Introduction to Data Science

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7/10 stars
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Coursera online courses
Coursera's online classes are designed to help students achieve mastery over course material. Some of the best professors in the world - like neurobiology professor and author Peggy Mason from the University of Chicago, and computer science professor and Folding@Home director Vijay Pande - will supplement your knowledge through video lectures. They will also provide challenging assessments, interactive exercises during each lesson, and the opportunity to use a mobile app to keep up with yo...
Coursera's online classes are designed to help students achieve mastery over course material. Some of the best professors in the world - like neurobiology professor and author Peggy Mason from the University of Chicago, and computer science professor and Folding@Home director Vijay Pande - will supplement your knowledge through video lectures. They will also provide challenging assessments, interactive exercises during each lesson, and the opportunity to use a mobile app to keep up with your coursework. Coursera also partners with the US State Department to create “learning hubs” around the world. Students can get internet access, take courses, and participate in weekly in-person study groups to make learning even more collaborative. Begin your journey into the mysteries of the human brain by taking courses in neuroscience. Learn how to navigate the data infrastructures that multinational corporations use when you discover the world of data analysis. Follow one of Coursera’s “Skill Tracks”. Or try any one of its more than 560 available courses to help you achieve your academic and professional goals.

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Humanities
Sciences & Technology
4723 reviews

Course Description

Join the data revolution. Companies are searching for data scientists. This specialized field demands multiple skills not easy to obtain through conventional curricula. Introduce yourself to the basics of data science and leave armed with practical experience extracting value from big data. #uwdatasci
Introduction to Data Science course image
Reviews 7/10 stars
42 Reviews for Introduction to Data Science

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Esteban Afonso profile image
Esteban Afonso profile image
8/10 starsCompleted
  • 4 reviews
  • 4 completed
6 years, 4 months ago
I will start with the cons then get to the pros: This lectures are rough, even painful at times. It often seemed that Dr. Howe put minimal effort into preparing the videos, he regularly caught errors in his slides and often seemed as though he was thinking about what to say for the first time. He frequently attempted to cover too much in a hurry. I especially found the lectures on NoSQL, statistics, and machine learning difficult to follow (despite a background in statistics and machine learning). Also, if you only do the required assignments, the workload is very unbalanced. The first assignment is very challenging and time consuming. The second is easier. And the rest of the assignments require little time commitment. Now this can be okay, because there were optional assignments you could use to fill in this extra time. Pros: The class does expose you to the main topics of data science. You definitely do not come out an expert in t... I will start with the cons then get to the pros: This lectures are rough, even painful at times. It often seemed that Dr. Howe put minimal effort into preparing the videos, he regularly caught errors in his slides and often seemed as though he was thinking about what to say for the first time. He frequently attempted to cover too much in a hurry. I especially found the lectures on NoSQL, statistics, and machine learning difficult to follow (despite a background in statistics and machine learning). Also, if you only do the required assignments, the workload is very unbalanced. The first assignment is very challenging and time consuming. The second is easier. And the rest of the assignments require little time commitment. Now this can be okay, because there were optional assignments you could use to fill in this extra time. Pros: The class does expose you to the main topics of data science. You definitely do not come out an expert in these topics. But you at least gain familiarity with them. The assignments were pretty much all valuable, the most so probably being the Kaggle assigment. Kaggle is where many a data scientist honed their chops. This class gets you started competing in Kaggle competitions. If you really want to learn how to do data science at home, spend time competing at Kaggle. The forums are great. I personally benefited enormously by two threads, one by the President of Kaggle and another by a Kaggle champion, in which they answered any data science questions the students had for them. Very knowledgeable and willing to help. Thank you to them. Overall: If you are interested in data science, take the course. Despite its shortcomings, you will find value in it. Additionally, you got the sense that one of the main reasons for the issues of the course was that it was rushed to get out. I imagine in future iterations, these wrinkles will begin to get ironed out. Don't expect to become an expert in data science from this course, but do expect to have a better feel for what the field of data science is, and some direction as you embark on becoming a data science expert.
