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.
MOOCs stand for Massive Open Online Courses. These arefree online courses from universities around the world (eg. StanfordHarvardMIT) offered to anyone with an internet connection.
How do I register?
To register for a course, click on "Go to Class" button on the course page. This will take you to the providers website where you can register for the course.
How do these MOOCs or free online courses work?
MOOCs are designed for an online audience, teaching primarily through short (5-20 min.) pre recorded video lectures, that you watch on weekly schedule when convenient for you. They also have student discussion forums, homework/assignments, and online quizzes or exams.
Numancompleted 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.
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.