concepts extremely well explained, very good overview of Python tool boxes for data analysis. Very good melting of white board and coding....
The good things:
If I only had to evaluate the video lectures, the course would get 5 stars: excellent lecturer, concepts extremely well explained, very good overview of Python tool boxes for data analysis. Very good melting of white board and coding.
The bad things:
- The quizzes are mostly trivial. They are on the lowest rank of Bloom's taxonomy, basically only checking factual knowledge, but not checking understanding of concepts.
- Videos where at some point outdated not reflecting current state of a Python package. Instead of updating the video, they just wrote above the now correct implementation
- The programming assignments - most definitely the worst part of the course:
a) Obviously error prone, sloppy description of exercises. Corrections mentioned on mostly dead discussion forum were ignored.
b) Grader which lead obviously incomplete answers pass as correct.
c) One case study wasn't available for entire months.
d) Very often trivial problems to solve during assignments, as e.g. just typing many, many print statements. This should not be a problem assignment. Again: Assignments should check conceptual understanding, not banalities.
e) Assignments used pandas functionalities which were not (yet) introduced in course, so somehow had a disjunct feeling of course content and assignments.
This course most definitely lacks the curation of tutors correcting well known errors mentioned by many students. I personally find it quite offensive to still charge for a course which is most definitely very poorly maintained. Programming assignments: Fair too many, trivial questions. Lecturer himself: excellent.
Numan completed this course, spending 5 hours a week on it and found the course difficulty to be medium.
Most people don't know about this course. I found it a very great source of Python,Numpy, Pandas and Matplotlib. First two weeks of the course are teaching Python and the necessary libraries for research. Week 3 and Week 4 consist of many case studies which I liked a lot. However, some exercises are really difficult and not relevant to topic.
Overall, I recommend this course if you have some knowledge of Python and Numpy. It certainly can be challenging for beginners.
Anonymous completed this course.
Datacamp exercises are especially poor: instructions are often imprecise and ambiguous, with grader having numeric precision errors and unhelpful error reports. There are issues that were reported more than half a year ago that are still not fixed.
Problems are rather simple, with quite a few Python coding choices that would be frowned upon if you'd do that at work one day.
It might be an interesting course for a beginner, but there are so many better out there that it's just not worth the time. The course attempts both to teach you some Python and to teach you some basic data science skills. It falls short of both.
Andrew Braun completed this course, spending 10 hours a week on it and found the course difficulty to be medium.
If you want a quick introduction to some of the most widely-used Python libraries for data, this is a great course to check out. It does have some unfortunate flaws, but sometimes fighting with the auto-grader can actually be productive--it forces you...
If you want a quick introduction to some of the most widely-used Python libraries for data, this is a great course to check out. It does have some unfortunate flaws, but sometimes fighting with the auto-grader can actually be productive--it forces you to try lots of different approaches before you realize what's going on.
- Great lecturer. He explains and demonstrates everything very clearly.
- Great topic coverage. I feel like I learned a lot of good basic skills in this course.
- The Datacamp auto-grader. I swear this course would have taken me half the time to complete without the bugs in this thing. Sometimes the exercise and the grading criteria conflict; sometimes the grader only accepts one very specific way of doing it; sometimes it just decides it doesn't like you. Luckily, you can find most of the fixes in the discussion forums.
- Some of the modules went a bit over my head and I didn't understand exactly what some of the code was doing statistically. That's on me, since I'm not 100% on research and stats, but I felt like the course was missing some explanations of the concepts we were implementing. That wasn't the case in most of the course, though: usually things were covered very well.
Overall, I absolutely recommend it! In its current state, though, maybe skip the certificate. A lot of your grade depends on some pretty buggy software, and in one case you couldn't complete the assignment at all because of a problem with the dataset. Great for more casual learning, especially if you're not a grade-perfectionist.
Clément Poiret completed this course, spending 2 hours a week on it and found the course difficulty to be medium.
A great course even with few red points. The course is a good introduction to Python and some concepts but I'd have liked more detailed/advanced case studies - like the one on DNA translations - with more theory.
One case study isn't available due to a bug as another review stated.
Videos are really well, the quality of images and sound is better than many other courses and the professor is really good!
The code you produce is reviewed through Data Camp, which I don't like very much but it may not be your case.
Despite all these points, I really enjoyed this course which is covering many fields, thank you.
Lectures are well structured and clear. I definitely learned a bunch of new things.
Comprehension tests are mostly boring and ridiculously simple only checking if a student was sleeping during the video. Nothing to reflect about, no questions to evoke some deeper understanding.
The homework assignments are poorly worded, which sometimes leads to inconclusive results. Some exercises are missing with the next ones relying on the missing ones. The questions are often ambiguous.
Kristina Šekrst completed this course and found the course difficulty to be medium.
This isn't an introductory courses, but gives you a first-week overview of Python. It's useful for intermediate-to-advanced levels, in order to try out case studies related do classification and data analysis of various datasets and areas, such as biology and DNA sequencing or natural language processing. Instructor is great, but the exercises are sometimes dull and not really creative, but I guess it's useful if you want to dive into a specific area. All in all, a nice course.
Prashant SIngh is taking this course right now, spending 35 hours a week on it and found the course difficulty to be medium.
-This adds more knowledge to my introductory knowledge of python.
-Videos and Prof. is good.
-But the datacamp exercise are boring and instructions are not very clear.
-I did 2 weeks then lost interest.
Anonymous completed this course.
A LOT of content, excellent professor and teaching, homework sometimes annoyingly difficult, sometimes easy, took longer than I had first thought.
Osama Heba completed this course and found the course difficulty to be easy.
This is probably one of the best courses I have audited. It is well presented and it relies showcase the different concepts and techniques in carefully chosen real projects. I would say it suits is for students with a little bit of background or those how have the capacity to catch up with a slightly sharp learning curve.
Anonymous is taking this course right now.
The course provides a great deal to learn Python and apply the programming knowledge in various areas. The case studies fulfill the application part. Difficulty level is medium. Course requires good knowledge of Python beforehand and basics are taken in depth. Gaining a lot from this course.
Ilir Sheraj is taking this course right now, spending 8 hours a week on it and found the course difficulty to be hard.
I would like to look at the course from two aspects:
1. Instructor: He is really awesome, explains the concepts clearly and concisely. He is one of the best i have seen in programming, almost on the same level as the legendary Eric Grimson, just not...
I would like to look at the course from two aspects:
1. Instructor: He is really awesome, explains the concepts clearly and concisely. He is one of the best i have seen in programming, almost on the same level as the legendary Eric Grimson, just not that cool :)
2. Materials: The check exercises are relatively easy and relevant to check the understanding of the concepts explained. However, the assignments are way harder than the materials covered in the course, at least in the first two weeks. The course is not for everyone and not sort of "cool", but if you aspire to have a good grasp of creative problem solving and computational thinking, it is the place to start because it doesn't just ask you to carry out tasks like a robot. Also, those who wanna take this course need to have some strong foundations in python, and 2-4 hours a week studying is misleading.