Introduction to Data Science in Python

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

(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.

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.