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Avis et commentaires pour d'étudiants pour Mastering Data Analysis in Excel par Université Duke

3,694 évaluations
887 avis

À propos du cours

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....

Meilleurs avis

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.

13 oct. 2020

The course was excellent. A little difficult and overwhelming at times but as long as you stayed the course the professors gave you every opportunity to succeed. Thank you for your time professor.

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501 - 525 sur 866 Avis pour Mastering Data Analysis in Excel

par Javier V

3 mai 2018

Nice course, could've been better explained or provide more regression models (like logistic) but good overall

par Seshadri

5 janv. 2016

I wish the duration between each week was a bit more and there was an option to chat with the faculty directly

par Smitha

8 nov. 2015

More worksheets and examples would help :) the course is quite dense in terms of the number of topics covered

par Shiv P

22 nov. 2016

Good Course so far. But lacks indepth explanation. Courses such as probability need to be taken prior to this

par Erica M

9 avr. 2016

Very heavy on the stats front, not so much on utilizing Excel but I learned some useful things in this class.

par Łukasz C

21 août 2016

This is not an easy course and some concepts/tasks weren't clearly addressed.

But the instructor is great!

par Kelly D

6 mars 2017

Solid class focused on the mechanics of building and evaluating binary classification models using Excel

par Даурен

11 janv. 2018

Finals are a bit stressing, restructuring of the load may be needed to make it more evenly distributed


18 oct. 2016

really use full,but before this cousre we need to acquire some mathematical and statstical knowledge.

par Ji, C

19 janv. 2018

Very helpful to understand basic data analysis. The tasks in the final week are pretty challenging.

par Colin M

23 sept. 2017

Difficult learning curve, but cool concepts. I'd recommend a basic stats class before taking this.

par Cliff E

23 mai 2017

Very challenging but helpful! I will hold on to and reference the course material for a long time.

par Hubert D

31 janv. 2016

It's not only basic Excel course but you can learn a lot of useful Excel functions like log or ln.

par Michael N

18 juil. 2017

This course is hard and fast paced. Expect to stretch yourself and retrace steps to complete it.

par Samantha C

20 sept. 2016

I work with excel and thousands of lines of data everyday. I found this class to be challenging

par Chao Z

19 nov. 2015

Good topic and resources to learn. However the business terminologies sometimes are confusing.

par Alan W

14 août 2017

Very informative, but did not include a lot of analysis work with Excel. Not what I expected.

par Sambhrant S

2 févr. 2016

I believe pivot tables also plays a big part in Excel, wish they could incorporate that too.

par L. E C

16 déc. 2015

Very challenging and wish there were more exercises throughout to strengthen understanding.

par Geovanna

14 févr. 2016

The training course excellent however I would like to find the quiz for the week number 1

par Govind S

7 févr. 2016

Very helpful course. Intense concepts covered and good examples to master them. Thanks!

par Juan P A

14 déc. 2015

Amazing to get a grasp on the concepts! Might need further inmersion on the Excel area

par Marco A M M

9 juil. 2020

It´s supposed that I would receive a Certificated, I completed the course and nothing

par Deleted A

10 févr. 2017

A bit too academic, I would like to see explanations on Excels and business cases.

par Pitch S

25 oct. 2015

It's very useful to learn how excel able to handle different mathematical models