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Online Course

Python for Data Science

All-Time Top 100

University of California, San Diego via edX

Found in Data Science Courses

Provider edX
Cost Free Online Course (Audit)
Session Upcoming
Language English
Certificate $350 Certificate Available
Effort 8-10 hours a week
Start Date
Duration 10 weeks long
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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.

Taught by

Ilkay Altintas and Leo Porter

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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.

1

Anonymous

Anonymous completed this course.

Overall a pretty good course and intro to data science using Python. How much you learn from this course is pretty much what you put into it. The grades are very easy to earn and earning a high grade doesn't necessarily mean that you learned a lot. The best thing about the course are the jupyter notebook...

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Dave G

Dave completed this course, spending 30 hours a week on it and found the course difficulty to be medium.

This was my first time taking an online course as if it were a college class. The content is excellent, and the instructors are also excellent. One of them speaks a little fast, but all video lectures come with subtitles. I didn't rate this 5 stars because there was at least one time where I asked...

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Anonymous

Anonymous is taking this course right now.

This course is absolutely terrible. 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...

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Anonymous

Anonymous completed this course.

I completed the whole course but did not apply for a certificate for the sole reason that I follow MOOC's out of curiosity, not to pursue career interests.

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.

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Anonymous

Anonymous completed this course.

This was a worthwhile course. I enjoyed it. It was challenging, but not exhausting.

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.

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Anonymous

Anonymous completed this course.

A good overall intro to Data Science. Each week you learn something new and build upon skills gained in previous weeks. The jupyter notebooks are great and I reference them even after completing the course. Really enjoyed the instructors as well. The later weeks do throw a lot of material at you (machine learning, natural language processing, databases) but don't dive into it deeply. I know it is an intro class but these weeks left me wondering what I had just learned and why. If you are new to the subject, I would recommend taking this course and reading all extra material suggested in the course to get the most out of it. Cheers

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Anonymous

Anonymous completed this course.

This course is a perfect mix of coding and lectures to kick you off to a perfect start in the field of data science. After having this course, I decided to take up the MicroMasters program offered by UCSD in this area and am currently in my second course. I would recommend this without any reservations to anyone who is looking to delve into the beautiful field of data science. I myself am a mechanical engineer and had no idea about CS. This course however doesn't need any pre-requisites in CS to do well. I had a great time completing it!

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Luiz C

Luiz is taking this course right now, spending 1 hours a week on it and found the course difficulty to be easy.

Overall some (very) good points, and some (very) bad points; that's why at the end I could only give an average mark of 3 for this course. 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...

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Anonymous

Anonymous completed this course.

An extremely good introduction to jupyter notebooks, pandas, numpy and others. I now use these tools most days. Don't expect to be a master by the end, but this course gives you the necessary tools and knowledge to keep on learning; the keys to the door of the library if you will. The projects are interesting esp. the final one. I would definitely recommend to anyone interested in data science and python. I hope to continue with the other courses in this series!

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Anonymous

Anonymous completed this course.

One of the better Python DataScience course out there. Good overview of how various tools are used in the Data Science process.

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Varun S

by Varun completed this course.

Hi I audited the course and this is definetely one of the good courses for getting intro to data science with python . Since iam a working professional i didnt had enough time after office completion 6.30 pm , to complete it on daily basis but i sticked to atleast 3 hrs daily . At multiple places...

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Ciro E

by Ciro completed this course, spending 5 hours a week on it and found the course difficulty to be medium.

A good introductory course to the subject. Many available tools are presented to the student, and also the students are encouraged to investigate much more on their own.

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.

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Harald F

by Harald completed this course, spending 12 hours a week on it and found the course difficulty to be medium.

I have done other Python MOOCs before, so most topics were not new to me. However, I liked the pervasive use of Jupyter Notebooks, since so far I always was a command line hacker, and Jupyter is the first IDE that I like.

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.

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Anonymous

Anonymous completed this course.

I audided this course and did all the exercises but not the mid term and the final project.

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.

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Kelvin Y

by Kelvin completed this course, spending 4 hours a week on it and found the course difficulty to be medium.

i really liked the course. i felt that the material was able to impart to me fundamental knowledge about data science for someone with 0 background on the topic. brush up on your python skills before starting the course, but i'm no expert either and i had a learn as you go mentality. it really is a must that the student should explore outside the given course material and learn to tinker and tweak with the notebooks given to truly learn more

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Anonymous

Anonymous completed this course.

This course was a great opportunity for me to learn more than just coding in python. Since I work as planning engineer, my background is not data science neither programming. However, I managed to finish this course, obviously, with a lot of effort and time. I only can say that this course worths the effort and the time. Now I am the path to finish the micro master in Data Science.

Regards,

Diego

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Ronny W

by Ronny completed this course, spending 5 hours a week on it and found the course difficulty to be medium.

Practical introductory course for data science using python and its major data science libraries (numpy, pandas, matplotlib, ...). The use of jupyter notebooks encourages to dive into it and explore in further detail.

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.

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Vikram A

by Vikram completed this course.

It's a great course. I learnt a lot though I had prior Python knowledge. Instructors are great. You are also rewarded for course engagement in addition to assignments. As an audit learner I scored 70% on the course spending 1-2 hours per week. If your profession is in the area of Data Science or you intend to pursue a career in that area, this course is helpful to build a strong foundation.

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Qaqambile Q

by Qaqambile is taking this course right now, spending 4 hours a week on it and found the course difficulty to be medium.

its a great cause,the teachers are both very good. i am new in programming but at least i could understand. however i was not able to download the videos from my PC, i could not see the button or option to download videos. this second time around please, the button for downloads must be visible. i was able to download from my phone. But from my PC i am not able.

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Ashlynn P

by Ashlynn completed this course, spending 4 hours a week on it and found the course difficulty to be easy.

This course gave clear instructions on how to get started in making data science projects with Python and Jupyter Notebooks. Unlike some other courses, the walkthroughs were prepared as Jupyter Notebooks which saved me from having to stop the videos every two seconds to type out notes. Following the instructions made it easy to prepare the two projects.

Was this review helpful to you? Yes

1

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