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Learner Reviews & Feedback for Mastering Data Analysis in Excel by Duke University

4.2
stars
3,460 ratings
809 reviews

About the Course

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....

Top reviews

JE

Oct 31, 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.

NC

Dec 20, 2016

Overall, the course material is good with many example. Need a general knowledge with mathematical and statistical from the beginning to pass the exam, because course slide is a little bit fast.

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351 - 375 of 789 Reviews for Mastering Data Analysis in Excel

By Artem A

Apr 14, 2016

Noiiice!

By Franco A M A

Jan 11, 2016

amazing!

By Monica C M

Dec 27, 2015

Awesome!

By Hector G

Sep 05, 2020

perfect

By AshQiu

Nov 28, 2019

awesome

By LINDA A L

Oct 30, 2019

Exel'nt

By Jagmohan M

Dec 28, 2018

Great !

By Liu Y

Oct 29, 2017

like it

By Cristian D A

Dec 13, 2015

GREAT!!

By Pramit A M

Nov 02, 2015

Awesome

By Jose C S M

Feb 11, 2016

Great!

By Marica C

Dec 13, 2015

Great!

By HUILIN M

Dec 02, 2015

useful

By Dishant P

Feb 12, 2020

Nice

By Chirag K

Sep 27, 2019

Good

By Meenal C

Dec 05, 2018

ccol

By Bharath M

Jul 04, 2017

good

By Кирилл

Apr 07, 2017

Nice

By Kyle A

Feb 22, 2016

Ver

By Malgorzata P

Feb 10, 2016

:)

By Michal K

Oct 07, 2017

V

By winnielou

Oct 24, 2016

k

By Al S

Dec 17, 2015

e

By Tania K

Dec 08, 2015

.

By Isa P

Jun 04, 2018

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.