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Mastering Data Analysis in Excel

This course is part of a Specialization (series of courses) called Excel to MySQL: Analytic Techniques for Business.

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.

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Coursera

&

Duke University

Rating 3.6 based on 576 ratings
Length 7 weeks
Effort 6 weeks, 8-10 hours per week
Starts May 14 (32 weeks ago)
Cost $79
From Duke University via Coursera
Instructors Jana Schaich Borg, Daniel Egger
Free Limited Content
Language English
Subjects Data Science Business
Tags Data Science Data Analysis Business Business Essentials

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What people are saying

We analyzed reviews for this course to surface learners' thoughts about it

final project in 33 reviews

Material is really poor and final project is disconnected from weekly lessons.

However, I think the final project could have been more spaced out.

Also, it doesn't prepare you very well for the final project, so pay attention to class!

Final project is awesome but very practical and needs more clarity in videos and notes.

I found that the first part of the final project was the most difficult and time-consuming part of the course.

great course, the final project is really hard but I can really learn something.

The final project is a very good practice and it summarized most of the knowledge in this class.

Good for learning statistical analysis using real examples (bank credit card application) and hands on the data in the final project!

binary classification in 11 reviews

The biggest addition to my knowledge via this course would have to be binary classification and entropy.

We didn't cover enough advanced Excel functionalities, opting instead to focus on 2 statistical models (Binary Classification and Linear Regression).

Having a BSc in Economics, the Linear Regression tutorials and quizzes seemed infantile, while the Binary Classification tutorials proved to be too vague, when we actually had to apply this knowledge on the final project.

Solid class focused on the mechanics of building and evaluating binary classification models using Excel The course is both tough and interesting.

Great insight on binary classification model !!

Well, it'll be good if you set the expectation that you're learning information theory and binary classification in this course, with Excel being the tool by which you use to learn those concepts.

Fascinating materials (Markowitz optimisation, linear regression, information gain, binary classification...) with both theoretical and practical aspects!

Very good course Although more about Prob and Stat than learning to use Excel, concepts of binary classification and entropy are very interesting and are taught very deliberately and thoroughly.

linear regression in 10 reviews

Great course!I would have liked more practical linear regression example.

The course is quite tough for me, but I enjoyed the topics discussed, primary between a binary model and a linear regression model.

If you've never heard of linear regression or Bayes Theorem don't bother with this course.

Linear regression is not for sissies, in my opinion.

towards end of the course, specifically linear regression week, a lot of buzzwords are strung together.

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.

Professor Daniel Egger and his assistan MUST improve the lessons, including examples step by step specially for the multiple linear regression su I never knew excel had so much possibilities Very informative, and very challenging!

Would have liked to have had more explanation on linear regression, but overall everything was great.

knowledge of statistics in 8 reviews

The course covers some good topics, but it is not introductory due to the requirement that you have prior knowledge of statistics.

I recommend having a significant knowledge of statistics before taking this course.

you MUST have intermediate to advanced knowledge of statistics and math.

Awesome!However, you need knowledge of statistics.

More examples and small practice exercises within the lectures would significantly improve the retention of material.You should posses at least basic knowledge of statistics.

The course requires good knowledge of statistics as a pre-requisite.

Someone with background in statistics would find this course more useful than on who only has basic knowledge of statistics.

for data analysis in 7 reviews

Excellent starting point for data analysis, while prior knowledge of statistics and excel are not necessary, they do help with course comprehension.

They just continuously provide spreadsheets for you without teaching you how to actually use excel for data analysis.

as i didn't do this, it's really painfull to finish the final assginment consisting of several parts in a week Tough but good Too good to be true course An in depth deep dive into the mathematical concepts needed to be understood for data analysis tools and techniques.

It is incredible how some concepts clearly defined and put into practice turn into a a powerful tools for data analysis... like binary classification models, confusion matrix, bayes theorem.

Indeed a Mastering course for Data Analysis!

Really good course, I think the most value is at the end when you are able to apply the concept learned in the final assignment Great class, however I felt it was very heavy on statistics and math concepts versus how to use Excel for data analysis.

Very challenging course that is very insightful in the use of Excel for Data Analysis 很有趣的课程,作为基础性的知识很好地引导了学生的兴趣 im in week 4 , cant master data analysis in Excel Not much explanatory Great course!

figure out in 7 reviews

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.

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.

I can't figure out what, and there doesn't seem to be any way of finding out.

In the end a lot of the concepts were not that difficult to figure out - don't overthink things as well!

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 !

The final project was especially difficult as not much was explained - I had to read the forums to figure out what I was actually supposed to do.

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.

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Coursera

&

Duke University

Rating 3.6 based on 576 ratings
Length 7 weeks
Effort 6 weeks, 8-10 hours per week
Starts May 14 (32 weeks ago)
Cost $79
From Duke University via Coursera
Instructors Jana Schaich Borg, Daniel Egger
Free Limited Content
Language English
Subjects Data Science Business
Tags Data Science Data Analysis Business Business Essentials