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10/10 starsTaking Now
3 years ago
Excellent. So far the best course in data science in MOOCs. Prof. Bill Howe has a great knowledge of his subject and communicates very nicely. It is a course that will clear your basics and make a strong foundation for data science.
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2/10 starsTaking Now
3 years, 2 months ago
I am currently thinking of dropping this course and be happy with the money lost in enrolling The course on "communicating data science results" has a good week 1, but ... In order to complete week 1 assignment you need to already know Tableau or similar and currently there are not enough people enrolled to get scored and score, so will never pass Week 2 and 3 teacher sound not very interested and was just reading the slide, no effort to make it sound interesting. To add injury to the insult the last assignment apper to me to have out of dates instruction making impossible to complete if you do not have previous knowledge of teh cloude computing tool used and know how to twick the instruction. A little disappointed as had previous courses with Coursera and were excellent, but this one to me sound like a complete waste of time and money.
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Oberst Waschbär profile image
Oberst Waschbär profile image
8/10 starsCompleted
  • 1 review
  • 1 completed
3 years, 2 months ago
This feels like taking a live class without the ability to ask questions during the lecture. The course does not coddle you and hold your hand through ever hurdle you'll encounter and it shouldn't. If you want a breezy course you can put a half effort in, just read some articles.
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10/10 starsTaking Now
3 years, 9 months ago
This feels like taking a live class without the ability to ask questions during the lecture. The course does not coddle you and hold your hand through ever hurdle you'll encounter and it shouldn't. If you want a breezy course you can put a half effort in, just read some articles.
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Kristina Šekrst profile image
Kristina Šekrst profile image
8/10 starsCompleted
  • 102 reviews
  • 102 completed
4 years, 1 month ago
This was my first Coursera course, so I'm a bit nostalgic and emotional about it. However, I did enjoy it quite a lot. It was a nice blend of theoretical models and practice, but it should say that it requires previous experience in Python and SQL, otherwise it could be tough for a beginner. The Tableau visualization was fun, but other assignments could be improved by adding tutorials and more time to grasp these concepts. Kaggle competition was a huge deal, and it should stay mandatory. I'd recommend this course if you want a nice introduction as a programmer. Even though people disliked the teaching style, I was quite fond of the humor.
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2/10 starsDropped
4 years, 1 month ago
Terrible class and poorly advertised. Would give 0 stars if I could. And don't trust the refund policy. Watch the first video and take the practice, apparently you get a certificate and no refunds allowed after that. Disappointing since the other classes from the same university partnerships were excellent.
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Kristina Šekrst profile image
Kristina Šekrst profile image
7/10 starsCompleted
  • 5 reviews
  • 5 completed
4 years, 10 months ago
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David Asboth profile image
David Asboth profile image
8/10 starsCompleted
  • 1 review
  • 1 completed
4 years, 10 months ago
The breadth of topics covered was impressive, and I liked how some of the assessments were optional, otherwise it would have been too demanding within the timescale. Basically after completing the bare minimum, the more time you put into it the more you get out of the course and you can pick and choose some of the topics to look into further. The pre-requisites should have been explained in greater detail, as I was missing some of the statistics knowledge that was assumed by the instructor in some videos. I particularly liked the assignment where you participated in a Kaggle competition, it gave the course a more 'real world' feel.
