Retour à Mastering Data Analysis in Excel

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3,874 évaluations

Important: The focus of this course is on math - specifically, data-analysis concepts and methods - not on Excel for its own sake. We use Excel to do our calculations, and all math formulas are given as Excel Spreadsheets, but we do not attempt to cover Excel Macros, Visual Basic, Pivot Tables, or other intermediate-to-advanced Excel functionality.
This course will prepare you to design and implement realistic predictive models based on data. In the Final Project (module 6) you will assume the role of a business data analyst for a bank, and develop two different predictive models to determine which applicants for credit cards should be accepted and which rejected. Your first model will focus on minimizing default risk, and your second on maximizing bank profits. The two models should demonstrate to you in a practical, hands-on way the idea that your choice of business metric drives your choice of an optimal model.
The second big idea this course seeks to demonstrate is that your data-analysis results cannot and should not aim to eliminate all uncertainty. Your role as a data-analyst is to reduce uncertainty for decision-makers by a financially valuable increment, while quantifying how much uncertainty remains. You will learn to calculate and apply to real-world examples the most important uncertainty measures used in business, including classification error rates, entropy of information, and confidence intervals for linear regression.
All the data you need is provided within the course, all assignments are designed to be done in MS Excel, and you will learn enough Excel to complete all assignments. The course will give you enough practice with Excel to become fluent in its most commonly used business functions, and you’ll be ready to learn any other Excel functionality you might need in the future (module 1).
The course does not cover Visual Basic or Pivot Tables and you will not need them to complete the assignments. All advanced concepts are demonstrated in individual Excel spreadsheet templates that you can use to answer relevant questions. You will emerge with substantial vocabulary and practical knowledge of how to apply business data analysis methods based on binary classification (module 2), information theory and entropy measures (module 3), and linear regression (module 4 and 5), all using no software tools more complex than Excel....

TB

16 nov. 2021

I like and appreciate courses provided through Coursera.This course is very interesting and valuable for those whose jobs do have relevance with data management .God bless Coursera and Duke University

JE

30 oct. 2015

The course deserves a 5-star rating because: (1) content is relevant, (2) the professor is concise and possesses great teaching skills, and (3) the learning modules are applicable to daily problems.

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par Zewei R

•7 août 2019

too hard

par Foo J W

•17 juin 2020

tough!!

par Karan S C

•4 janv. 2016

Love it

par Ansar A K

•26 juin 2021

good

par 121913901021 g

•17 déc. 2020

Good

par Zhengfeng Y

•25 mai 2016

Go

par Francis K

•24 juil. 2021

I feel that the course title is inaccurate as it is more about statistical concepts and their application as opposed to learning analytics using excel. This point has been raised by several past students.

Having said that, I learned a great many news concepts which I can apply in my professional field. I would suggest that having the students make their own excel models would both give them experience in working with excel and also make it much easier for them to arrive at answers to quiz questions. I spent more time trying to navigate the large excel spreadsheet prepared by another person and to learn how to use them with no roadmap, that it took to arrive at answers to the quiz questions.

I also found it hard to follow the shift from on topic or video to the next and had to go over these several times. I felt there was no smooth flow of concepts and sometimes a concept was introduced with no direct relevance to preceding ones. i.e. a disjointed flow of information/presentation. Its decades since I was last in a class setting so maybe things have changed with online classes.

Overall a tough course to go through but given the complexity of some concepts to newbies and the potential this has to open the world of ML, AI and Big Data to many people I would still rate it a 3.

par Lidia B

•28 janv. 2021

The course information is great, and my expectation was that the main focus will be on learning how to manipulate data with excel, Tableau, and SQL. The Mastering Data Analysis in Excel course part fully revolves around the Binary Classification concept and a student can't pass the quizzes without knowing and understanding it in entirety. Maybe the course title should have been more specific, such as "Mastering Statistical and Binary Classification Data via Excel Analysis". In order to avoid having students drop out before getting to the topics that made them sign up for the full course in the first place, maybe there should be a more generic emphasis on the Binary Classification topic and not focus on it as a career goal.

Also, just a note: if former Duke students who work at Argus, Google, or other companies happen to use Binary classification as part of their jobs that does not mean that every other job involves the same tasks and requirements.

par GOH L H

•28 mai 2020

In general, the course is fairly rewarding for someone like me who is coming from Engineering, and doesn't major in Business / Analytics.

What I liked: Assessments (Practice Quiz, Quizzes, Final Project) are very much rewarding in a sense that by the end of the assessments, you gain a better understanding on how the topics and concepts taught in the lectures could be applied in a practical sense in the world.

What I disliked:

1. The title of the course is pretty misleading. I signed up hoping to learn more about the technical side of Excel, the analysis parts, but here it seems that the emphasis are more on the relatively abstract analytical concepts, while Excel is merely a tool in the big picture.

2. It gets very frustrating and demotivating when the topics taught are not well-structured. There should be a video in the beginning to show the big picture, and a video at the end to sum up the main concepts and how they relate to each other.

par Jody P

•8 nov. 2016

Though at the start of the program indicates no prerequisite I would suggest that you be familiar with Algebra and Stats. Most videos are of Dr. Egger writing out algebraic equations and discussing them, the excel component of Mastering data in excel come via pre-made calculators as attachments that you for the most part need to figure out on your own.

If you do not have a good comfort level with stats then you will require more time to spend on understanding the spreadsheet and it’s use.

It would be fantastic if Dr Egger could go through the spreadsheets as a part of the video and show a couple examples, hopefully revisions down the road !

