Introduction to Analytics Modeling

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8/10 stars
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edX online courses
Harvard University, the Massachusetts Institute of Technology, and the University of California, Berkeley, are just some of the schools that you have at your fingertips with edX. Through massive open online courses (MOOCs) from the world's best universities, you can develop your knowledge in literature, math, history, food and nutrition, and more. These online classes are taught by highly-regarded experts in the field. If you take a class on computer science through Harvard, you may be tau...
Harvard University, the Massachusetts Institute of Technology, and the University of California, Berkeley, are just some of the schools that you have at your fingertips with edX. Through massive open online courses (MOOCs) from the world's best universities, you can develop your knowledge in literature, math, history, food and nutrition, and more. These online classes are taught by highly-regarded experts in the field. If you take a class on computer science through Harvard, you may be taught by David J. Malan, a senior lecturer on computer science at Harvard University for the School of Engineering and Applied Sciences. But there's not just one professor - you have access to the entire teaching staff, allowing you to receive feedback on assignments straight from the experts. Pursue a Verified Certificate to document your achievements and use your coursework for job and school applications, promotions, and more. EdX also works with top universities to conduct research, allowing them to learn more about learning. Using their findings, edX is able to provide students with the best and most effective courses, constantly enhancing the student experience.

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Sciences & Technology
Business & Management
22549 reviews

Course Description

Please note that the verified certificate option for this course is limited to 250 learners. The verified certificate option will close when this limit is reached.

Analytical models are key to understanding data, generating predictions, and making business decisions. Without models it’s nearly impossible to gain insights from data. In modeling, it’s essential to understand how to choose the right data sets, algorithms, techniques and formats to solve a particular business problem.  

In this course, part of the Analytics: Essential Tools and Methods MicroMasters program, you’ll gain an intuitive understanding of fundamental models and methods of analytics and practice how to implement them using common industry tools like R.

You’ll learn about analytics modeling and how to choose the right approach from among the wide range of options in your toolbox.

You will learn how to use statistical models and machine learn...

Please note that the verified certificate option for this course is limited to 250 learners. The verified certificate option will close when this limit is reached.

Analytical models are key to understanding data, generating predictions, and making business decisions. Without models it’s nearly impossible to gain insights from data. In modeling, it’s essential to understand how to choose the right data sets, algorithms, techniques and formats to solve a particular business problem.  

In this course, part of the Analytics: Essential Tools and Methods MicroMasters program, you’ll gain an intuitive understanding of fundamental models and methods of analytics and practice how to implement them using common industry tools like R.

You’ll learn about analytics modeling and how to choose the right approach from among the wide range of options in your toolbox.

You will learn how to use statistical models and machine learning as well as models for:

  • classification;
  • clustering;
  • change detection;
  • data smoothing;
  • validation;
  • prediction;
  • optimization;
  • experimentation;
  • decision making.
Introduction to Analytics Modeling course image
Reviews 8/10 stars
26 Reviews for Introduction to Analytics Modeling

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1/10 starsTaking Now
1 year, 10 months ago
Professional program is much better than this one. Be careful. Please do it again, Georgia Tech! Thank you
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1/10 starsTaking Now
1 year, 11 months ago
course is good, however nothing is available to audit people except for the homeworks. The lectures are a little too generic and not very deep. It's like a survey of most generic modeling methods.
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1/10 starsDropped
1 year, 12 months ago
Sad course that breaks their initial promise of providing exams to audit learners in the middle of the course. People who did homework faithfully are turned away in the middle of the course. If you're audit learners, all GT courses are not worth it. They don't give you good materials.
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1/10 starsDropped
2 years ago
Course no longer provides exams for audit learners. The frustrating thing is this is not made known upfront. The syllabus says that audit learners can take exams without proctoring. Then when the exam comes nobody tells audit learners that they don't provide exams anymore.
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1/10 starsDropped
2 years ago
It's just sad that the course no longer provides assessment for audit learners. You may as well put this as "Professional Education" For better experience, I suggest "The Analytics Edge" by MIT
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Yeok C profile image
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Yeok C

