Introduction to Data Science in Python

The 5 courses in this University of Michigan specialization introduce learners to data science through the python programming language. This skills-based specialization is intended for learners who have a basic python or programming background, and want to apply statistical, machine learning, information visualization, text analysis, and social network analysis techniques through popular python toolkits such as pandas, matplotlib, scikit-learn, nltk, and networkx to gain insight into their data.Introduction to Data Science in Python (course 1), Applied Plotting, Charting & Data Representat

Created by: Christopher Brooks

Produced in 2016

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Overall Score : 86 / 100

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Course Description

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.

Instructor Details

Christopher Brooks

Christopher Brooks is a Research Assistant Professor in the School of Information and Director of Learning Analytics and Research in the Office of Digital Education & Innovation at the University of Michigan. His research focus is on the design of tools to better the teaching and learning experience in higher education, with a particular interest in understanding how learning analytics can be applied to human computer interaction through educational data mining, machine learning, and information visualization.



145 total reviews

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By Ivan L on 23-Mar-19

I am surprised that it was a 4-week course. It could have been condensed to 1 slide stating "Go read a few books and if you're stuck, consult Stackoverflow".I cannot believe the average score that this course got and I mean that literally: I can't believe those are real numbers. There are no course materials, except some crappy unformatted transcripts, the speed is ridiculous and didactically it's a disaster. I now feel sorry for given Andrew Ng's course on Machine Learning only 3 stars, compared to this it should have received 7 stars. PS I have 30 years of experience in teaching data analysis and statistics, both in industry and at university and I have 35 years of programming experience.

By Wang H on 1-Jan-19

It was the worst experience I had in coursera. The video content is too general and has little connection with assignment. The assignments are challenging but poorly organized. Students are encouraged to search and self-learning, which is irresponsible and inefficient. I register the course to learn things systematically and save time! I spent about 8 hours on searching desperately for each assignment. My passion for Python and Data Science almost disappear. The version of pandas used in this course is 0.19.2, which is not convenient for using some functions. The assignment is not explained after grading. I had no idea about how well my solution is and whether there is a better way to do it. (The forum may be useful, but it took too much time for searching suitable answers) I have learnt Java before and had little experience in Python. Maybe it's the reason for the longer commitment on those assignments. However, as a former teacher, I do suggest some improvements on the pedagogy in this course and take more responsibility for the students.

By Thomas K on 17-Mar-19

Extremely dissatisfied. Lectures are useless and the instructor didn't put any effort into designing a curriculum. After struggling for days on the second assignment I purchased the recommended text for the class. Week 2 effectively starts at chapter 7 in the book. Instructor needs to replace his absurd amount of face time some slides showing the application and logic behind the methods he is trying to use. Jupiter notebook is a tool designed to make the instructors job easier, doesn't help the student at all. I could go on but this course has drained the energy from me. FYI, half the people that complete (or attempt to to complete) this course don't continue on with the specialization. This should speak volumes.

By Marcus M F T on 7-Mar-19

I'm not sure you should be paying for a course where you have to search the Internet to learn how to do the assignmens. I could be doing that for free! Is the certificate worth it?

By Saksham S on 6-May-19

Huge disparity between the course videos and the assignments provided. A background in basic programming is highly recommended. The assignments provided are of good quality however, and provide a great learning experience given one can get through them.

By Pratik M on 15-Feb-19

This is not a course, rather just guidance to use StackOverflow. The trainer Prof. Brooks is highly unlively and plainly reads out/speaks some statements. He teaches only 10%, remaining 90% you need to explore on your own. The Assignments have the most difficult questions and for solving them, students are not even given any getting-started questions, to begin with! If you wish to learn Data Science/Data Analysis then I would not recommend this course since it is not worth the time, effort and money. Also, the title of the course is devaluing the efforts we put it. The entire course is focused towards using Pandas to perform Data Analysis / Data Cleaning / Data Wrangling / Data Munging / Data Preprocessing and thus I would recommend that the title of the course should be one of these rather than "Introduction ..." which hardly gives any weight to what hard work this certification demands!In the brief course videos professor makes some hand gestures and the background shows the people working - both has no relevance and rather prove to be a distraction. Above that auto-grader comes with its own lot of problems which consume hours and days of all the candidates. This course is online for more than 2 years now and I doubt if Coursera really takes such feedback seriously and takes any action for improvement!Request Coursera / UM to go through all the reviews:

