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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 3,121 ratings
Length 5 weeks
Starts Feb 24 (3 days 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

stack overflow in 50 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.)

The assignments do force you to learn how to python, but through lots of googling, stack overflow and pandas documentation.

Expect long stretch of time on Stack Overflow to look up codes and examples....I almost give up on this one, but still managed to get through after 5 days!

If I have to learn how to do the assignments from Google and Stack Overflow, why am paying for this course?

Much more practice-based than similar courses on Coursera - expect to spend a lot of time on Stack Overflow!

I feel spending too much time on that is "hacking the grader" vs. learning the material.Second, the course emphasized following along in the notebook, playing with it, and doing the assignments via checking Google / Stack Overflow.

Be careful that often the programming assignments go beyond what has been explained in the lectures and therefore require an active search in the documentation, on online forum such as stack overflow or in the resources section.

Documentation, Stack Overflow for assignments is a must which can boost your understanding not just for this course but for a lot more.

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introduction to data science in 38 reviews

Nice course, you've to work hard to pass the course and definitely you learn more Good course but not for beginners Very good introduction to data science with python (numpy, pandas).Only negative aspect is that the assignment questions are sometimes not unambigious.

Third, the title of this course is a misnomer: an introduction to data science would provide an overview of the tools, techniques and scope of the field.

Fourth, the title of this course is a misnomer: an introduction to data science would provide an overview of the tools, techniques and scope of the field.

A hands-on introduction to data science.

Excellent rendition and accessible content Great introduction to data science techniques if you have some prior experience with Python.

It's a really good introduction to data science with python.

Excellent course on introduction to data science in Python Excellent course!

A clear and well structured introduction to Data Science in Python.

I highly recommend this course as an introduction to Data Science.

Excellent introduction to Data Science.

Great introduction to Data Science!

Excellent, if frustrating introduction to data science in python.

Introduction to Data Science in Python is good starter course helped me to introduce to lot of concepts needed for basic retrieval, cleaning and manipulating.

Very informative introduction to Data Science using Python.

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machine learning in 31 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 .

This is a very nice, structured organized course to start on empirical machine learning.

After studying the course of Andrew on Machine Learning, I want to study a course , which focus on python.

I once chose the Machine Learning Specification , but the course use the non open source python packages.

The teacher should slow down a bit and show some more examples (for inspiration watch Prof. Andrew Ng from Stanford lecture on machine learning).

Its a very good course to start learning for data analysis which can work as a foundation to learn more in data science and machine learning.

Best course to learn Pandas library and some basics of Python required for Data Sciences and Machine Learning.

I chose this course to learn more about technical skills to do research in data mining, data science, and machine learning.

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fast paced in 30 reviews

Fast paced so be prepared to 'pause' to research or think about things.

The fact it is fast paced did not bother me.

Good Course Quite a fast paced course, would be good to have some background knowledge of Python and statistics but not essential.

I wanted a more advanced and fast paced course so a lot of the reviews about it being difficult didn't turn me off.

It helped a ton with learning and were the most important part very fast paced, but very useful The course is very good to get started with Python essential for M Waste of time.

Great course for intermediate python learner Fast paced, very concentrated course.

Fairly fast paced.

Good course - fast paced and needs a lot of self learning from the web to get through the challenging assignments.

Fast paced course but still useful if you are getting yo very good introductory course though with a steep outside the lectures learning curve.

Thought assignments could have been better designed Fantastic course Great course and challenging if you are still learning Pandas It’s very fast paced.

As others said, this course is fast paced, has only brief information in the videos, and has challenging programming tasks that requires students to get the required information elsewhere that was not given in the intros.

I think we need to have more interactions into the lectures, also the material inside the course is not enough most of the course i do it searching and copy and paste the code from the online materials Its too fast paced and less elaborated An extremely demanding course, especially on the coding exercises.

It's a little fast paced but you can always go back and watch the videos again.

Fast paced course with good supplementary materials.

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university of michigan in 21 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.

The University of Michigan does not disappoint and neither does the delightful instructor Christopher Brooks.

Thanks Coursera and University of Michigan.