10/10 starsCompleted
2 years, 1 month ago
I agree with the other reviewers that this is an excellent course and most pointers have already been covered e.g. recommend basic knowledge of R programming prior to starting course, be prepared to spend more than 10 hours per week especially in the first few weeks. The content of the course is very comprehensive and I am rather surprised at the amount of topics they managed to 'squeeze' within the 10 weeks. It's not going to be a breeze, but the hard work is well worth it.While the course is excellent, I will not recommend this course to people without any IT or programming background. This is because you will need to dive right into programming in the first week homework and the R programming know-hows are not covered in the lecture videos. Pros: 1) Lecture contents are of high quality, straight-to-the-point and cover the most important points. 2) Homeworks are formulated in a way that require us to do our own research in addi... I agree with the other reviewers that this is an excellent course and most pointers have already been covered e.g. recommend basic knowledge of R programming prior to starting course, be prepared to spend more than 10 hours per week especially in the first few weeks. The content of the course is very comprehensive and I am rather surprised at the amount of topics they managed to 'squeeze' within the 10 weeks. It's not going to be a breeze, but the hard work is well worth it.While the course is excellent, I will not recommend this course to people without any IT or programming background. This is because you will need to dive right into programming in the first week homework and the R programming know-hows are not covered in the lecture videos. Pros: 1) Lecture contents are of high quality, straight-to-the-point and cover the most important points. 2) Homeworks are formulated in a way that require us to do our own research in addition to the lecture video topics. 3) Good support from Dr Sokol and his teaching assistants (for Verified students). They are always on the look out for students' questions and difficulties (priority on Verified students) . 4) Students community support is great and quite a number of students have quite strong analytics knowledge themselves and they are very helpful and have organised separate communication channels and resources to help fellow students. Cons: 1) Deadlines were somewhat confusing due to multiple factors e.g. different time zones stated in different parts of the edx platform. For me, I always target to finish the homework, quiz 2 days before the deadline, so as not to bother with the time-zone difference. 2) Exam proctering software: This software requires re-installation every time an exam/quiz is taken. I spent quite substantial time in 'fixing' this software every time I took a quiz, even though my laptop fully met the 'system requirements'. 3) Edx platform has a few bugs, which was not resolved till now. Although these are minor bugs, I find that Edx support should have dealt with them in a more responsive manner.
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Douglas Trent

10/10 starsCompleted
2 years, 2 months ago
Excellent intro to modeling and a solid foundation for analytics in general. The course provided a comprehensive survey of models: classification, time-series, regression, non-parametric, simulation, optimization, etc. with a focus on cross-cutting concepts like data wrangling and model validation. The class videos were thankfully short and focused. Much of the learning came from hands-on exploration and experimentation with the tools, just as in the real world. Google and StackOverflow were valuable assistant professors! Our TA's were also very helpful on the discussion boards and recitations. Our professor allowed a condensed self-prepared set of notes in the exams. Now I have a great set of cheat sheets for the workplace. Mine was the first class through this course so some startup issues were to be expected. The edX platflorm is a little challenging to use, especially its discussion boards and the proctoring software. But overall... Excellent intro to modeling and a solid foundation for analytics in general. The course provided a comprehensive survey of models: classification, time-series, regression, non-parametric, simulation, optimization, etc. with a focus on cross-cutting concepts like data wrangling and model validation. The class videos were thankfully short and focused. Much of the learning came from hands-on exploration and experimentation with the tools, just as in the real world. Google and StackOverflow were valuable assistant professors! Our TA's were also very helpful on the discussion boards and recitations. Our professor allowed a condensed self-prepared set of notes in the exams. Now I have a great set of cheat sheets for the workplace. Mine was the first class through this course so some startup issues were to be expected. The edX platflorm is a little challenging to use, especially its discussion boards and the proctoring software. But overall the course was a tremendous and satisfying experience. I'm looking forward to starting the full-blown OMS Analytics program in a few weeks. By the way, don't even think of taking the class without a solid background in R. Some Python would be useful too.
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10/10 starsCompleted
  • 0 reviews
  • 0 completed
2 years, 2 months ago
This course was everything I hoped it to be and more, totally mind-blowing introduction to real analytics modelling for a lifelong Excel jockey (ok, and Access and SQL, but you know the limits here). The most mentally challenging prolonged task I have ever taken on let alone completed. A lot to think about in a very condensed set of lecture material. I must have watched most of the videos several times each; there was 30 seconds out of lecture 2(?) on SVM would have worn out the tape if this had been a VCR. Maybe that says the material could have been more clearly elaborated but somehow I found it inspiring and just worked my way through it until the understanding came. As several reviewers have said, it's best if you come into this with a basic course in R under your belt, that said the first 3 week first time immersion in R seemed to bond a lot of us together, first on the forums and then on slack (shout out to Ben L. and the r... This course was everything I hoped it to be and more, totally mind-blowing introduction to real analytics modelling for a lifelong Excel jockey (ok, and Access and SQL, but you know the limits here). The most mentally challenging prolonged task I have ever taken on let alone completed. A lot to think about in a very condensed set of lecture material. I must have watched most of the videos several times each; there was 30 seconds out of lecture 2(?) on SVM would have worn out the tape if this had been a VCR. Maybe that says the material could have been more clearly elaborated but somehow I found it inspiring and just worked my way through it until the understanding came. As several reviewers have said, it's best if you come into this with a basic course in R under your belt, that said the first 3 week first time immersion in R seemed to bond a lot of us together, first on the forums and then on slack (shout out to Ben L. and the rest, many of whom have posted here). Also take a Stats refresher if you're rusty! Yes, there were some flaws in lecture material but for me picking up on those proved I was learning. The only weakness I would say is that sometimes the advice from TAs was found wanting. Dr Sokol was personally engaged on the edx discussion boards, as much as you could hope a program head could be, and assisted many times when his advice was needed. I hope this course and Dr Sokol are typical of what can be expected for the GT's online MSA, 'cos that's where I'm headed next.
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10/10 starsCompleted
  • 1 review
  • 1 completed
2 years, 2 months ago
I have completed this class and it was a great learning experience. The short videos and knowledge check are an excellent way to keep the student occupied. Moreover, last 3 weeks homework and course project provide a big-picture view of what analytics is and how one can apply it in the real world. One thing I can suggest to improve the class is to add R refresher lecture/recitation videos before first-week homework. In that way, the student will not get overwhelmed in the first week- especially the student with little to no R knowledge. Edx delivery method was good for this course. However, Edx discussion forum should be improved. It's very hard to keep track of discussion in its current form.
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N. Sharma profile image
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N. Sharma

