Retour à Mastering Data Analysis in Excel

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3,829 évaluations

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931 avis

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 Чурсина К Е

•11 nov. 2020

This course has lower quality then others in specialization. Warning: it is concetrating on math not on Excel. This fact isn't bad by itself, but there is a big gap between video materials and quizes. There aren't enough practic examples which allow you to do graduated exercise confidently.

More than that there are a lot of cases of inaccuracy in automatic quizes and only forum discussins can hepl you understand that there is no your mistake.

Also sound is not perfect (in other courses it is much better) like it was recorded with home mic not professional.

par Alexander K B

•2 nov. 2020

The course material is very interesting and important for learning the mechanics of data analysis. However, the course organization and presentation is sub-par. Course assignments are often explained poorly, incorrectly, or not at all. Course videos are poorly organized and often difficult to follow for many reasons. After also completing another course by this instructor (Mr. Egger) I would steer clear of the courses that he teaches, if possible. When the learners in the discussion boards teach the material better than the instructor, there's a problem.

par Carlos A V J

•2 déc. 2015

It has a lot of problems, including: Not so many lectures about excel itself - most of the time you will be watching videos about statistic methods. The lectures are very superficial, even though the quizzes and projects demand a lot of knowledge and time. The course seems to have a staff of one man: the professor of the course rarely replies (even though he did at the beginning of the session) and all the work seems to be done by his assistant, who tries his best, but ends up not coping with all the questions asked at the discussion forums.

par Rai G

•12 janv. 2016

The content is really interesting and useful. However, the way in which the lessons are organised I'd say is confusing. The professor explain you complex concepts like entropy or binary classification to continue with an excel worksheet. It's here where the issue relays, the excel files are filled and instead of learning by doing you have to figure out how the prof made the file. In consequence, the solution is always available.

In my opinion, it'd be better if a raw data set is given and you build the model step by step from the scratch.

par Manuela

•20 juin 2017

Though the course teaches important topics, I found the practical part of it rather lacking. The videos are mostly about the theory, using algebra and all that, but the exercises are on Excel spreadsheets that either you don't really need to do much (change a number here or there) or, when you do need to really work on it, there's no instruction provided other than the discussion forums and an one-page PDF. It was very disappointing, prepare yourself to spend a lot longer on this course, just trying to understand the spreadsheets.

par Corey D

•9 nov. 2018

The course is very poorly laid out. You are encouraged to work on your final project each week during each module but you don't learn important key elements to complete the final project until later in the course. I enjoyed what I learned in this class, however, stating there are no math prerequisites is misleading. If we were taught the concepts only in Excel, that would be one thing, but to hand solve these statistics problems by hand it really hurts the learn because the professor is speaking above most everyone's head.

par Øyvind M E

•13 août 2020

I think the learning objectives are good for the class but feel this course doesn't live up to the expectations. Personally disappointed in that we weren't taught how to build the Excel sheets and tools ourselves but rather were given ready made sheets that needs to be interpreted to be understood. I also believe that the quiz needs fixing as most if not all require checking the forum for "mentor hints" to fully understand what's being asked; in some cases, these don't help as the quiz has been changed/updated.

par Robin M

•10 avr. 2018

Actually a nice course but I feel completely lost with the last and final assignment. I looked through the forum, there are good hints but i still don't get it. It's frustrating that for the really hard stuff you don't have any explainatory videos from Prof. Egger so you can comprehend the material better.

I understand that one has to try to work out a solution with the methods given but a explanation of the difficult stuff afterwards would be quite helpful sometimes.

par Joseph C

•20 déc. 2020

Great information, but poorly executed or explained. There was inconsistencies in the quizzes and which resources to use. Information that was not updated over the lifespan of the course. When the course was first created there were errors found with the information provided. These adjustments were implemented but not updated in the actual course so the only recourse to find out what to do or which spreadsheet to use were only found in the discussion forums.

par Donna K

•20 sept. 2016

It is best to have a strong statistical background before taking this course. Lots of statistical calculations and procedures. If you don't have a strong background in statistics , there is a good chance you will get lost at some point in the course. There is no questions that the instructor of this course is an accomplished mathematician, I just found it hard to follow his lectures as he got deeper into various statistical aspects of the course.

par Donald L

•28 déc. 2015

Much work needs to be done on this course to make it friendly to learners. The quizzes and assignments do not align with what is taught in the lessons. Further, the content in the lessons does not even show or explain what is expected from the learning.