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Paulo Almeida profile image
Paulo Almeida profile image
10/10 starsCompleted
  • 2 reviews
  • 2 completed
4 years, 11 months ago
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Jeff Winchell profile image
Jeff Winchell profile image
1/10 starsCompleted
  • 91 reviews
  • 66 completed
4 years, 9 months ago
This class should be called: "Introduction to my disorganized world of Data Science and the hidden grading system" This instructor should be ashamed of himself. It is one thing to have a lot of mistakes in the first iteration of a course (assuming he has never taught this before, outside of a MOOC). But I'm taking this course the second time and there are still major mistakes in it. Although the professor is from a world class university in Seattle, a city with the highest reputation for customer service anywhere, this professor shows disdain for customer service. Poor descriptions that make an assignment IMPOSSIBLE to fulfill are corrected by a TA, buried in some thread, but NEVER corrected in the assignment description, even after a week. Other missing information stays missing for months. The "real world" assignment was using this "not ready for primetime" website Coursolv which mostly seemed to be projects looking for people to d... This class should be called: "Introduction to my disorganized world of Data Science and the hidden grading system" This instructor should be ashamed of himself. It is one thing to have a lot of mistakes in the first iteration of a course (assuming he has never taught this before, outside of a MOOC). But I'm taking this course the second time and there are still major mistakes in it. Although the professor is from a world class university in Seattle, a city with the highest reputation for customer service anywhere, this professor shows disdain for customer service. Poor descriptions that make an assignment IMPOSSIBLE to fulfill are corrected by a TA, buried in some thread, but NEVER corrected in the assignment description, even after a week. Other missing information stays missing for months. The "real world" assignment was using this "not ready for primetime" website Coursolv which mostly seemed to be projects looking for people to do grunt work (nothing remotely resembling data science) for free. The big data assignment (using TB size data) seemed to require that you like Linux in order to figure out how to do it. They even warned you that you may not get through the setup/installation if you use Windows. What arrogance. 90% of the world is using Windows, even if this professor apparently dislikes it. The peer review assignments gave no indication (or gave contrary indications) on what rules people would use to assess you, so you could waste your time doing things that actually took points away from you. I got a 56% on one peer assessment, which is FAR lower than any peer assessment I've ever had in the 50+ MOOCs I've taken. The only 2 things I got out of this MOOC was realizing what I've been doing for decades was called "data science" (so I have a title "upgrade") and by having a mandatory assignment using kaggle.com I finally got myself to try out kaggles (which are cool, but you don't need this MOOC to find out... just go there and try them). If you can find another way to learn this material, do so. If you are a true hacker and like trying to solve the unsolvable, there will likely be good students to learn from. Much better experience than trying to learn from this instructor who cares far less about teaching than many of the students do.
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Greg Hamel profile image
Greg Hamel profile image
6/10 starsCompleted
  • 116 reviews
  • 107 completed
5 years, 2 months ago
Introduction to Data Science is misleading title for this course because it is not introductory level and it does not have a sensible flow that builds from one week to the next as you would expect from an intro course. Instead, the course acts as more of a data science sampler that introduces new topics each week that often have little to do with material covered in previous weeks. Lecture topics include relational databases, relational algebra, SQL, MapReduce, No SQL, miscellaneous topics in statistics, machine learning, visualization and graph analytics. If that sounds like a disjointed smorgasbord of topics, it is. To make matters even more complicated, the programming assignments use three different languages: Python, R and SQL. This course is best suited for those who have some exposure to Python, R, SQL and statistics. If you have the appropriate background knowledge, this course touches on many interesting topics... Introduction to Data Science is misleading title for this course because it is not introductory level and it does not have a sensible flow that builds from one week to the next as you would expect from an intro course. Instead, the course acts as more of a data science sampler that introduces new topics each week that often have little to do with material covered in previous weeks. Lecture topics include relational databases, relational algebra, SQL, MapReduce, No SQL, miscellaneous topics in statistics, machine learning, visualization and graph analytics. If that sounds like a disjointed smorgasbord of topics, it is. To make matters even more complicated, the programming assignments use three different languages: Python, R and SQL. This course is best suited for those who have some exposure to Python, R, SQL and statistics. If you have the appropriate background knowledge, this course touches on many interesting topics and while the lecturer's delivery is not great, he is quite knowledgeable and the material usually isn't too hard to grasp. Although the homework assignments require different languages and may take you a while to complete, they are rewarding. For instance, you'll work with real Twitter data you capture from the net, implement MapReduce operations in Python and participate in a machine learning competition on Kaggle.com. Introduction to data science is likely to be frustrating to those expecting a general intro to data science. The course jumps around too much and uses too many different tools to be a good first course in data science, but the breadth of topics covered and programming assignments make this course worth a look if you already have some exposure to data science or the tools the course uses. If nothing else, you can skip through the lectures and watch sections that are of particular interest to you.