10/10 starsCompleted
2 years, 2 months ago
Overall, I would highly recommend this class to anybody who works in an analytical role or is interested in analytics. The class serves as the perfect intro to a wide range of analytical topics. While the class doesn't deep dive into too many of the topics, I think the course content is great at allowing learners to learn a little bit about a lot of content. This allows learners to decide for themselves what they feel is necessary for them and what to focus on in later classes, or if a learner decides to apply for the GT OMS in Analytics degree, which track to pursue. More specifically about the class, this is not just a class you can take leisurely. I think the 5-10 hours per week is pretty accurate however more often it leans towards the 10. Without a programming background, with a focus on R, I could see this estimate being a bit light as well. However, the coding is almost exclusively needed for the homework assignments. The ex... Overall, I would highly recommend this class to anybody who works in an analytical role or is interested in analytics. The class serves as the perfect intro to a wide range of analytical topics. While the class doesn't deep dive into too many of the topics, I think the course content is great at allowing learners to learn a little bit about a lot of content. This allows learners to decide for themselves what they feel is necessary for them and what to focus on in later classes, or if a learner decides to apply for the GT OMS in Analytics degree, which track to pursue. More specifically about the class, this is not just a class you can take leisurely. I think the 5-10 hours per week is pretty accurate however more often it leans towards the 10. Without a programming background, with a focus on R, I could see this estimate being a bit light as well. However, the coding is almost exclusively needed for the homework assignments. The exams are focused more on theory. About edX, if you haven't taken an edX course before make sure to really familiarize yourself with the website. In particular give yourself plenty of time for the exams as the remote proctor software can be quite finicky. Also carefully note due dates as the edX time stamps were confusing to some learners. However, there is always at least a week to complete an assignment. Also there was an extremely active discussion board so if you are uncertain about something you will get a quick reply anyway. All in all, the class was a great experience. I will continue to recommend the class to my peers and the quality of the class is exactly what I continue to expect from Georgia Tech courses: difficult and challenging, but incredibly rewarding.
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Yuce B.