The first course on Python from University of Michigan was really very good.

Thank you for the scholarship Coursera and University of Michigan.

I'd hoped for something similar for Python with this class, but I guess The University of Michigan isn't Johns Hopkins.

!BIg Thanks to Coursera & University of Michigan for putting such an awesome course !!!

thanks university of michigan for letting me do this course Videos not very explanatory, ended up being a lot of self-teaching, video seemed too scripted and didn't really explain concepts well This is not really a course.

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looking forward in 24 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!

Looking forward for the next courses.

Looking forward to taking the next courses.

However, I'm looking forward to when the rest of the specialization is available, as this looks like a good track!

Chris is great at explaining things in an accessible manner, and I'm very much looking forward to going into more detail in the rest of the specialisation.

I am looking forward to the rest of the courses in this series.

great hands on course, with challenging exercises and fast paced lectures.the homework assignments need some rephrasing, as it is not always clear whats being asked.looking forward to the second course in this specialization Overall I thought the level of the course was good.

Mostly focused on the Pandas library - you'll be ok if you know a bit of Python, or have experience in another language.Looking forward to subsequent courses.

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christopher brooks in 16 reviews

Thank to Christopher Brooks to teach me this course and I am highly recommend this course to all those want to do somrething in data science.

homework format of outputting values from functions needs to be improved The course content and explanation is superb, Assignments shall have access to course auditing students also though they shall not be graded, anyways thanks to Prof. Christopher Brooks to deliver such great lectures.

I thank Christopher Brooks and the team.

Christopher Brooks teaches in a very clear and objective way, in addition to the weekly assignments that are challenging and puts into practice all the content you've learned trough the videos and the material.

At the same time, the assignments are somewhat difficult for those who are not familiar with Python, but for me it's just OK. What I want to make complaints about the assignments is that sometimes the Autograder is so rigid that I have to try one question over and over again until the Autograder "feels happy", and for me, sometimes the gap between "correct answer" and "incorrect answer" isn't so large...And finally, thank you, teacher Christopher Brooks!

Thank you Mr. Christopher Brooks for great teaching.

Thanks to Christopher Brooks for his efficient way of teaching and excellent assignments.

christopher brooks is great.

This is one of the best courses on coursera by offering, the instructor Christopher Brooks has a great ability to deliver a lot of information/knowledge in a concise manner!

Thank you "Christopher Brooks" sir and Coursera to gave this amazing experience to me.

I can't wait to continue with the remaining modules.Special thanks to Christopher Brooks Appreciated !!

The assignments are really time consuming and challenging.Also, I had to refer to stackoverflow for countless number of times to derive the logic.The instructor has only touched upon the material but additional videos should be included by the TAs for the assignments.Thanks,Sourav Lectures clearly explain material and you can gain first experience with practical assignments Christopher Brooks was exceptional but the other guy was going too fast.Overall it was a good course.

Thank Prof. Christopher Brooks and Coursera and the ones who share their problems and ideas in the forum.

After holding on for the first 2 weeks (it's a very useful topic after all), I gave up and decided to learn from the "learning the Pandas library book", which is a very good summary of the main Pandas functions and methods (and which was recommended by Dr Christopher Brooks), and I was able to follow it very easily.

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hypothesis testing in 14 reviews

i don't quite understand all the concepts but it is free :) Amazing course for an introduction to the pandas library and its main data structures Series and DataFrame.Improvements could be made to the hypothesis testing section of the course.

I would replace that with explanations on how to use jupyter notebook and getting more from the course in that way.In all i enjoyed this course, I particularly enjoyed Week 4's lectures on hypothesis testing.

I've learned a little about statistics, specially hypothesis testing.

The course is based on getting hands on experience of pandas package and some hypothesis testing.

be sure to learn basics of pandas and hypothesis testing before enrolling the course.

could have explained Hypothesis testing in better way with good examples very nice explanations provided in the videos really liked the course.

Amazing course, good introduction to pandas and I loved the hypothesis testing part !

nice course to begin with in order to do data science Week 4 lectures could have focused slightly more on hypothesis testing, perhaps delving a bit deeper into the thought process and methodology of coming up with hypotheses, designing an experiment to prove it, executing it, summarising and interpreting the results, etc.