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Ramiro Aznar profile image
Ramiro Aznar profile image
2/10 starsCompleted
  • 27 reviews
  • 26 completed
4 years, 4 months ago
Before I took this course I had enrolled to some similar ones such as Computing for Data Analysis, Learn to Program: The Fundamentals and Getting and Cleaning Data at Coursera. All of these were well organized, easy to catch up and with very good teachers. On the contrary, Intro to Data Science lacks all of these attributes. It is a course with too many and too long video lectures, which are badly build up and (sorry) very poorly explained by the professor. In addition, there is little connection between the lectures and the projects. The former is basicaly theoretical stuff and the later is very practical. To summarize, if you do not have a degree in Computer Science or a lot of experience programming do not enroll this course. And if you do, I am sure you will get bored during the lectures. Bad course.
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Mark Butler profile image
Mark Butler profile image
10/10 starsCompleted
  • 3 reviews
  • 3 completed
5 years, 2 months ago
This is an excellent course, very clear presentation, very experienced lecturer. You will need knowledge of basic programming (Python). Some knowledge of R and SQL might help too.
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Francois Fernando profile image
Francois Fernando profile image
7/10 starsCompleted
  • 5 reviews
  • 4 completed
4 years, 10 months ago
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Richard Taylor profile image
Richard Taylor profile image
10/10 starsCompleted
  • 29 reviews
  • 28 completed
5 years, 3 months ago
Eclectic, fascinating, full of mistakes and mysteries. This course is not for the weak. First of all I would like to point out that you need to have experience in Python, SQL and programming in general otherwise this course will be just too difficult to complete. Each week there's a new topic and new assignments, totally different and all of them quite fun. Including sentiment analysis of twitter, matrix multiplication in SQL, Map-Reduce demonstration in Python, Writing and running Pig scripts in the Amazon AWS cluster, a Kaggle competition for machine learning and a visualization using Tableau. The assignments appear randomly, the instructors don't answer questions, the course is full of mistakes, glitches, missing links and many quirks that can be solved via some team work in the forums. It would be a mistake not to give five stars to this course because of those glitches and errors, they are just like life itself. The amount of th... Eclectic, fascinating, full of mistakes and mysteries. This course is not for the weak. First of all I would like to point out that you need to have experience in Python, SQL and programming in general otherwise this course will be just too difficult to complete. Each week there's a new topic and new assignments, totally different and all of them quite fun. Including sentiment analysis of twitter, matrix multiplication in SQL, Map-Reduce demonstration in Python, Writing and running Pig scripts in the Amazon AWS cluster, a Kaggle competition for machine learning and a visualization using Tableau. The assignments appear randomly, the instructors don't answer questions, the course is full of mistakes, glitches, missing links and many quirks that can be solved via some team work in the forums. It would be a mistake not to give five stars to this course because of those glitches and errors, they are just like life itself. The amount of things the course covers, the fun assignments and the very interesting topics makes this course really wonderful and that's why I'm giving it full marks, you will learn a lot, you will suffer some and you will have a lot of fun.
