Using Python for Research
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.
What you'll learn
- Python 3 programming basics (a review)
- Python tools (e.g., NumPy and SciPy modules) for research applications
- How to apply Python research tools in practical settings
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| Rating | 3.5★ based on 12 ratings |
|---|---|
| Length | 12 weeks |
| Effort | 12 weeks, 2–4 hours per week |
| Starts | On Demand (Start anytime) |
| Cost | $169 |
| From | Harvard University, HarvardX via edX |
| Instructor | Jukka-Pekka "JP" Onnela |
| Download Videos | On all desktop and mobile devices |
| Language | English |
| Subjects | Programming Data Science Science |
| Tags | Computer Science Data Analysis & Statistics Science |
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What people are saying
having numeric precision errors
Datacamp exercises are especially poor: instructions are often imprecise and ambiguous, with grader having numeric precision errors and unhelpful error reports.
spent roughly 20 hours
I spent roughly 20 hours on the entire course.
tests could be passed
That said, the tests could be passed without that particular install and the problem formulation for the assignments where improved during the course.
contain poor instructions and/or
Unfortunately, they are dull, not challenging and often contain poor instructions and/or feedback.
passed without that particular
few red points
A great course even with few red points.
list populations etc
From a CS perspective you really learn a lot of the pythonic quirks (like the inline generators, list populations etc) as well applying different libraries and data structures.
unhelpful error reports
week 4 consist
Week 3 and Week 4 consist of many case studies which I liked a lot.
assignments where improved
auto-grader can actually
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.
desired result can
The only issues are the grader (where the desired result can be ambiguous) and that (when I took the course) you'd have to downgrade python to get one library to work (the map lib).
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| Rating | 3.5★ based on 12 ratings |
|---|---|
| Length | 12 weeks |
| Effort | 12 weeks, 2–4 hours per week |
| Starts | On Demand (Start anytime) |
| Cost | $169 |
| From | Harvard University, HarvardX via edX |
| Instructor | Jukka-Pekka "JP" Onnela |
| Download Videos | On all desktop and mobile devices |
| Language | English |
| Subjects | Programming Data Science Science |
| Tags | Computer Science Data Analysis & Statistics Science |
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