The topics could have been explained in little details, however the exercise were quite challenging Very good course and I learned all the new stuff like pandas, numpy and hypothesis testing etc.

The week consists mainly of the main project assignment where 50% of points are given on data cleaning and munging (contents of weeks 1-3) and the other 50% of points are on modelling and hypothesis testing.

This is a great introductory course for learning python (data types, data wrangling, simple hypothesis testing).

I learned about the Python lambda functions,dates ant time,objects and map() in week 1.In week 2 and 3 I learned about pandas.In week 4 I learned about the distribution and Hypothesis testing in Python.In video quizzes cleared the doubts regarding fundamentals of the topic.

Introduction to hypothesis testing is too short.

Also, it includes hypothesis testing, which you can apply on any data-sets to get answers to many insightful questions for that data.

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lot of self study in 6 reviews

The course is properly structured and tutorials are very good The lecturer encourages the student to do a lot of self study and finish very challenging assignments.

Very good course, forces to do a lot of self study.

The assignments required a lot of self study which is great but at the beginning, every assignment looks daunting and discourages a little.

Assignments are very good as compared to videos Nice course, Love it :) Great introductory course overall, but requires a lot of self study from you.

The last assignment project is a bit harder, and requires quite a lot of self study, but the course is well structured and worth it.

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trial and error in 8 reviews

Requires a tremendous amount of self-study and trial and error if you're starting from ground zero in terms of pandas knowledge, but the reward is a level of comfort and facility with pandas dataframe manipulation.

Also took extra time by some trial and error to get right format of results.

Assignments are challenging, but some questions are very vague and require lots of trial and error guesswork to get the autograder to accept them.

The grader bugs considerably, and the questions are often not well defined, which requires a lot of unnecessary trial and error to understand what exactly the question was about.

The learning of python coding rely heavily on your own trial and error, which you could do even without this course.

I really like learning by trial and error, and I think that is how coding is typically learned.

For those who learn through google (Stackoverflow) and of course, trial and error (I think that's the daily business of a Data Scientist / Data Analyst), I would recommend the course.

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dr. chuck in 9 reviews

I think that the content was not explained as well as Dr. Chuck did in the introductory course.

Guys, you are AWESOME!I started learning Python 6 month ago with Dr. Chucks course "Python for Everyone".

(To give you an idea of my skill level, I have coded in R for a few years and finished Dr. Chuck's entire Python specialization before starting this course.)

somehow it seems they just took an on-site course, played with some fancy technical solutions and call it now a MOOC... they seem not really aware of the fact that on-site teaching and MOOCs require completely different types of pedagogic methodsamazing how the same university can offer both the best MOOC (Dr. Chuck's) and one of the worst (this one) Amazing Training on Python Data Science, doing the course assignment its like read the book Python for data analysis.

Very disappointing after starting the Coursera course with Python for everybody with Dr. Chuck.

Dr. Chuck class was amazing and I will always recommend it for people that want to start learning Python from scratch.

Important material, but taught in a far less optimal manner than Python for Everybody (or maybe Dr. Chuck's material is just the gold standard).

Great intro course My first online course on Coursera Dr. Chuck's "Python for Everybody" specialization.

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other programming languages in 5 reviews

Amazing Light-weighted Python intro that requires some previous experience with Python itself or other programming languages like MATLAB.

The content and the instruction were great: just the right level for someone like me with experience in other programming languages a decent familiarity with stats.There were a few glitches with the auto-grader for the assignments, but nothing to onerous.

I have a lot of experience working with data in other programming languages and found the assignments very challenging.

Thanking You Sir it was a very good course and I'm happy Excellent Course Dr. Chuck (the instructor) is very thorough and detailed in his explanation of concepts.Since the target audience is individuals who do not have/ have a little background in programming, the speed is ideal.Assignments are helpful Great course for beginners, as well as for those with previous data science experience with other programming languages (i.e.

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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 3,121 ratings
Length 5 weeks
Starts Feb 24 (3 days 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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