Sep 28, 2021
This is the practical course.There is some concepts and assignments like: pandas, data-frame, merge and time. The asg 3 and asg4 are difficult but I think that it's very useful and improve my ability.
May 9, 2020
The course had helped in understanding the concepts of NumPy and pandas. The assignments were so helpful to apply these concepts which provide an in-depth understanding of the Numpy as well as pandans
By Michael E L•
Aug 27, 2017
Tough without some prior knowledge of data science in python. It will take you a lot of on your own research, debugging, and looking up techniques, which is fine, but I feel not a great "intro" course. I need a better foundational understanding of pandas and numpy and still barely know why I did what I did, just that the syntax I managed to find works. Both numpy and pandas are quite powerful and its easy to see their usefulness, but I would call this an intermediate course.
By Mauro B•
Mar 5, 2017
A very interesting and useful course for those who want to learn how to clean data in Python.
I loved the opportunity to work directly on the notebook as I was listening to lectures.
I think that more emphasis should have been put in the pros and cons why and how of using the pandas lib. functions in order to accomplish tasks.
Assignments were very instructive and well explained by staff in the Discussion Forum
Many thanks to the staff and to U. of Michigan for the course.
Oct 11, 2020
1) Course demands students to perform their own research for answers, which is how it is in real-life.
2) Good Assignments, which make you think.
3) Helpful mentors on discussion boards.
1) Poorly worded assignments, evident by the number of clarification questions posted on the discussion boards. Much time could have been saved if detailed instructions were part of the assignments.
2) Version issues with Python version for autograder versus my local machine.
By Giovanni S•
May 6, 2020
The course is really based on a hands-on approach and the instructor expects you to navigate though different sources (books, stackoverflow etc) to deepen your knowledge throughout the program. Maybe video contents could be more detailed and focus should be a little less on self-study. Assignments can be quite tough (sometimes too much) but are also helpful in better understanding the subject and how to 'play' with Python programming. Discussion forum very useful.
By Bilal A•
Feb 26, 2017
I found that the videos weren't entirely helpful when tackling the assignments. I had to outsource most, if not all, of my inquiries to external sources (read: stack overflow). Even simple things such as advanced indexing, such as multi-indexing, was unclear. I understand that not everything can be covered, but I feel that basic things should be. Other than that, I enjoyed using the Jupyter notebook, and thought this was integrated very nicely into the coursework.
By devansh m•
Jun 18, 2020
It was a good experience. Learned a lot of things and assignments really gets your brain to work and explore. But, sometimes, a bit more of explanation would have helped for assignments. Like, for me personally, understanding 'for' and 'if' loop was difficult to get as it was used almost everywhere. For, that i had to see other videos and learn from somewhere else. It will be helpful, if more questions to practice will be given before directly giving assignments.
By SUDHEER K K•
May 4, 2017
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. But I thought teaching can be little more expanded to give more detailed information. Assignments were good, but some are little complicated for the starter. I appreciate Staffing team in helping to us in all possible ways with approach and expected behaviors, validation scripts etc. Thanks to all Staff.
By CHEN K•
Feb 4, 2020
i like the course quite a lot. It is very systemetic and well designed. However, personally i think week four is relatively insufficient. Maybe the explanation of mathematics could be less ( coz those are quite basic math at University) and add more test type and examples. Besides, as for the assignment, I think for some quesitons, the explaination is not clear enough, like in assignment 4 last question, "between start and bottom" includes start point or not?
Dec 27, 2016
Quite a fast-paced course, with very quick and packed lectures. Would have preferred the course to be slightly longer e.g. 6 weeks and covering more content. I actually found the style of the coursework to be a plus - though it involved a lot of Googling, this is more reflective of real life, where answers aren't just handed to you on a plate. However, the instructions for the assignments aren't very clear in places, which can easily cause you to become stuck.
By Rui d S•
Jul 24, 2020
This course is worth due to its challenging assignments. If you are taking this with no previous knowledge of data science, you will be expected to make a lot of complementary research.
There is definitely room for improvement in the video lectures - the contents are somewhat limited when compared to the difficulty and what is asked on the assignments. The lecture could and should present more examples/problems and how to solve them in the most efficient way.
By Subham R•
Dec 15, 2019
Nice course to learn data science. I learn a lot of new thing about data science, different python packages used for DS. I felt like week 3 and week 4 assignment were a bit difficult and video lectures should have been in more details and assignments should be more related to the video lectures.
One should have coding background in python in order to complete the course assignments without too much difficulty.
Anyway I'm glad I took and completed this course.
By Ramanadha R•
Nov 17, 2018
Feels great, I am introduces to the Data Science in Python. But this was too much of crash course. It needed a lot of homework outside this course - Youtube, Python docs, stackoverflow and some blogs. That work should have been added as part of the course. And those statistics almost went over my head. As I have no foundation in statistics, I may not choose for the next level of course - seeing the difficulty of the statistics it has introduces so far.
