Retour à Mastering Data Analysis in Excel

4.2

étoiles

3,457 évaluations

•

809 avis

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.

Filtrer par :

par Gani

•Feb 06, 2016

I feel that the course does cover adequate topic to improve our mastery of data analysis in Excel. The course is more focus on the statistic and probability theory without giving adequate explanation on how to work on Excel.

par Cheng-Kang C

•Mar 28, 2019

Extremely challenging to put so many concepts into one course.

I have to do my own studying and research outside of this course to catch up with.

However, it is a good start to follow what need to be learnt for data analysis.

par Lisa Y

•Feb 25, 2016

In light of R or Python, doing statistical computing in excel is really time consuming and backward. The instruction might be OK, but I lost interest beginning of week 2 seeing how he build binary decision model in excel.

par Chester J

•Sep 03, 2017

Lectures are messy, and assume more in-depth statistical knowledge than they have led-on in the course requirements. Quiz questions are also sometimes poorly explained and requires digging into the forums for corrections.

par Thierry M

•Jun 25, 2020

It is an okay refresher for Excel basics. But not a very good course for teaching you how to make a model using excel. The statistics and probability portions of the course were okay but definitely not taught very well.

par Luis d M O

•Jan 09, 2016

Videos fine. Quizes impossible to understand unless you take previous courses and have time to go through forums and more extra staff. It takes a lot of time. Not suggested unless you have it.

par Phil M

•Feb 16, 2016

I expected far more handy real life Excel examples, not to see a teacher speaking and writing stat formulas for hours.

The idea of this course is very good, it has some room for improvement.

par Candice S

•Sep 07, 2017

This course is too focused on specific real world applications like confusion matrix and ROC curves that are very useful. I would rather spend more time learning actual features in Excel.

par Karina L

•Mar 07, 2017

Final project almost impossible to do with material taught. Also, each lecture comes with an additional spreadsheet that needs to be studied. Material taught doesn't match material tested.

par Christian H

•Jan 22, 2016

Very confusing stuff, need better clarification in course description and the way the material is transmitted to the student need to be reconsidered

But very well made and interesting.

par Rocío d C A M

•Nov 13, 2015

El curso no ense;a cosas prácticas en el análisis de la data. Además el enfoque es muy financiero, tomando en cuenta que no existe solamente el sector privado sino también el sector

par piyush g

•Mar 19, 2017

Not quite the excel actually relevant to industrial requirements. had to leave the course mid way.

There are better Excel DA courses out there. Concept presentation was good though.

par Rebekah L

•Sep 05, 2016

Very little to do with Excel, mostly focused on statistics with brief overviews of how things could be done within Excel, but very little focus on actual Excel use and knowledge.

par Smit R

•Jul 05, 2017

The lectures are poorly delivered. The quizzes are ambiguous. The files available for download are erroneous. It seemed the professor was not paid for this course. Stay Away!!

par Olga I

•Jan 14, 2019

I wish there were more quizes after each video to memorize the formulas and get a hold of them.

For the begginers this course is way too difficult to comprehend on their own.

par Hannah S

•Dec 04, 2015

Too much material, that moved too fast. Wasn't opportunity for enough feedback and explanation. I say this as someone who went to Duke and has taken similar classes at Duke!

par Ashish M

•Feb 17, 2016

The content of the course was good.

However, I did not like the presentation of the material, moreover such a topic would need to be covered by a longer duration course.

par Abdullah A M A

•Jul 08, 2016

The title is misleading .

This course has a little to do with excel , you could get some mathematical information but I do not think you will master anything

par KIM P C S

•Oct 30, 2015

The transition is from easy to advanced. It gets really confusing, not sure if it's just me. I am not following the lessons anymore so I left the course.

par Steph L

•May 02, 2016

I found Week 2: Binary Classification really confusing. It was just not clear. Specifics were not given. Definitions were not given. Poorly instructed.

par Nagraj N

•Apr 16, 2018

Course material is too heavy for a beginner. I would not be able to continue. For a new comer it is an heavy stuff taught like a brushing up course.

par Patricio L M

•May 28, 2018

Significant difference between the material covered during the class period and the examinations. It could be improved considerably. Thank you.

par Daniel R C

•Nov 23, 2015

Great idea. Unfortunately, the course is young and there were too many bugs to get past Week 2. Hopefully these will be finished my next session

par Kentaro H

•Aug 09, 2017

Very confusing. I passed this course but I felt like I didn't learn too much. I think they should include prerequisite for this course.

par Michelle V

•Mar 28, 2016

I found much of this material wasn't as applicable to excel analysis as hoped and didn't cover many of the analysis tools

- L'IA pour tous
- Introduction à TensorFlow
- Réseau de neurones et deep learning
- Algorithmes, Partie 1
- Algorithmes, Partie 2
- Apprentissage automatique
- Apprentissage automatique avec Python
- Apprentissage automatique à l'aide de SAS Viya
- La programmation en R
- Intro à la programmation avec Matlab
- Analyse des données avec Python
- Principes de base d'AWS : Going Cloud Native
- Bases de Google Cloud Platform
- Ingénierie de la fiabilité du site
- Parler un anglais professionnel
- La science du bien-être
- Apprendre à apprendre
- Marchés financiers
- Tests d'hypothèses dans la santé publique
- Bases du leadership au quotidien

- Deep Learning
- Le Python pour tous
- Science des données
- Science des données appliquée avec Python
- Bases de la gestion d'entreprise
- Architecture avec Google Cloud Platform
- Ingénierie des données sur Google Cloud Platform
- Excel à MySQL
- Apprentissage automatique avancé
- Mathématiques pour l'apprentissage automatique
- Voiture autonome
- Révolutions Blockchains pour l'entreprise
- Business Analytics
- Compétences Excel pour l'entreprise
- Marketing numérique
- Analyse statistique avec R pour la santé publique
- Bases de l'immunologie
- Anatomie
- Gestion de l'innovation et du design thinking
- Bases de la psychologie positive