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Student

1/10 starsDropped
5 years, 3 months ago
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Hamideh Iraj profile image
Hamideh Iraj profile image
6/10 starsCompleted
  • 70 reviews
  • 60 completed
5 years, 7 months ago
It was a course with good syllabus but not well taught. The instructor was explaining too hard, unable to make it easier and also too fast so that he himself could not keep up. Only a phd can fully understand what he says. only MAP REDUCE introduction was good. Generally this course is wide not deep and I think it is not worth the time you dedicate on the whole course. you may download and use it by subject. It might help in this way.
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Gavin Conran profile image
Gavin Conran profile image
9/10 starsCompleted
  • 25 reviews
  • 25 completed
6 years, 2 months ago
If you are familiar with functional, object oriented and parallel programming and if you are familiar with linear algebra, relational algebra and SQL and if you are familiar with machine learning: you will do well in this course else: you will struggle I thoroughly enjoyed the class but my only issue was the title (introduction to) as you really need a firm grounding in mathematics and programming to become a Data Scientist. Title suggestion: Becoming a Data Scientist.
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2/10 starsCompleted
6 years, 4 months ago
Rambling, error-filled lectures that really don't explain the material. You'll be left to figure it out on your own or by posting questions to the online forums. Instructor doesn't assume you know statistics, Python, and SQL, but you really do need to know them because the lectures are so poor. My girlfriend has a PhD and knows (and uses) statistics and agrees that he's a poor instructor. Not worth the trouble unless you already know most of the material, but then why bother with this course? Worst course I've ever taken on Coursera. Coursera should be embarrassed by this offering. The others were far better.
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colin mcdermott profile image
colin mcdermott profile image
9/10 starsCompleted
  • 2 reviews
  • 2 completed
6 years, 1 month ago
Fantastic course, but there are some week points: Good: Bill Howe is knowledgeable and his insight is worth taking the course just for this alone. I understood more about database theory then ever before. I got into the internals of Big data and I loved it. Very flexible course and assignment structure. If you have a Big data project and want to run with it as a part of the course you can do this! Bad: The assignment workload is crazy. I found myself spending hours just getting my python programs to compile. However once done I found my programming skills were pushed to the limit (amatuer C++ programmer delving into Python). Allot of breath, with a fair bit of depth. This is not an easy course. Cecillia Aragon's lectures on visualisation are fantastic too. I loved them and the content within. Great course, Tough but well worth doing.
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9/10 starsCompleted
6 years, 3 months ago
The workload was uneven, and there were a variety of logistical issues, but the lectures covered an enormous amount of ground and some of the assignments were incredibly valuable. This is one of the more valuable courses I've ever taken in terms of information content and practicality.
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3/10 starsCompleted
6 years, 4 months ago
Poor communication from Professor and staff. Numerous technical issues, lectures appeared unprepared or practiced (long pauses with NOTHING happening...just the professor staring), cell phones constantly going off during lectures, assignment details changed mid-assignment due to issues. I think this course can be made great, but this first iteration was not it.
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ashish bhutani profile image
ashish bhutani profile image
9/10 starsCompleted
  • 2 reviews
  • 2 completed
6 years, 4 months ago
# Very nice course for beginning in this area. # Introduces you to map-reduce, AWS, ML libraries in python and R and basic info about ML algos. Also, tableau. # Introduces you to Kaggle by including a mandatory assignment of participating in an open competition. # Discussion forum is very active with AMA threads by experienced people on Kaggle. I agree that it's not very exhaustive and assignments are easy compared to other courses. But that's what the title says, it is an "INTRODUCTION to data science". One can build upon ideas learnt from here.
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David Fountain profile image
David Fountain profile image
8/10 starsCompleted
  • 2 reviews
  • 2 completed
6 years, 1 month ago
It had been a while since I actively programmed (in C, Java, and SQL). Other reviewers have mentioned the difficult nature of the content of the course; however, that was one of my favourite parts. Of a number of the courses I have taken, I have probably learnt the most from this one and would welcome another from this instructor.
