Zurück zu Mastering Data Analysis in Excel

4.2

Sterne

3,474 Bewertungen

•

812 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.

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.

Filtern nach:

von Eve D

•May 20, 2020

Very interesting course

von raghav

•Dec 30, 2015

its very helpful course

von Phan N H

•Apr 19, 2020

few clear explanations

von Vardges Z

•Dec 15, 2018

A lot of useful theory

_{}^{}

_{}^{}

von Divya M

•May 13, 2020

Excellent course!

von Pranshu J

•Nov 29, 2015

Extremely helpful

von Raja K P

•Oct 06, 2016

Excellent Course

von Deepak S

•Jul 12, 2017

Good course!

von Tan Y Y

•Feb 03, 2019

Very useful

von Chuang M

•Feb 07, 2016

Good course

von Angel S

•Feb 05, 2016

Very useful

von Felipe P

•Dec 15, 2015

Excelent!

von Jay K P

•Dec 03, 2015

Awesome!!

von Zewei R

•Aug 07, 2019

too hard

von Foo J W

•Jun 17, 2020

tough!!

von Karan S C

•Jan 04, 2016

Love it

von Zhengfeng Y

•May 25, 2016

Go

von GOH L H

•May 29, 2020

In general, the course is fairly rewarding for someone like me who is coming from Engineering, and doesn't major in Business / Analytics.

What I liked: Assessments (Practice Quiz, Quizzes, Final Project) are very much rewarding in a sense that by the end of the assessments, you gain a better understanding on how the topics and concepts taught in the lectures could be applied in a practical sense in the world.

What I disliked:

1. The title of the course is pretty misleading. I signed up hoping to learn more about the technical side of Excel, the analysis parts, but here it seems that the emphasis are more on the relatively abstract analytical concepts, while Excel is merely a tool in the big picture.

2. It gets very frustrating and demotivating when the topics taught are not well-structured. There should be a video in the beginning to show the big picture, and a video at the end to sum up the main concepts and how they relate to each other.

von Jody P

•Nov 08, 2016

Though at the start of the program indicates no prerequisite I would suggest that you be familiar with Algebra and Stats. 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 !

It was challenging but not impossible, and if you do not challenge yourself how much are you really learning?

Best of luck!

von Xu Z

•Nov 27, 2017

Professor Egger is pretty good at explaining concepts and make the class interesting. However, even to someone with solid statistical and math background, the class seems to have a steep learning curve. Concepts and projects can be discussed in more in-depth manner. Many classmates seem to be confused during middle of class. Be prepared to study up and research a lot using google search.

On the other side, I thought that it's an Basic Excel class before staring. Apparently I'm wrong. I picked up a lot of learning on data analytics side and how to use excel to accomplish the analytical goals. This class would be useful to anyone wants to get the exposure.

von Sarah L

•Aug 19, 2019

This course was much to focused on math proofs of statistical reasoning and left the actual instruction in Excel and how to use those formulas for mere supplemental material. The organization of the course left me in tears as I was struggling to understand the math and only THEN was I shown the application of that math to something useful. This is backwards. It needs to focus on the use of excel, not handwritten math formulas and proofs, and teach us the things we actually need to learn for the exam (how to compare 2 models, etc) instead of leaving us googling for the most pertinant information while the professor drones on about greek letters.

von Derek O

•Oct 25, 2017

While I found this course useful, it was originally advertised with the Data Analytics Specialization from Duke University, and it says that you need no previous knowledge to complete the specialization. I found this course VERY challenging and I think it is because of my limited experience in the fields of statistics and calculus. There were many times when vocabulary, formulas, calculations, etc. are mentioned quickly and I felt a little left behind. With some background in business statistics I think this class would be much more effective.

von Markus S

•Nov 20, 2015

Really interesting course and material and a very good instructor. This would be a 5 stars course if it wasn't for the final project. During lessons some concepts of statistics were taken as known (which is okay). However the final project required to utilize a combination of all new learned material on a whole different level of difficulty compared to the preceding quizzes. I did not expect that jump in difficulty and enjoyed the course a lot but the final project just was a struggle.

von Stefano J N

•Apr 29, 2016

The Course is fine, explanations and videos are a bit hard to follow at times.

The final assessment is in my opinion very bad, as it i appears to me quite unrelated to the course it self. The lectures are quite abstract and the exam is a practical application of the concept.

The instructions of the course also aren't very good as you need to do each part of the final project at the end of each week.

I would strongly suggest to not take this course unless you have many spare hours.

von Kristin K

•Feb 27, 2018

The course covers some good topics, but it is not introductory due to the requirement that you have prior knowledge of statistics. I found the lectures got progressively more confusing with few examples of how to apply the knowledge. 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.

- 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