I spent the majority of my time exploring the discussions to discover what I was supposed to be doing and the rest of it on Google teaching myself the concepts that were not taught in the course.

par Valerie P

•5 févr. 2018

The professor jumps from subject to subject like if he assumes that people know the stuff he is teaching. Sometimes he goes on about something without telling you why he started talking about it, how it will be used, what is its relations with the whole course or at least that week's course. He even goes on to start the definition of a concept but then starts talking about an example and does not go back to the actual definition or use.

par Yoav S

•10 juil. 2017

First of all, it's important to point out that this course hasn't got a lot to do with Excel. It's more about statistics.

I am somewhat disappointed with this course. A lot of the assignments and tests were unclear, and it was hard to get clarifying information that will help you proceed. I was often left confused about some of the topics. Nevertheless, I learned some techniques that will probably be prove very valuable in the future.

par Philip g

•20 févr. 2016

I found this course too advanced for my level of familiarity with statistics. Its true that most basic stuff is covered to be fair, but the progression is very rapid.

As I work long hours and other commitments I didn't have enough time to dedicate to study beyond the time needed just for this course. If I had been able to study an extra 10 hours a week practicing using the formulas then I'm sure I would have got more out of it.

par Mark A

•8 mars 2018

The description of the course series does not adequately represent what is taught, more details up front about the material covered would have helped set expectations. The first course in the series focuses primarily on terminology, with little practical application. Almost all of the material was on finance & accounting.

In week 2 of this course, I realized this wasn't addressing what I expected and dropped out.

par Kevin R

•2 janv. 2017

All theory. Quizzes you were left with the task of answering questions based on models never seen or explained spent most time trying to make sense of them. Final project is almost a joke to try and decipher how to create from scratch based on the ones they provided in the quizzes. Its not a joke but it is very difficult when the actual excel and model making isnt part of the lectures.

par Justin M

•11 nov. 2015

So many equations presented without explaining why these things work or how they are derived. I'm having a hard time seeing the relevance. Also, I learned a lot of these statistics in college and the explanations provided in these lessons actually confused me about things I already know. Very disappointed in this course. Especially since part 1 of the specialization was so good.

par Bruno G

•17 janv. 2016

I would have liked the class to be less theoretical, with more frequent explanations linking concepts to practical applications in daily / business life, in order to keep interest and motivation high and facilitate understanding and memorisation. It sure would help to enter this course with a certain background of probability. Those memories were too ancient in my case :-(

par Andrew H

•19 mars 2020

Not recommended if you're a beginner. I don't understand a single bit of the math involved in this course, it is explained little if at all. Every time I start a video I have to stop and learn from elsewhere how to learn from this course. I'm sure it's great for people that know what is going on, but if you don't understand one thing at any point you're lost for good.

par Jade C

•7 mai 2017

I wish this course was more focused on building and analyzing data in excel. It feels like it's more focused on financial measurements and probability, it hasn't related to my line of work yet (marketing analysis). I wish the course also focused on advanced excel formulas outside of probability like advanced pivot tables, arrays, and macros.

par Anthony R

•29 févr. 2016

While the first week touched on Excel and the equations can be used in Excel and have efficacy in regards to analyzing data, it feels as if the course emphasizes more on utilizing math than utilizing Excel. And according to my understanding, that was what I thought I was signing up for. So, it felt like a bait-and-switch to me.

par Niyazi E D

•13 juin 2020

This Course has the wrong title, as many others mentioned prior to me. It was a lot about statistics and math, but the thing was that none of it was explained very well. It was unorganised and sometimes had crucial mistakes in it, which wasted a lot of my time. I give it two stars, because I learned a little Excel.

par Karolis M K

•1 nov. 2015

The course name is very misleading. While overall it is not a bad course, I believe a lot of people come here expecting very different material. Let's just put it this way: Mastering Data Analysis (mostly things to do with probability) with occasional use of Excel. Do not expect to learn anything new in excel.

par Ajai G G

•18 nov. 2016

Course did not meet any of the expectations. Neither Excel nor the analysis part. Should have focused on one of the aspects to make it more stronger. In my opinion "deviated" & stressed too much on statistics and model building (regression, logistic regression etc.) but did not do justice to that also!

par Rich

•2 mars 2016

Does not teach Excel very well while tryng to apply the basic usage instruction to not too difficult problems. The ability to interface with Excel after some knowledge of Python/R or other programming languages would be helpful in the Data Science programming that is increasingly being promoted.

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