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Nacho Castejón profile image
Nacho Castejón profile image
8/10 starsCompleted
  • 2 reviews
  • 2 completed
6 years, 3 months ago
As an introduction to the topic, I think the course works great. It covers an amazing number of topics, so it's really just skimming through it, but serves to know "what's out there" and start reseaching in the topic you are most interested on. I took the course as a Computer Science graduate (10 years out of college), so I was very familiar with some of the material. However, I found interesting info in almost al lectures, and a couple of illuminating segments.
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Marjan Ček profile image
Marjan Ček profile image
7/10 starsCompleted
  • 20 reviews
  • 17 completed
6 years, 3 months ago
The course was very disorganized, without clear passing conditions, and suffered changes along the way. Yet the content was very good as an introduction to data science, and there where some really interesting assignments such as matrix multiplication in SQL, or the Kaggle competitions. Overall, I think I took a lot of good things from the course, and I recommend it to anyone interested in the topic, provided we have enough self motivation to overcome the course's shortcomings. Hopefully any future iteration of the course will be improved.
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Gordon Haff profile image
Gordon Haff profile image
6/10 starsCompleted
  • 6 reviews
  • 5 completed
6 years, 3 months ago
I generally concur with many of the other reviews. For me, I still come down on the pro side. However, I had some degree of familiarity with the majority of the topics covered in the course--including a fair bit of programming experience--so I didn't have any trouble setting up the various environments required for the assignments. The only specific point that I found especially frustrating was auto-grader issues for the first assignment--which I assume will be corrected in future iterations. For me personally, I found the breadth of topics welcome as this gave me new insights into a number of different areas, a number of which I just had cursory knowledge of. That said, I can understand the frustration of many. The course was certainly a bit rough in terms of both content of individual lectures/assignments and overall flow. In several ways, it felt as if it petered out towards the end. The Tableau assignment, in particular, just see... I generally concur with many of the other reviews. For me, I still come down on the pro side. However, I had some degree of familiarity with the majority of the topics covered in the course--including a fair bit of programming experience--so I didn't have any trouble setting up the various environments required for the assignments. The only specific point that I found especially frustrating was auto-grader issues for the first assignment--which I assume will be corrected in future iterations. For me personally, I found the breadth of topics welcome as this gave me new insights into a number of different areas, a number of which I just had cursory knowledge of. That said, I can understand the frustration of many. The course was certainly a bit rough in terms of both content of individual lectures/assignments and overall flow. In several ways, it felt as if it petered out towards the end. The Tableau assignment, in particular, just seemed ill-conceived as it required proprietary Windows software. Overall, I think there's the core of a much better course here, albeit one that probably requires a better grounding in programming etc. than is obvious from the prereqs.
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6/10 starsCompleted
6 years, 4 months ago
This course started impressively, especially if you weren't familiar with Python and JSON. But much of the second half appeared random and made up as it went along, as if the lecturer seemed to lose interest. The "guest" lectures were not much more than advertorials and the final assignment using Tableau software also had a "brought to you by our sponsor" feel - was this an attempt to develop a Coursera revenue model? On the positive side, the discussion forums were very active and helpful. Numerous technical issues with assignment submission were frustrating but this can probably be be put down to the course being in beta mode and would probably be more streamlined if the course is run again. The programming assignments (Python, SQL, Mapreduce, Pig / AWS) were not particularly difficult in themselves but configuring environments to run them definitely provided a sense of achievement.
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4/10 starsCompleted
6 years, 4 months ago
Pros Interesting subject matter. Breadth of different topics (statistics, programming, machine learning, etc.). Active discussion board with knowledgeable people. Cons The overall course sort of felt piecemealed together. No natural progression from one subject to the next. Assignments not always related to anything learned. As others have mentioned, there were numerous problems, bugs with the assignments. Long pauses in lectures, constant cell phones going off, and numerous mistakes caught on-screen by the professor during the lecture. Overall Wait for improvements or learn the material elsewhere.
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