It was challenging but not impossible, and if you do not challenge yourself how much are you really learning?

Best of luck!

par Xu Z

•26 nov. 2017

Professor Egger is pretty good at explaining concepts and make the class interesting. However, even to someone with solid statistical and math background, the class seems to have a steep learning curve. Concepts and projects can be discussed in more in-depth manner. Many classmates seem to be confused during middle of class. Be prepared to study up and research a lot using google search.

On the other side, I thought that it's an Basic Excel class before staring. Apparently I'm wrong. I picked up a lot of learning on data analytics side and how to use excel to accomplish the analytical goals. This class would be useful to anyone wants to get the exposure.

par Sarah L

•19 août 2019

This course was much to focused on math proofs of statistical reasoning and left the actual instruction in Excel and how to use those formulas for mere supplemental material. The organization of the course left me in tears as I was struggling to understand the math and only THEN was I shown the application of that math to something useful. This is backwards. It needs to focus on the use of excel, not handwritten math formulas and proofs, and teach us the things we actually need to learn for the exam (how to compare 2 models, etc) instead of leaving us googling for the most pertinant information while the professor drones on about greek letters.

par Derek O

•25 oct. 2017

While I found this course useful, it was originally advertised with the Data Analytics Specialization from Duke University, and it says that you need no previous knowledge to complete the specialization. I found this course VERY challenging and I think it is because of my limited experience in the fields of statistics and calculus. There were many times when vocabulary, formulas, calculations, etc. are mentioned quickly and I felt a little left behind. With some background in business statistics I think this class would be much more effective.

par Markus S

•20 nov. 2015

Really interesting course and material and a very good instructor. This would be a 5 stars course if it wasn't for the final project. During lessons some concepts of statistics were taken as known (which is okay). However the final project required to utilize a combination of all new learned material on a whole different level of difficulty compared to the preceding quizzes. I did not expect that jump in difficulty and enjoyed the course a lot but the final project just was a struggle.

par Stefano J N

•29 avr. 2016

The Course is fine, explanations and videos are a bit hard to follow at times.

The final assessment is in my opinion very bad, as it i appears to me quite unrelated to the course it self. The lectures are quite abstract and the exam is a practical application of the concept.

The instructions of the course also aren't very good as you need to do each part of the final project at the end of each week.

I would strongly suggest to not take this course unless you have many spare hours.

par Kristin K

•27 févr. 2018

The course covers some good topics, but it is not introductory due to the requirement that you have prior knowledge of statistics. I found the lectures got progressively more confusing with few examples of how to apply the knowledge. If you take the time to figure out what is actually being asked and how to do it in the spreadsheets provided you can learn something, but the amount of time wasted hunting for the correct approach to their spreadsheets can be quite frustrating.

par Dhananjay P

•14 mars 2016

The class has great concept but it needs a bit more structure and change to hit its full potential.

The Good:-The spreadsheets and exercises along with Quiz material. The project was also very informative.

The Bad:- The video need to better explain how Area Under Curve and how the Credit model make sense. It was not very intuitive and I struggled for over 2 weeks to put the Project together due to this gap.

But a worthwhile class with great potential. I am glad I took it.

par Darryl B

•22 oct. 2020

The course is not updated and you have to depend on students solely to get through the material. There are several learning gaps. It would be better if they said that, these topics aren't covered but you need to read up on them. The lecturer style of delivery is not engaging so it is a challenge to get through the videos. The content and concept, that is the ability to determine a model using Excel is cool.

par Mohit M

•4 janv. 2016

Course contents are rather deep which is good and makes it challenging. Explanations could be better and there should be more content on some topics for better understanding. Someone with background in statistics would find this course more useful than on who only has basic knowledge of statistics. Nevertheless, it inspired me to learn from other sources to fill the gaps in my understanding.

par Grant V

•4 avr. 2017

Leve of difficulty was above my expectations. Moved too fast. Could have been more thorough in applying the content to examples similar to the quiz. Seemed to stick to the bare minimum of what would be required before moving to the quiz. Be transparent with future prospective students that this course absolutely requires knowledge of calculus, probability, and statistics.

par Thao V

•16 nov. 2015

The course has some valuable information, however course materials and videos are not so helpful. If you already have some knowledge about statistics and excel, it will be a lot easier to follow the instructor's lectures, otherwise I don't feel how a beginner could really follow up on this course. Some topics are run through so quickly that they are confusing to students.

par Ambreen H

•23 mars 2016

This quickly became a statistics course, which although important was really difficult for me as I went in not expecting it.

However, Dr. Egger is incredible! I think he's a fantastic and well-versed instructor in the Data & Analytics realm and am eager to find ways I can use what I've learned into practice at work.

par Marta M

•2 févr. 2016

I would like to have more example, it feels there is mostly theory. The quizzes were super easy, I dropped out at final project, I couldn't get through the first questions. The course (and especially the final project) requires a good basis in statistics beforehand.

The assistants are super helpful and patient.

par Chris C

•15 août 2016

The instructor should spend more time putting equations and concepts into concept, and tying them together. It often felt like values and questions were arbitrary. This is less about master data analysis in Excel, and more about gaining ground in a few key concepts, so the title is misleading as well.

par Allen O

•30 juin 2020

The quality of the content did not meet my expectations. There were typos, incorrect answers, and vague explanations. Courses like these, which are fully online and do not have any sort of support besides the forum, really need to make sure everything is robust in terms of content and quality.

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