10/10 starsCompleted
2 years, 2 months ago
This was a great class, incredibly good considering the delivery is online. I learned a lot. I highly recommend this course to anyone who would like to dive into analytics, it is a great class. Office hours and recitation hours given by TAs made a big difference in learning, and eased the difficulty of completing some assignments. One of the students created a slack group for the entire class and that made it easy to exchange ideas and discuss issues, and connect socially.
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H. LAM

10/10 starsCompleted
2 years, 2 months ago
Pros. - Excellent lecturer and mentors support - Great course material with clear explanation - Proper balance between knowledge and practice Cons. - edX wrong setting of deadline (i missed the final project!) - definitely not an introductory course - the most expensive one in edX MicroMaster series
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10/10 starsCompleted
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  • 0 completed
2 years, 2 months ago
This was a wonderful introduction to a broad array of analytical models, concepts, and tools. I learned a ton in this rigorous course. I would highly recommend it to anyone interested in an introduction to data analysis methodology and ideas. people with little programming experience will find the homework challenging. The first week or two in particular did have a pretty sharp learning curve -- students spent a lot of time and energy learning R -- but it flattened out and got a lot easier. But the three exams, the class project, and three of the ten homework assignments (collectively literally 90% of the course grade) are focused on concepts and broad applications, with no programming or math expertise necessary, making the subject matter approachable to ). That's not to say it's easy, because there is a ton of material. I definitely felt like I got a lot of value in that regard for my time and money. I found the professor... This was a wonderful introduction to a broad array of analytical models, concepts, and tools. I learned a ton in this rigorous course. I would highly recommend it to anyone interested in an introduction to data analysis methodology and ideas. people with little programming experience will find the homework challenging. The first week or two in particular did have a pretty sharp learning curve -- students spent a lot of time and energy learning R -- but it flattened out and got a lot easier. But the three exams, the class project, and three of the ten homework assignments (collectively literally 90% of the course grade) are focused on concepts and broad applications, with no programming or math expertise necessary, making the subject matter approachable to ). That's not to say it's easy, because there is a ton of material. I definitely felt like I got a lot of value in that regard for my time and money. I found the professor and TA's very helpful and friendly, particularly for an online course. It's clear they want both the class and its students to succeed.
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10/10 starsCompleted
2 years, 2 months ago
Let me start by saying that, this is the best thing available out there that can give you needed introduction into data modelling with hands on assignments and a lot of critical thinking. It can get high demanding if you don't have background in R but once you get a hang, it's so much fun. I will call it a mile wide and an inch deep, that's what introductory courses are for and this does a great job delivering it. It does cover theory behind different machine learning models, generalized data collection, scaling & missing concepts and a fine intro to R, Arena & Python. Most importantly, in second phase of the course it allows you to learn how to implement combination of all these to real world scenarios, that is priceless. If you don’t have much background in analytics, still this can be completed with ease, as professor, TA’s and discussion board is always your friend. It also provides enough for one to take it forward and start app... Let me start by saying that, this is the best thing available out there that can give you needed introduction into data modelling with hands on assignments and a lot of critical thinking. It can get high demanding if you don't have background in R but once you get a hang, it's so much fun. I will call it a mile wide and an inch deep, that's what introductory courses are for and this does a great job delivering it. It does cover theory behind different machine learning models, generalized data collection, scaling & missing concepts and a fine intro to R, Arena & Python. Most importantly, in second phase of the course it allows you to learn how to implement combination of all these to real world scenarios, that is priceless. If you don’t have much background in analytics, still this can be completed with ease, as professor, TA’s and discussion board is always your friend. It also provides enough for one to take it forward and start applying learning in their day-to-day job. I would highly encourage all looking to jump into world of data to go ahead and try this course. Kudos to GT team in putting this together, truly a remarkable job, thank you!
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Evan R

