Python for Data Science
All-Time Top 100University of California, San Diego via edX
- Provider edX
- Cost Free Online Course (Audit)
- Session Upcoming
- Language English
- Effort 8-10 hours a week
- Duration 10 weeks long
- Learn more about MOOCs
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Overview
In the information age, data is all around us. Within this data are answers to compelling questions across many societal domains (politics, business, science, etc.). But if you had access to a large dataset, would you be able to find the answers you seek?
This course, part of the Data Science MicroMasters program, will introduce you to a collection of powerful, open-source, tools needed to analyze data and to conduct data science. Specifically, you’ll learn how to use:
- python
- jupyter notebooks
- pandas
- numpy
- matplotlib
- git
- and many other tools.
You will learn these tools all within the context of solving compelling data science problems.
After completing this course, you’ll be able to find answers within large datasets by using python tools to import data, explore it, analyze it, learn from it, visualize it, and ultimately generate easily sharable reports.
By learning these skills, you’ll also become a member of a world-wide community which seeks to build data science tools, explore public datasets, and discuss evidence-based findings. Last but not least, this course will provide you with the foundation you need to succeed in later courses in the Data Science MicroMasters program.
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Reviews for edX's Python for Data Science Based on 43 reviews
- 5 stars 51%
- 4 stars 44%
- 3 star 2%
- 2 star 2%
- 1 star 0%
Did you take this course? Share your experience with other students.
Write a review- 1
99% of the UNIX coding used in these Jupyter notebooks will not execute on a Windows system. The instructors briefly address that there could be issues for windows users going forward, but don't address how to fix these issues or provide work-around solutions to follow along in the lecture.
The result is I end up wasting tons of time at the start of each lecture, after downloading example files, to devise 'work-arounds' so that I can follow along on my windows system.
I am just auditing the course but the discussion bo…
Overall, this course is a very good practical introduction into Python for Data science. While it does not provide you in-depth with the mathematics behind topics such as classification, clustering, etc., it does expose you to the Python Numpy, Pandas and Matplotlib functions so that you are ready-2-go for real-life problems. I wanted to also understand the mathematics behind it, reason why I took the coursera Machine learning course [using Octave] by Andrew Ng in addition. Combined, they form a good introduction into Machine learning.
All-in-all a highly recommendable course with very good teachers.
The Python section could have been longer more thorough. I had hoped that the course was more about learning Python, as well as learning about Data Science. Fortunately, this was not my first course in Python.
Peer-review of the mini project was a disappointment. I had taken the task seriously. I reviewed more than the minimum required presentations and I took care to leave comments explaining the scores I had given. The two students who reviewed my work gave less than perfect scores with no notes as to how they reached their scoring conclusion.
Review of the final project was more constructive and I appreciated the reviewers time and effort in explaining their scores.
On the very good side:
- the notebooks: they are of quite good quality. You can learn by example from them. They give you concise efficient learning value by practical example illustration
On the good side:
- the Unix part: I did learn more than a few usefull tips there
- the Numpy part: maybe the best concise overview, and most convincing arguments to use Numpy I ever had
On the …
At multiple places instructor used different ways to do the same task which is good for exposure but confusing for naive learners.
There were few concepts that cud hv been explained more easily.
My advice to the team wud be to explain via a small data set which learners cud v…
Several aspects of Data Science are well presented and supported with practical cases.
Assignments and quizes are relatively easily to complete, but the programming assignments have enough depth for the student to learn a lot more from them, if one is interested enough in the subject and spares no effort or time in completing the assignments.
The course has helped me to understand how to use Pandas Dataframes, and after the course I have continued to study the effects of global warming, using one of the datasets in kaggle. Overall, one of the best courses I have completed so far.
This course is a very good introduction to jupyter notebooks, pandas, numpy, matplotlib etc.
It is a practical oriented introduction into Python for Data science and contains also interesting references to the open data available on the internet.
This course gives you a strong foundation for data science - a highly recommendable course with very good teachers.
Regards,
Diego
The course has good references to and makes good use of open data available on the internet, both for the lecture examples and the assignments. The projects are relevant and useful.
- 1
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