듀크대학교의 Mastering Data Analysis in Excel 학습자 리뷰 및 피드백

This course was a rigorous introduction to using Excel for the specific purpose of solving data analytics problems. The challenges were fun and rewarding for those who love mathematics, applications to real-world data problems, and who are comfortable with wrestling with complex concepts independently. The core components of this course were binary classifications, linear regression, and the supporting mathematical and statistical theorems. While the first two weeks of the course were a very steep learning curve (even for a student with a B.A. in Applied Mathematics), the supplemental explanations after submitting assignments helped the learning process. I wished such structure and explanatory post-quiz materials persisted through weeks 3-6 of the course. This would have made it more rewarding, as I came away wishing I could review my weaker areas.

After completing the course, I'd like to say that the title of the course seems quite misleading as it focuses very much on math and statistical concepts behind some of the commonly used techniques and not so much on using excel to perform data analysis. It's interesting to know the concepts behind, but I have to admit that I struggled with the course as I've not touched advanced maths and statistics since college almost 2 decades ago.

From the peer review assignment, it appears that I'm not the only one struggling with the course. One suggestion to Duke University would be to set up screening of the peer review assignments and single out those that have exceptionally short responses, out of 5 that I reviewed, 2 submissions have only (.) in its response. That surely shouldn't be allowed

Entering this course with a pretty in-depth knowledge of Excel, it was good to be able to brush up on some things I don't use on a day-to-day basis such as VLOOKUP and STANDARDIZE functions. The subject material is quite good, AOC and Binary Classifications were very interesting to learn about and have tons of applications, particularly in operation optimization problems/cases. One point of suggestion would be to double-check and clean up the accompanying Excel files. Some of the material was confusing to follow along with. If you could define cells, arrays, and tables for instance it would make the material much more intuitive to follow-along with.

I learned new things, and each time something clicked I felt like it was a huge achievement. Overall, I found this course very hard. Things build on each other so if you don't get any of the steps along the way, then you can't really move on. I think it is anticipated that you have a deeper knowledge of algebra and other concepts in general than I have. This is supposed to take 6 weeks, and it took me 13 weeks, so basically twice as long. To be fair, I spanned the holiday season as well, but overall it took me much longer to complete things than the outline suggests.

The material is actually very good. But the assignments have too many overall flaws, flaws that are not fixed in years. It wasted me a ton of time. A lot of materials are also very "skippy" where the instructor derives something suddenly and does not say if this is derived from a out-of-scope method or what not. It leaves you hanging and rewatching and thinking and wasting time just because he does not explain "hey don't think about it because it's out of scope and i am just giving you the result and nothing else is going on ". All these said, I love the materials.

I learned a ton, but the course was TOUGH! You really have to hunker down, pay close attention, and take notes...sometimes even re-watch lectures more than once. I am glad I took the course. The only reason I didn't give it 5 stars is that the lectures can be tough to follow, and the quizzes and assignments have missing or misplaced information that make it difficult to get to the right answer. It takes some serious patience, reading of the supplemental material, and hints from others on the discussion forums.

Quite an useful course for Excel application in business decision-making. But I think the final project is a little more difficult than weekly assignments. And only Quiz 1 has some hints about the potential problems we might come across during the quiz. The linear regression part, especially multivariate linear regression, need more explanations how to derive the matrix in Excel. Even though I followed every steps and referred other classmates' discussions, I cannot work out the matrix with Excel. So sad :(

towards end of the course, specifically linear regression week, a lot of buzzwords are strung together. I wish, the instructor would be a little bit more sparing in their usage together (together is the keyword). Eg "This is the connection between linear regression and mutual information in a parametric model where we have gaussian distributions" I feel that each term in isolation I know what they mean, but when strung together, I am unclear how to process the meaning.

The course material itself may not be so easy for people with no background knowledge. On discussion forum, classmates are much more helpful than mentors. About final project: (a) the questions asked in the final project didn't clarify what metrics to display, only to describe the grading criteria in peer-grading session. (b) more tips could have been given to classifying applicants. If we can have more opportunities to practice, it will be even better.