10/10 starsCompleted
2 years, 2 months ago
Initial Warning: Having had only limited exposure to R and a limited STEM background (i.e. no calculus/linear algebra), I found that the effort (hours per week) estimate was woefully underestimated. If you have limited R programming experience/STEM background, expect to spend roughly 2 times to 3 times the amount estimated on the course page. Weekly homeworks take GaTech's traditional approach of "figure it out on your own/with other students" which can become a time sink. With that said, the course is extremely rewarding. I have attempted to dip my toe in the "data science" pool for the last two years, but I was always discouraged by the sheer volume of knowledge/training I didn't have. This course gave a thorough introduction into the types of analytic techniques/philosophy that data analysts/scientists use to approach analytic/operational challenges. Moreover, Dr. Sokol and his teams of TAs both expanded "what I know" as well a... Initial Warning: Having had only limited exposure to R and a limited STEM background (i.e. no calculus/linear algebra), I found that the effort (hours per week) estimate was woefully underestimated. If you have limited R programming experience/STEM background, expect to spend roughly 2 times to 3 times the amount estimated on the course page. Weekly homeworks take GaTech's traditional approach of "figure it out on your own/with other students" which can become a time sink. With that said, the course is extremely rewarding. I have attempted to dip my toe in the "data science" pool for the last two years, but I was always discouraged by the sheer volume of knowledge/training I didn't have. This course gave a thorough introduction into the types of analytic techniques/philosophy that data analysts/scientists use to approach analytic/operational challenges. Moreover, Dr. Sokol and his teams of TAs both expanded "what I know" as well as "what I know I don't know." More simply, this course provided me with a firm foundation/entry point into data analytics and machine learning. Finally, a bit of advice. Stick it out until the final three weeks. The last three weeks were by far the most compelling/rewarding portion of the course.
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Nathan Oliphant

10/10 starsCompleted
2 years, 2 months ago
I recently completed the data science track in the Microsoft Professional Program, and while it taught me how to use various ML and analytics tools, I wanted a course that taught more of the fundamentals. This course is definitely that. I found it very challenging, but it you have taken the prerequisites, you should gain quite a bit from it. Good lectures, comprehensive quizzes, and challenging homework.
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Mrinalini Sharma

10/10 starsTaking Now
2 years, 2 months ago
Dr. Sokol and the TAs have put together a fabulous course to help aspiring data analysts join the industry. Dr. Sokol does a great job in making the concepts easy to understand and relatable to real life tasks. The TAs are extremely helpful is clarifying doubts and encourage deeper discussion on concepts. That being said, this is difficult course and the staff does not sugarcoat it. They do, however, help if you are putting in the effort. The course focuses heavily on learning and provides ample opportunity to do so. Consider taking a beginner level course in R before taking this up.
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Aaron S

10/10 starsTaking Now
2 years, 2 months ago
This is a course in "figure s**t out 101." And that's a good thing. You see, out there in the working world, that's what you'll have to be doing, day after day. This class will put you into that position with every homework assignment. It won't be easy, that's the point. You won't get a neat set of data and be asked to do a regression on it. That would be simple. Instead, you'll get a messy set of data and asked to clean it, possibly using 3 or 4 different imputation techniques to handle missing variables. THEN do an analysis, but that might just be question 1 of 4 for a given homework. Good luck. So, good course. Challenging if you have absolutely no experience with programming or analytics. In my case, I came in with many years' experience as an analyst, but little in the way of R experience. I had worked through 'R in Action' years ago and had a copy of 'Introduction to Statistical Learning' handy. Also made good use of th... This is a course in "figure s**t out 101." And that's a good thing. You see, out there in the working world, that's what you'll have to be doing, day after day. This class will put you into that position with every homework assignment. It won't be easy, that's the point. You won't get a neat set of data and be asked to do a regression on it. That would be simple. Instead, you'll get a messy set of data and asked to clean it, possibly using 3 or 4 different imputation techniques to handle missing variables. THEN do an analysis, but that might just be question 1 of 4 for a given homework. Good luck. So, good course. Challenging if you have absolutely no experience with programming or analytics. In my case, I came in with many years' experience as an analyst, but little in the way of R experience. I had worked through 'R in Action' years ago and had a copy of 'Introduction to Statistical Learning' handy. Also made good use of the Hadley Wickham book as well as the R cheat sheets provided by the good folks at R Studio. For me, given the above, some of the homework was quite time consuming, but, in the end, good experience, and exactly what I probably needed. Grading is mainly based on three exams (two mid-terms and a final) that are worth 25% of your grade each. The remaining 25% is comprised of homework (16%) and a final project (9%). Homework and project are peer-reviewed and we are told that grading should be lenient as these are intended to be a learning experience. If you are thinking that you'll just Google your way through these R exercises, think again. In most cases the problems are a bit more complex and require work on your part to complete. It was not unusual to spend 15 hours a week on these in the first several weeks. The exams themselves test to see whether or not you understand, conceptually, what the various models do (i.e., when might you use ARIMA, what does it do, etc.). Coding up a solution will not be part of the tests. And, for the tests, you are allowed a cheat sheet (both sides) of one page. For the final: two pages both sides. Interestingly, my cheat sheets really didn't help me that much. I found that for some questions, I simply knew the answer. For others, nothing in my cheat sheet would help anyways. In the end, this was a challenging course, pitched more to someone who already is pretty familiar with coding. If you have a programming background this may seem a bit easier for you than it was for me. On the other hand, if you haven't the slightest idea about programming and no experience with anything related to analytics, this may be a steep learning curve. As for me, I expect to end up in the 80's as far as % goes. I only have a chance to break 90% if I ace the final, which I plan to take in the next couple days. I have no idea what that will translate into as far as an on-campus grade goes. Or, indeed, if this will be counted as part of the degree if/when I am accepted into the OMSA program. If I have any complaint, it's that the nature of the relationship between these courses and the degree program isn't quite yet hashed out. I hope they don't change much in terms of the difficulty. I had to get through it, as did my classmates. I wouldn't want it to be watered down because of some cry babies who couldn't either figure stuff out or have the sense to drop it.
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Paul Babin

