Zurück zu Mastering Data Analysis in Excel

4.2

Sterne

3,517 Bewertungen

•

828 Bewertungen

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....

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.

PW

Oct 14, 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.

Filtern nach:

von Кирилл

•Apr 07, 2017

Nice

von Kyle A

•Feb 22, 2016

Ver

von Malgorzata P

•Feb 10, 2016

:)

von Michal K

•Oct 07, 2017

V

von winnielou

•Oct 24, 2016

k

von Al S

•Dec 17, 2015

e

von Tania K

•Dec 08, 2015

.

von 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.

von Yun Q N

•Sep 17, 2020

After completing the course, I'd like to say that the title of the course seems quite misleading as it focuses very much on math and statistical concepts behind some of the commonly used techniques and not so much on using excel to perform data analysis. It's interesting to know the concepts behind, but I have to admit that I struggled with the course as I've not touched advanced maths and statistics since college almost 2 decades ago.

From the peer review assignment, it appears that I'm not the only one struggling with the course. One suggestion to Duke University would be to set up screening of the peer review assignments and single out those that have exceptionally short responses, out of 5 that I reviewed, 2 submissions have only (.) in its response. That surely shouldn't be allowed

von Max H

•Dec 14, 2015

Entering this course with a pretty in-depth knowledge of Excel, it was good to be able to brush up on some things I don't use on a day-to-day basis such as VLOOKUP and STANDARDIZE functions. The subject material is quite good, AOC and Binary Classifications were very interesting to learn about and have tons of applications, particularly in operation optimization problems/cases. One point of suggestion would be to double-check and clean up the accompanying Excel files. Some of the material was confusing to follow along with. If you could define cells, arrays, and tables for instance it would make the material much more intuitive to follow-along with.

von Charlie C

•Jul 10, 2019

The material is actually very good. But the assignments have too many overall flaws, flaws that are not fixed in years. It wasted me a ton of time. A lot of materials are also very "skippy" where the instructor derives something suddenly and does not say if this is derived from a out-of-scope method or what not. It leaves you hanging and rewatching and thinking and wasting time just because he does not explain "hey don't think about it because it's out of scope and i am just giving you the result and nothing else is going on ". All these said, I love the materials.

von Rebecca S

•Mar 27, 2018

I learned a ton, but the course was TOUGH! You really have to hunker down, pay close attention, and take notes...sometimes even re-watch lectures more than once. I am glad I took the course. The only reason I didn't give it 5 stars is that the lectures can be tough to follow, and the quizzes and assignments have missing or misplaced information that make it difficult to get to the right answer. It takes some serious patience, reading of the supplemental material, and hints from others on the discussion forums.

von Sonya G

•Jan 16, 2016

Quite an useful course for Excel application in business decision-making. But I think the final project is a little more difficult than weekly assignments. And only Quiz 1 has some hints about the potential problems we might come across during the quiz. The linear regression part, especially multivariate linear regression, need more explanations how to derive the matrix in Excel. Even though I followed every steps and referred other classmates' discussions, I cannot work out the matrix with Excel. So sad :(

von Alfredo Z

•Feb 20, 2016

towards end of the course, specifically linear regression week, a lot of buzzwords are strung together. I wish, the instructor would be a little bit more sparing in their usage together (together is the keyword). 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.

von Oleksandr Z

•Mar 04, 2017

The course is both tough and interesting. The interesting bit comes from developing a model for a credit card company, which is a rather creative and captivating process. The tough part is in hectic learning of a vast array of statistical terms, often poorly explained – and alost never applied to practice (it is true that some of statistical metrics are "applied" in quizzed but it is unclear what's their purpose besides computing yet another number).

von Jianxu S

•Oct 29, 2019

This course teaches the skills and techniques to build and analyze both predictive and classification models in EXCEL. The information theory treatment is unique and helpful to understanding the underlining business objectives and mathematical principles. Make sure you have studied calculus and probability before taking this course. I compare it to an entry level graduate course. Professor Egger is very good at teaching difficult concepts.

von Keep i S #

•Dec 02, 2015

Great course, great professor and great TA in community discussion. Can't give 5 stars as course introduction and content are not really matching. As per introduction anybody can take and learn this course, but indeed it is far from reality. If you don't have good math and statistic background then you better learn them first...

I personally learnt a lot from this course even though could not get the certificate. Thanks everyone.

von Jessica R

•May 13, 2017

Learned a lot, but instructions were often very unclear, especially in the final week. 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. Without this input, I don't think I could have made it through this course. But overall, I felt really challenged and more ready to approach real world problems.

von Fil T

•Oct 26, 2017

The math in this course kicked my butt! It was good to be challenged by binary decision analysis. Tons of great spreadsheet resources here. In one of the quizzes, the incorrect data was given and it took me several weeks to find the correct datasheet but after I found it, everything was smooth. The videos all performed correctly.

