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University of California, San Diego

Python for Data Science

University of California, San Diego via edX


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


4.4 rating, based on 48 Class Central reviews

3.9 rating at edX based on 24 ratings

Start your review of Python for Data Science

  • Anonymous
    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…
  • Profile image for Luiz Cunha
    Luiz Cunha
    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. Th…
  • Dave Guenther
    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 sta…
  • 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.
  • Anonymous
    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. Whil…
  • Anonymous
    First of all you should know this course is aimed to people that, although having a basic knowledge in python, want to brush up or learn pandas, numpy and matplotlib and the general data science process. In my case I already knew how to use them, bu…
  • Gbaawa Albert Peter
    This tool is the best simplest tool to learn. It has some advantages over other other programming languages. Python is one of the most popular and fastest-growing programming languages. Inherently, it is interpreted, high-level, general-purpose, a…
  • Anonymous
    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 f…
  • Anonymous
    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. For…
  • Anonymous
    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
  • Profile image for Praneeth Kumar
    Praneeth Kumar
    must join in this program very helpful for improving knowledge, skills deeply understanding the concepts explained by the professors
  • Anonymous
    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!
  • Profile image for Ciro Emmanuel
    Ciro Emmanuel
    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.
  • Anonymous
    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!
  • Anonymous
    Everything in this course about Python is too SHALLOW. Only when I paid and spent tens of hours on the course did I realize that it was just an introduction. The exercises and quizzes are so easy that they help little to real data analysis.

    How many people can complete learning NumPy / Pandas / SciPy in one single week? I suupose there shouldn't be a lot.

    So disappointed at this course.
  • 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.
  • Anonymous
    One of the better Python DataScience course out there. Good overview of how various tools are used in the Data Science process.
  • Profile image for Varun Sharma
    Varun Sharma
    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 s…
  • Profile image for Harald Von Fellenberg
    Harald Von Fellenberg
    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.
  • Anonymous
    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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