10/10 starsTaking Now
2 years, 2 months ago
Dr. Sokol has organized a nice online experience for a broad and complicated subject - providing easy to understand lectures, fair online exams, and very challenging homework assignments (with peer reviewed grading based on effort and learning). I recommend the "certified learner" approach, as the discussion board are segregated based on that "peer" status. Excellent course!
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student

10/10 starsTaking Now
2 years, 2 months ago
I am still busy with the course, but I have been so impressed by the top notch quality of the content, concept behind the course and the doors it is opening for me in the world of analytics , that i was compelled to write this review. It is not an easy course, it challenges you and really demands time to think and practice, practice practice.... loving it!
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Matthew Aeschbacher

10/10 starsTaking Now
2 years, 3 months ago
One sentence review: This is the best MOOC I have taken for understanding how to combine multiple analytics methods to produce models that solve challenging problems. I've completed dozens of MOOCs over the past couple of years, most of them related to math, computer science, and analytics. My review is biased by the fact that I enjoy rigorous, well-organized MOOCs that provide ample opportunity for learning-by-doing. This class has all of those components. At first the material seemed easy, but the learning quickly accelerates. If you don't have background with R Programming or basic data analysis you might feel like you are drowning the first couple of weeks. The homework forces you to engage with classmates and put what you've learned into practice. The last couple of weeks present case studies that reinforce the art of analytics modeling. This is a demanding course but you will be rewarded if you give it your full attention.
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Mark Davis

10/10 starsTaking Now
2 years, 3 months ago
I don't comment online often, but after seeing the shockingly low amount of total reviews and the weight given to the 2 bad reviews below, I feel I have to. I finished my undergrad (at GT) almost 20 years ago, so this course has been an absolute beast to tackle since I haven't taken any refresher courses in that time. However, despite this handicap, I've managed to not only survive, but am doing decently well. The amount of course material and homework required a lot of extra effort, but I feel that the struggle encouraged a greater depth of understanding. The lectures were presented in an organized and easy to understand manner, but the homework was quite difficult (for an R newbie), and the tests were tricky. If you lack the background, I'd suggest a stats refresher and an R crash course if you'd like to make your life easier. If not, I've managed to do without and supplement on the fly, but I'd advise against it if taking th... I don't comment online often, but after seeing the shockingly low amount of total reviews and the weight given to the 2 bad reviews below, I feel I have to. I finished my undergrad (at GT) almost 20 years ago, so this course has been an absolute beast to tackle since I haven't taken any refresher courses in that time. However, despite this handicap, I've managed to not only survive, but am doing decently well. The amount of course material and homework required a lot of extra effort, but I feel that the struggle encouraged a greater depth of understanding. The lectures were presented in an organized and easy to understand manner, but the homework was quite difficult (for an R newbie), and the tests were tricky. If you lack the background, I'd suggest a stats refresher and an R crash course if you'd like to make your life easier. If not, I've managed to do without and supplement on the fly, but I'd advise against it if taking this course while working full-time (as I am). The bottom line is that this is a difficult course, but well worth the struggle if you want a solid understanding of a good breadth of analytic models and their practical application (mostly in R). I'm glad I stuck with this course (2 coworkers quit in the first few weeks due to the heavy workload), as I've already learned enough to start applying this knowledge to real-world problems at my job. Geeky or not, I find this truly exciting!
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Sher Bhail

