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Introduction to Data Science in Python

This course is a part of Applied Data Science with Python, a 5-course Specialization series from Coursera.

This course will introduce the learner to the basics of the python programming environment, including fundamental python programming techniques such as lambdas, reading and manipulating csv files, and the numpy library. The course will introduce data manipulation and cleaning techniques using the popular python pandas data science library and introduce the abstraction of the Series and DataFrame as the central data structures for data analysis, along with tutorials on how to use functions such as groupby, merge, and pivot tables effectively. By the end of this course, students will be able to take tabular data, clean it, manipulate it, and run basic inferential statistical analyses. This course should be taken before any of the other Applied Data Science with Python courses: Applied Plotting, Charting & Data Representation in Python, Applied Machine Learning in Python, Applied Text Mining in Python, Applied Social Network Analysis in Python.

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University of Michigan

Rating 4.2 based on 2,385 ratings
Length 5 weeks
Starts Jun 17 (5 weeks ago)
Cost $79
From University of Michigan via Coursera
Instructor Christopher Brooks
Download Videos On all desktop and mobile devices
Language English
Subjects Programming Data Science
Tags Computer Science Data Science Data Analysis Software Development

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What people are saying

According to other learners, here's what you need to know

jupyter notebook in 46 reviews

At intervals, the view switches to a Jupyter Notebook, and the lecturer walks through the material far too fast to allow anything to "sink in."

Another issue I had is the grader was not grading the version of my code I saw in the jupyter notebook but rather an earlier version it had saved.

FWIW: My recommendation is to get to know Jupyter Notebook early and follow along with the lectures by opening the Week[x] files in the course download folder.

The course uses Jupyter notebooks for assessments, which was refreshing, and has in-video code to work-through which was also much appreciated.

In general I had fun solving them, and althoug I've had my share of Jupyter Notebook and Grader's issues I was able to complete the course.

I don't think the Jupyter Notebooks format is the adequate for teaching.

The jupyter notebooks to follow the lessons are brilliant!

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stack overflow in 39 reviews

The course in and of itself is not _terrible_, but expect to do a lot of searching for outside help on Stack Overflow and the like as the lectures do not provide anywhere near sufficient material to solve the problems.

I did not find Stack Overflow as helpful as the instructor suggested.

The prof and TA refer to using Stack Overflow to figure it out early and often!

This course is a mess:-Not well structured-They don't properly guide you in the learning process : better courses from better universities, as Stanford ones, describe all you need to complete an assignment, or give you a good and deep introduction to the libraries/framework you have to use... here they just explain part, non including many info you need in the assignment, so, you have to spend a lot of time trying to find proper documentation, reading other external tutorials, checking people with same issues in stack overflow -some even with the same datasets, I am not sure if they were from the same courseA lot of stuff to improve, I recommend to you to look for another course Very good Very good course.

If I have to add another 3h per week to find the right advice on stack overflow, that must be stated somewhere so I can plan ahead.

Learned a lot (mostly thanks to stack overflow) but the course also opened my eyes to all the possibilities available out there and I feel like i'm only scratching the surface!

However, I definitely think that learning to utilize the internet (stack overflow etc.)

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machine learning in 27 reviews

I am an undergraduate Physics student who intends to delve into the remarkable field of Data Science and Machine learning.

The course was very informative to have solid baseline to heading on machine learning The lecture content is rather short and you have to search a lot on your own.

I decide to learn the machine learning course next, I love doc.Christopher!

Before Machine Learning comes a lot of Human Action.

Moreover, he barely touches on why any of this is important, does not go over scikitlearn or numpy, both very important in data science / machine learning.

You should already have some experience with Python and Machine Learning before you start this course.

If you are thinking of doing something in the field of machine learning and Artificial intelligence .

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looking forward in 21 reviews

Looking forward for same with next course.

Otherwise great introduction to data processing in Python, looking forward to the next course in the specialization.

Overall, I think I learned a lot of valuable skills in the course and I'm looking forward to continuing the others in the specialization.

Looking forward to the following courses!

I really wanted to like this class and was looking forward to learning data science in Python but this isn't the way to do it.

Looking forward to another great learning experience in course 2.Thanks,Neel Roshania The content is really good, but their explanation of the topics is insanely terse; by both the professor and his graduate student.

Looking forward to the next class!

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university of michigan in 15 reviews

Thank you Coursera and also University of Michigan.

Overall I expected better out of University of Michigan.

I took and passed this course with a view to doing the specialisation but I'm not going to waste any more money on University of Michigan courses.

Thanks Coursera and University of Michigan for bringing us such a great Learning opportunity.

Kudos to Coursera and University of Michigan!

Superb course and the staff from University of Michigan were quite committed with the learners, they've given me very valuables tools and skills, thanks a lot.

For an instructor from the University of Michigan whose bio says "I work with colleagues to design tools to better the teaching and learning experience in higher education," I was expecting a lot more value for the time spent in this class.

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highly recommended in 12 reviews

Highly recommended course.

Highly recommended to anyone interested in the application of python in data science.

The course loses a star for the incredibly fast pacing of the lectures, and the off topic data science assignments.Still highly recommended.

Highly recommended that you get a solid understanding of the fundamentals of Python before starting this course.

Highly recommended for enthusiastic learners Assignments need to provide better feedback when there is an error Awesome course, not very easy, but very useful.

Highly recommended for anyone interested in data science.

Highly recommended course for Data Scientists.

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Reviews

Sorted by most helpful reviews first

Guest says:

There's a lot of great content packed into this course. The information learned is useful and it really brought my data science game up another level. Unfortunately, one big gripe I had with this course are the assignments. They take a very long time to complete and the autograder rejects answers without giving much detail, so you spend a couple of hours tearing your hair out wondering what went wrong. Hopefully they can make assignments + grader better in the future.

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Coursera

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University of Michigan

Rating 4.2 based on 2,385 ratings
Length 5 weeks
Starts Jun 17 (5 weeks ago)
Cost $79
From University of Michigan via Coursera
Instructor Christopher Brooks
Download Videos On all desktop and mobile devices
Language English
Subjects Programming Data Science
Tags Computer Science Data Science Data Analysis Software Development

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