By Manasi P•
Jul 30, 2017
This course was illuminating and rich in information. However, I noticed a huge gap between the content covered in lecture videos and the knowledge level needed to complete the assignments, which made the assignments way harder than expected. This might be intentional, in order to prompt students to explore the full functionality of python and take advantage of online documentation. Overall, this was a good course to really get my feet wet with python.
By Alberto G M H•
Mar 23, 2019
This is a great course for those with some Python background, as the professor clearly states at the first lecture. This was not my case but since we were a team of friends we could solve all the homeworks. If you have not taken an introductory course in Python, neither you have a group of friends to work with, then I suggest you not to take this course. Otherwise, It is a great opportunity to explore advanced Python data analysis techniques.
By Mark F•
Dec 23, 2017
Overall I thought this course was good quality. The videos and lecture material were informative. My only constructive suggestion is that that code is shown too briefly and moves on to new functions too quickly to absorb. The instructor participation in the forum was essential for being able to complete the assignments. But overall I thought this was effective in getting me to learn some Python independently and I would recommend it.
By Andrey K•
May 23, 2020
This is a pretty good course to start immersion in data science. Lectures are very compact and without extra water. Links to books and additional resources are provided. You can get support from teachers in the comments on lectures and assignments.
At the same time, the course requires updating in accordance with the latest version of pandas. During the course, I was faced with the need to rewrite my code to satisfy the old version of pandas.
By héjer s•
Apr 4, 2020
J'ai appris énormément avec ce cours et je remercie les professeurs et tout le staff ainsi que tous ceux qui ont participé dans le forum et qui étaient d'une aide précieuse.
Cependant je trouve que plusieurs questions sont mal posés et une grand partie de mon temps je l'ai passé à essayer de comprendre ce qui est demandé.
J'espère que les questions seront mieux posés et que l'effort sera consacré à l'apprentissage de nouvelles techniques.
By K. Y W•
May 29, 2017
Tough course for a "Introduction to..." course title. Good support resources saved the day. Learnt from doing the assignments and following the tips from the teaching staff. Very practical assignments. Gave a flavor of what data science work involves. Challenging and engaging. The course videos are very well produced. Prof. Brooks is motivating and energizing. The course also gave a human dimension to the work of data science. Thank you.
By Eric L•
Jan 8, 2020
I enjoyed the course, overall. The assignments were challenging and required some help via stack overflow, but they were do-able and not impossible. The notebooks are great for experimenting and self-learning, but the lectures didn't go into enough depth, for my liking. In addition, the speed of the lectures was too fast on many occasions. I'd recommend having some programming background and/or familiarity with Python before taking this.
By Bernardo S L e B•
Aug 4, 2019
The course has indeed taken place over much of what can be considered introductory in data science. However, there is nothing introductory about python, which may bother a more novice student, and the second week of the curos is much more challenging than the others. I believe it is possible to build this same course in 5 weeks to teach more about python and reduce the difficulty of the second week without compromising depth and quality.
By Jared P•
Mar 7, 2017
This course is difficult. It stresses a lot of core skills in pandas and python. I wish there was more instructor support for the times that code just doesn't seem to match up with the grader's expectations. There is still a question in the course that I am relatively positive I answered correctly yet did not receive credit. Overall, the format is incredibly well done and the use of Jupyter notebooks makes the tasks very approachable.
By Dustin H•
Jan 20, 2017
I learned in this course that pandas is a way deeper rabbit hole than it appears on the surface. However rather than teaching me pandas this course mostly just helped me verify that I was learning pandas. The questions in this class need more scaffolding. I ended up skipping most of the in-video questions because I felt that the work I was investing in getting them correct was not teaching me much. More scaffolding could fix this.
By Terry A W J•
Dec 29, 2016
As compared to some other machine learning/data science courses offered "Introduction to Data Science in Python" was very pragmatic. Starting out with simple data cleaning and data structuring may not be the most exciting thing ever, but it was extremely useful to learn the basic tools needed to be a competent data scientist. One point of warning - the homework and projects took me about twice a long as suggested by the course notes.
By Carlos V•
Nov 26, 2018
Introduction to Data Science in Python is a challenging and rewarding Course, the instructor explanations are excellent, and the recommendations in relation to best practices utilizing Python, Pandas and Numpy are super valuable, the assignments are super challenging in particular because of the auto-grader and the substantial amount of pre-processing of data required for the assignments so book extra time to complete this Course.
By Ashley L•
Jan 31, 2022
Great course material! Learned a lot of useful information. My only suggestion to the instructors, would be to refine some small technicalities in the Auto Grader (such as Q8 for assignment #3, or Q5 in assignment #4), which sometimes prevent a correct answer from being awarded points.
If you are considering taking this class, please be prepared to spend at least 50% more time than what Coursera estimates for each week's material!