8/10 starsTaking Now
2 years, 3 months ago
I must show my gratitude to Prof. Sokol for putting together an excellent course. It does feel that this is one the 1st offering of this course in online format, but sincerely I believe that it is well prepared. Maybe it helps that I have been familiarizing my self with Machine learning concepts and that I was exposed to upper division statistics in UC system.
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Guy Gaskin

10/10 starsDropped
2 years, 3 months ago
This is the real deal folks. I unenrolled after I underprepared then timeboxed myself for the first quiz, and, I'm okay with it (for a few reasons - heh). I sympathize with the first two reviewers, but, you have to know what you're in for - first-run (Summer 2017) of a course, 1/12th of a Master's level degree in Analytics from a prestigious regional science/engineering/tech school. Trust that Professor Sokol actually wants you to learn a lot (first!), and might have some experience teaching this on-campus. Should already know, Profs have the final say to adjust gradelines to make things fair, despite it all. Mind the prerequisites. Find collaborators (do not engage the homeworks on your own, unless you are quite sure of yourself). Make the lecture material your own (the homework will not be enough, as with many other subjects) - recreate the lecture models, make flashcards, work ahead _aggressively_, anticipate quiz questions... This is the real deal folks. I unenrolled after I underprepared then timeboxed myself for the first quiz, and, I'm okay with it (for a few reasons - heh). I sympathize with the first two reviewers, but, you have to know what you're in for - first-run (Summer 2017) of a course, 1/12th of a Master's level degree in Analytics from a prestigious regional science/engineering/tech school. Trust that Professor Sokol actually wants you to learn a lot (first!), and might have some experience teaching this on-campus. Should already know, Profs have the final say to adjust gradelines to make things fair, despite it all. Mind the prerequisites. Find collaborators (do not engage the homeworks on your own, unless you are quite sure of yourself). Make the lecture material your own (the homework will not be enough, as with many other subjects) - recreate the lecture models, make flashcards, work ahead _aggressively_, anticipate quiz questions..the things you did to earn a grade you were proud of in any tough class when you knew you were in for a fight, from the first day. As for the homeworks - you have weeks to get your bearings (you did join the inter-student support slack, right?!). Lowest one of nine dropped, 16% of the grade - technically, if you just make a submission with your name, 50% (60% for Micromasters pass, exemption from Master's degree TBD...), nice try 75%, mostly right 90%, etc. (And, your peers/staff seem to give you the benefit of the doubt when you're in between because they knew the homework was tough!) So...don't get off balance... I felt that Quiz 1 was tough but fair (you will probably want to use _all_ of your time) - aside from the timing w/ HW4 (and..lacking..a warning like..this), I'm not sure how the grades would be more fair. (I haven't seen the project and final, though). When I joined the Webex TA session in Week 4, it seemed pretty comparable to an in-person TA session - quite capable TA waiting for questions, some good questions, more than..3/4 handled proficiently on the spot, some offline follow-ups, etc. Treat this course with respect and team up with your classmates - you will be rewarded. For having spent the same amount of time on this and Analytics Edge (before I dropped because I got slammed with homework grading..heh), this course forces you to get a command of what is presented in a way that Analytics Edge did not (not that AE it isn't a great course). The 300 seats should be sold out...excellent value. [Guy T Gaskin, guy@umn.edu, 4th year TA for IDSC 4431 by Gordon Everest at U of MN, Business Analyst @ Carlson School of Management, 3 years UMTYMP, 2 years PSEO skipping 2 years of high school, 20+ year IT career, 40+ certification exams, etc. - I like to think I know what a good class is] (11k auditors, 200+ validated certificate seekers and ONLY two reviews so far - you guys are cold...certainly a few more could have an opinion and write about it?!)
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student

2/10 starsTaking Now
2 years, 4 months ago
The previous review is charitable. Don't bother if you intend to audit. And probably not even if you are a verified student
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Current Student

4/10 starsTaking Now
2 years, 4 months ago
This course is in beta. It has not been tested as an online course. There are many errors in documents, poorly specified models, and TA's are unprepared and cannot demonstrate concepts during "office hours." Students waste large amounts of time trying to get clarification on basic issues such as understanding the syntax of assignment questions, assignment expectations, document formats to use, coding requirements. Solutions provided use methods and models not allowed in instructions. It's basically DIY.
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