The lectures did not exactly correlate to the quizzes, but it was enough.

von Vikas G

•Oct 03, 2019

That's a nice course. It just needs to improve the explanation a bit, which will help students not lose interest in the course and complete it on time without wasting much of time on unnecessary things.

However, on the bright side, wandering here and there on the discussion forum and figuring out the answers ourself was also one of the best part of this course.

von Piotr M M

•Oct 29, 2016

Difficult as hell.When You complete it You really start understanding this area but I think due to level of difficulty teaching staff should be a little more active.Big difference in difficulty in lessons(which are easy)and test(which are sometimes incredibly difficult).If You want to work in Big Data field You HAVE TO complete this course but...expect hell.

von Jan C

•Apr 22, 2020

Very useful course - during the course, I struggled a bit to be sure that I would be able to combine all the knowledge and put together into something meaningful in real practice, but the final assignments in week 6 were really great and through the step by step process of building the model I finally got the whole picture. And it is great! thank you!

von Will S

•Jan 13, 2016

Amazing volume of topics covered and in a manner that is not overly complicated in terms of testing methodology, but still requires you to do the work. There are still several bugs with the course, test answers and instruction documents on the site, but clearly the course is new so hopefully that will be resolved in coming iterations of the class.

von Jason C D A

•Jan 12, 2016

The course is more on analysis techniques as opposed to Excel shortcuts, tips and tricks. It certainly helps if you have a good background in algebra-level math; if you're looking for a course on how to be an Excel Power User, this might not be the best fit. Otherwise, what I learned here is useful for me. Thanks!

von Chua W T C

•Jun 29, 2016

The course is quite tough for me, but I enjoyed the topics discussed, primary between a binary model and a linear regression model. The addition of Excel into this course adds an application layer, which helps me to bring the concepts to the working world as well, and not just plain academic knowledge.

- Sinn und Zweck im Leben finden
- Medizinische Forschung verstehen
- Japanisch für Anfänger
- Einführung in Cloud Computing
- Grundlagen der Achtsamkeit
- Grundlagen des Finanzwesens
- Maschinelles Lernen
- Maschinelles Lernen mittels Sas Viya
- Die Wissenschaft des Wohlbefindens
- Contact-Tracing im Kontext von COVID-19
- KI für alle
- Finanzmärkte
- Einführung in die Psychologie
- Erste Schritte mit AWS
- Internationales Marketing
- C++
- Predictive Analytics und Data-Mining
- UCSD: Learning How to Learn
- Michigan: Programming for Everybody
- JHU: R-Programmierung
- Google CBRS CPI Training

- Natural Language Processing (NLP)
- KI für Medizin
- Guter Umgang mit Worten: Redaktionelles Schreiben
- Modellbildung von Infektionskrankheiten
- Die Aussprache des US-amerikanischen Englisch
- Software-Testautomatisierung
- Deep Learning
- Python für alle
- Data Science
- Geschäftsgründungen
- Excel-Kenntnisse für Beruf
- Data Science mit Python
- Finance for Everyone
- Kommunikationsfähigkeiten für Ingenieure
- Verkaufstraining
- Career Brand Management
- Wharton: Unternehmensanalytik
- Penn: Positive Psychology
- Washington: Maschinelles Lernen
- CalArts: Grafikdesign

- Zertifikate über berufliche Qualifikation
- MasterTrack-Zertifizierungen
- Google IT-Support
- IBM Datenverarbeitung
- Google Cloud Data Engineering
- IBM Applied AI
- Google Cloud Architecture
- IBM Cybersecurity Analyst
- Google IT Automation with Python
- IBM z/OS Mainframe Practitioner
- UCI: Angewandtes Projektmanagement
- Zertifizierung in Instructional Design
- Zertifizierung in Bauwesen und -management
- Zertifizierung in Big Data
- Zertifizierung Maschinelles Lernen für Analytics
- Zertifizierung in Innovation Management & Entrepreneurship
- Zertifizierung in Nachhaltigkeit und Entwicklung
- Zertifizierung in Soziale Arbeit
- Zertifizierung KI und maschinelles Lernen
- Zertifizierung in Räumliche Datenanalyse und Visualisierung

- Abschlüsse in Informatik
- Business-Abschlüsse
- Abschlüsse im Gesundheitswesen
- Abschlüsse in Data Science
- Bachelorabschlüsse
- Bachelor of Computer Science
- MS Elektrotechnik
- Bachelor Completion Degree
- MS Management
- MS Informatik
- MPH
- Master-Abschluss in Buchhaltung
- MCIT
- MBA online
- Master of Applied Data Science
- Global MBA
- Master in Innovation & Entrepreneurship
- MCS Data Science
- Master in Informatik
- Master-Abschluss in Public Health