By Karen B on 14-Apr-19

This is a terrible course. The "instructors" give quick little lectures, then we're told, for the somewhat complicated (for a beginner) assignments, to look things up on StackOverflow to figure out how to do the assignments. Um, no. I want a class so I don't have to tear my hair out dealing with the internet.The Johns Hopkins Data Science Specialization was fantastic. They actually taught how to use R to do data science. I'd hoped for something similar for Python with this class, but I guess The University of Michigan isn't Johns Hopkins.

By Artem E on 12-Apr-19

Very rare I write review. And this is rare course that teaches nothing. The lector explains nothing. "I encourage you self-learning. Read the documentation. Search google. Ask If you don't know python, take another python course. If you don't know statistics, take another statistics course. I encourage you self-learning even more." - that is what lector sais to you.

By Matthieu L on 4-Jan-19

I found this course terribly bad and was the worst experience I had in Coursera. The instructor, rushes through few minutes of video explanations and then it is basically a learn everything by yourself by reading the panda documentation. If I wanted to learn things by myself reading the panda documentation, I wouldn't have taken the course! Like someone else mentioned, I registered the course to learn things systematically and save time!The assignments require us to do things in panda which where never explained in the lectures, nor it was explained how to do them efficiently. I managed to do everything through hours of research, but I'm pretty sure there are better, more efficient ways to code the assignments but these are not taught, the assignments provide no feedback and the forum too hard and time consuming to search for those answers!So what I learned was that I could either spend hours and hours cleaning the data store in Excel files in panda or few couple of minutes directly within Excel. Because the course is mainly a do everything yourself, it totally failed to show me the power of panda as a powerful tool for data science.

By Xiaoke L on 19-Mar-19

I have to say, the quality of this course is significantly inferior than the previous "Programming to everybody" . Firstly, it has something to do with the language that the lecturer uses to explain the concepts in Python. The language he uses is unnecessarily complicated. The sentences are very long and the words he uses are vague . If you want to explain a relatively complex concept, you need to use simpler and more comprehensible words. You cannot use a complex concept to explain another complex concept. The second thing is about the structure of the course, the insufficient engagement. The lecture is full of contents but only with very less interaction exercises. This will make students lost in the half way. I have to say this is not a pleasant experience even if I'm already very familiar to some other programming languages such as R and C++.

By Ommar H on 4-Jan-19

I would not recommend this course at all. I cancelled after week 2. It is framed as an intro to data science but the teacher often packs too much information into each video without taking time to properly explain underlying concepts sufficiently. The exercises often have issues in datasets that are not linked to current lesson which makes it confusing to follow along and you can spend hours searching forums to find solutions. In addition, the course does not give sufficient insight in videos to help you with questions. You have to search stack overflow and other sources to work out answers. Whilst this is reflective of real life, for an intro course which is how this was framed, it is difficult for someone new to data science to master a concept whilst trying to solve for other non related issues searching the web.

By Lingjun L on 11-Jul-19

Excellentmaterial. Admittedly I can see why there are so many negative reviews about theambiguity of the assessed tasks. It won't be an easy course for anyone who isunfamiliar with programming. However, if you do have programmingexperience under your belt, you'll likely find this course strikes anexcellent balance in terms of conciseness, practice, and theory. Eachlecture is crafted carefully to teach you about some nuance of pandas or numpy,and the programming assignments are packed with coding questions that will helpyou revise what you have learned, in a very efficient way. There is very little"fluff" in this course, which is a major weakness I've seen insimilar courses of its kind. Too much spoon feeding often does not challenge orengage the learner. The course is very direct about what it expects of itsstudents. Every week there is a comment "This week's assignment requiresmore self-learning than the last". And true to its word, there is less andless hand-holding as you go further into the course. I thoroughly enjoyed thematerial and probably learned the most out of this course than any other courseI've taken on Coursera, taking in to account its length.