The course is both tough and interesting. The interesting bit comes from developing a model for a credit card company, which is a rather creative and captivating process. The tough part is in hectic learning of a vast array of statistical terms, often poorly explained – and alost never applied to practice (it is true that some of statistical metrics are "applied" in quizzed but it is unclear what's their purpose besides computing yet another number).

This course teaches the skills and techniques to build and analyze both predictive and classification models in EXCEL. The information theory treatment is unique and helpful to understanding the underlining business objectives and mathematical principles. Make sure you have studied calculus and probability before taking this course. I compare it to an entry level graduate course. Professor Egger is very good at teaching difficult concepts.

Great course, great professor and great TA in community discussion. Can't give 5 stars as course introduction and content are not really matching. As per introduction anybody can take and learn this course, but indeed it is far from reality. If you don't have good math and statistic background then you better learn them first...

I personally learnt a lot from this course even though could not get the certificate. Thanks everyone.

Learned a lot, but instructions were often very unclear, especially in the final week. Luckily there were a lot of students who shared their approach of the final project on the discussion board, so it was easier to figure out what we were supposed to be doing. Without this input, I don't think I could have made it through this course. But overall, I felt really challenged and more ready to approach real world problems.

Good course with focus on maths, probability and statistics to be applied in real situations.

The course provides the skills to analyze data and build predictive models to face, quantify and reduce uncertainty. Excel is the tool used for the scope, however the course is not meant to master Excel itself. So, it's fine if you never even used it, while some prerequisites on probability theory are recommended.

The math in this course kicked my butt! It was good to be challenged by binary decision analysis. Tons of great spreadsheet resources here. In one of the quizzes, the incorrect data was given and it took me several weeks to find the correct datasheet but after I found it, everything was smooth. The videos all performed correctly.

The lectures did not exactly correlate to the quizzes, but it was enough.

That's a nice course. It just needs to improve the explanation a bit, which will help students not lose interest in the course and complete it on time without wasting much of time on unnecessary things.

However, on the bright side, wandering here and there on the discussion forum and figuring out the answers ourself was also one of the best part of this course.

Difficult as hell.When You complete it You really start understanding this area but I think due to level of difficulty teaching staff should be a little more active.Big difference in difficulty in lessons(which are easy)and test(which are sometimes incredibly difficult).If You want to work in Big Data field You HAVE TO complete this course but...expect hell.

Very useful course - during the course, I struggled a bit to be sure that I would be able to combine all the knowledge and put together into something meaningful in real practice, but the final assignments in week 6 were really great and through the step by step process of building the model I finally got the whole picture. And it is great! thank you!

This class was hard. At times, I felt like quitting, but I'm glad I didn't give up. Some assignments could use more clarity, and some videos need to be broken up. The videos were too dense with terminology, but after hours of searching, slowing down videos, reading discussion forums, and so on, I was able to complete the course. :) Would recommend.

Amazing volume of topics covered and in a manner that is not overly complicated in terms of testing methodology, but still requires you to do the work. There are still several bugs with the course, test answers and instruction documents on the site, but clearly the course is new so hopefully that will be resolved in coming iterations of the class.

The course is more on analysis techniques as opposed to Excel shortcuts, tips and tricks. It certainly helps if you have a good background in algebra-level math; if you're looking for a course on how to be an Excel Power User, this might not be the best fit. Otherwise, what I learned here is useful for me. Thanks!

The course is quite tough for me, but I enjoyed the topics discussed, primary between a binary model and a linear regression model. The addition of Excel into this course adds an application layer, which helps me to bring the concepts to the working world as well, and not just plain academic knowledge.

The course is very practical and sheds a light on data analyst job, however it may be very sophisticated and has little guidance when it comes to final assignments. A lot of ambiguity is present in final assessment, but it is a very good insight on the scientific background of the data science.

Excelente curso, pero a veces los vídeos o materiales se no son tan expresos en explicaciones y ejercicios para facilitar el entendimiento de lo estudiado. Por ejemplo la semana 2 de matriz de clasificación por su complejidad merecía mayor detalle en las explicaciones y ejercicios de práctica

The course is not structured around Excel. Instead Excel is used as a tool for introducing and practicing certain data analysis methods. The assignments were quite interesting and challenging. Overall I found the course beneficial for myself. Though at times a clearer explanation was needed.