Вернуться к Mastering Data Analysis in Excel

4.2

звезд

Оценки: 3,070

•

Рецензии: 694

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....

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.

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.

Фильтр по:

автор: Al S

•Dec 17, 2015

e

автор: Tania K

•Dec 08, 2015

.

автор: Eddie M

•Nov 16, 2018

The course is quite challenging and therefore worth doing. The materials provided are excellent and the video tutorials and Professor are excellent - but make sure you pay attention!

However, I was a bit disappointed with some of the quizzes particularly those in the final project. The penultimate quiz and peer assignment quiz do have problems. The penultimate quiz answers are not accurate and do not match the actual correct answers. You therefore need to be careful when responding and choose the answer that is closest to your result. On the peer review problem setting there are certain aspects required in your answer that are not defined in the problem. Indeed the way the problem is set out it appears that you can choose between predictive linear regression OR binary classification. Beware.

Also I would strongly recommend finishing the course work for week 3 AND 4 BEFORE taking the quiz on probability and distributions.

автор: 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.

автор: 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.

автор: 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.

автор: 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.

автор: 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 :(

автор: 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.

автор: 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).

автор: 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.

автор: Azar M

•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.

автор: 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.

автор: 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.

автор: 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.

автор: 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.

автор: 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.

автор: 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!

автор: 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.

автор: Jose D P

•Nov 13, 2015

Excelente curso, pero a veces los vídeos o materiales se no son tan expresos en explicaciones y ejercicios para facilitar el entendimiento de lo estudiado. Por ejemplo la semana 2 de matriz de clasificación por su complejidad merecía mayor detalle en las explicaciones y ejercicios de práctica

автор: Adam S

•Sep 24, 2019

The course enables me to get the knowledge about binary classification and predictive models I think with these skills I feel well-prepared to excel my skills in Data Science. The tutor explained all mentioned issues clearly however the assignment and quizzes may be written clearly.

автор: Dali S

•Mar 08, 2018

Excellent course. I am a finance professional with limited statistics knowledge. Was able to follow the lectures. The reason for the 4 stars only is that the quizzes can be confusing with missing or misplaced information. Sometimes you have to search in the forums for hints.

автор: Colin M

•Mar 24, 2017

Good course, bit disjointed at times and some of the maths is rushed through, or not really relevant. Not sure I need proofs on why standardising figures results in easier to use data, maybe just show us how to do the analysis and apply it to more examples in real life.

автор: Ka L C

•Feb 17, 2020

I have learnt a lot about creating models with training data set and testing the strength of the model on test data sets. Every quiz is designed brilliantly. The only problem is that questions in the peer-reviewed assignment are not very clear and need some fix.

автор: YEVGENIY V K

•Feb 02, 2016

Lots of good information. However, this course could be structured a lit bit better. 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.

- Искусственный интеллект для каждого
- Введение в TensorFlow
- Нейронные сети и глубокое обучение
- Алгоритмы, часть 1
- Алгоритмы, часть 2
- Машинное обучение
- Машинное обучение с использованием Python
- Машинное обучение с использованием Sas Viya
- Программирование на языке R
- Введение в программирование на MATLAB
- Анализ данных с Python
- Основы AWS: введение в облачные приложения
- Основы Google Cloud Platform
- Обеспечение надежности веб-сервисов
- Разговорный английский язык на профессиональном уровне
- Наука благополучия
- Научитесь учиться
- Финансовые рынки
- Проверка гипотез в здравоохранении
- Основы повседневного руководства

- Глубокое обучение
- Python для всех
- Наука о данных
- Прикладная наука о данных с Python
- Основы бизнеса
- Разработка архитектуры на платформе Google Cloud
- Инженерия данных на платформе Google Cloud
- От Excel до MySQL
- Продвинутое машинное обучение
- Математика в машинном обучении
- Беспилотные автомобили
- Блокчейн для организаций
- Бизнес-аналитика
- Навыки Excel для бизнеса
- Цифровой маркетинг
- Статистический анализ в здравоохранении на языке R
- Основы иммунологии
- Анатомия
- Управление инновациями и дизайн-мышление
- Основы позитивной психологии

- ИТ-поддержка Google
- Специалист IBM по привлечению клиентов
- Наука о данных IBM
- Прикладное управление проектами
- Профессиональная сертификация IBM в области прикладного ИИ
- Машинное обучение для Analytics
- Пространственный анализ данных и визуализация
- Проектирование и управление в строительстве
- Педагогический дизайн