The Complete Data Analytics Course in Excel

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Michael Parent

15:04:33

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1. Lectures

1.1 01 - GOOG - Navigation.xlsx

1. Navigation Part 1.mp4

11:24

2. Navigation Part 2.mp4

09:25

3.1 02 - Election results -Basic Functions.xlsx

3. Basic Excel Formulas.mp4

13:29

4.1 04 - SSN Internet - Data Structure.xlsx

4. Structuring Data in Excel.mp4

06:54

5.1 05 - Animals - Int. Functions.xlsx

5. Intermediate Excel Functions.mp4

13:50

6.1 06 - Orange Trees - Descriptive Stats.xlsx

6. Descriptive Statistics Part 1.mp4

07:07

7. Descriptive Statistics Part 2.mp4

11:39

8.1 Product+Recall.xlsx

8.2 Product+Recall+-+Solution.xlsx

8. Module #1 Practice & Solutions.mp4

25:29

2. Visualizations

1.1 01 - MTcars - Charts.xlsx

1. Introduction to Visualizations and Pie Charts.mp4

14:52

2. Histograms.mp4

08:31

3. Bar Charts.mp4

13:02

4. Line Charts.mp4

14:44

5. Box and Whisker.mp4

15:00

6. Radial Charts.mp4

09:54

7. Combo Charts.mp4

13:21

8. Scatter Plots.mp4

13:02

9.1 02 - Quakes - Conditional Formatting and Sparklines.xlsx

9. Conditional Formatting Part 1.mp4

08:37

10. Conditional Formatting Part 2.mp4

11:03

11. Conditional Formatting Part 3.mp4

07:18

12. Sparklines.mp4

07:29

13.1 module 2 appendix.xlsx

13. Appendix Control Charts.mp4

08:05

14.1 OJ.xlsx

14.2 OJ+-+Solution.xlsx

14. Module #2 - Practice & Solutions.mp4

18:19

3. Pivot Tables, Charts and Slicers

1.1 01 - Pivot Table - Production Data.xlsx

1. Introduction to Pivot Tables.mp4

13:30

2. Root Cause Analysis.mp4

14:10

3. Comparative Analysis.mp4

14:03

4. Pivot Charts and Slicers.mp4

11:01

5.1 Retail Sales - Solution.xlsx

5.2 Retail Sales.xlsx

5. Module #3 - Practice & Solutions.mp4

30:49

4. Statistics Hypothesis Testing and Regression

1.1 01 - Types of data.xlsx

1. Types of Data.mp4

14:57

2.1 02 - Sampling and Distributions.xlsx

2. Fundamentals of Sampling.mp4

11:29

3. Distributions Part 1.mp4

08:51

4. Distributions Part 2.mp4

10:35

5.1 03 - Hypothesis Tests.xlsx

5. Introduction to Hypothesis Testing.mp4

14:53

6.1 04 - T Tests.xlsx

6. T-tests Part 1.mp4

13:50

7. T-tests Part 2.mp4

06:48

8. T-tests Part 3.mp4

09:58

9.1 05 - Chi Square.xlsx

9. Chi-Squared Test Part 1.mp4

14:20

10. Chi-Squared Test Part 2.mp4

13:39

11.1 06 - Normality Test.xlsx

11. Test for Normality Part 1.mp4

14:21

12. Test for Normality Part 2.mp4

10:40

13.1 07 - ANOVA.xlsx

13. ANOVA Part 1.mp4

14:09

14. ANOVA Part 2.mp4

13:13

15.1 08 - Simple Regression.xlsx

15. Simple Regression Part 1.mp4

11:44

16. Simple Regression Part 2.mp4

09:38

17.1 09 - Multiple Regression.xlsx

17. Multi-Variate Regression Part 1.mp4

06:48

18. Multi-Variate Regression Part 2.mp4

10:52

19. Multi-Variate Regression Part 3.mp4

08:04

20.1 Police - Solution.xlsx

20.2 Police.xlsx

20. Module 4 - Practice & Solutions.mp4

20:55

5. Forecasting

1.1 01 - Intro to Forecasting.xlsx

1. Introduction to Forecasting.mp4

14:20

2.1 02 - Factor Forecasting - Regressions.xlsx

2. Factor Forecasting - Regression Part 1.mp4

13:27

3. Factor Forecasting - Regression Part 2.mp4

13:50

4.1 03 - Monte Carlo.xlsx

4. Factor Forecasting - Monte Carlo Simulation - Part 1.mp4

09:56

5. Factor Forecasting - Monte Carlo Simulation - Part 2.mp4

13:39

6. Factor Forecasting - Monte Carlo Simulation - Part 3.mp4

14:29

7.1 04 - Time Series - SMA and Ex. Smoothing.xlsx

7. Time Series Forecasting - The Simple Moving Average Part 1.mp4

12:30

8. Time Series Forecasting - The Simple Moving Average Part 2.mp4

05:20

9.1 05 - Time Series - Parameter Tuning.xlsx

9. Time Series Forecasting - Parameter Tuning Part 1.mp4

08:37

10. Time Series Forecasting - Parameter Tuning Part 2.mp4

13:27

11. Time Series Forecasting - Parameter Tuning Part 3.mp4

10:40

12.1 06 - Time Series - Auto Regressive Forecasting.xlsx

12. Time Series Forecasting - Auto Regression.mp4

12:29

13. Time Series Forecasting - Time Series ARMA.mp4

06:52

14. Time Series Forecasting - Time Series ARIMA.mp4

12:18

15.1 Problem Set #1 - Movies - Solution.xlsx

15.2 Problem Set #1 - Movies.xlsx

15. Module 5 - Practice Set #1 & Solutions.mp4

25:36

16.1 Problem Set #2 - Hospital - Solution.xlsx

16.2 Problem Set #2 - Hospital.xlsx

16. Module 5 - Practice Set #2 & Solutions.mp4

28:59

6. Miscellaneous Analytical Tools

1.1 01 - Goal Seek.xlsx

1. Intro and Goal Seek.mp4

04:48

2.1 02 - Scenarios.xlsx

2. Scenario Analysis.mp4

10:44

3.1 03 - Data Table.xlsx

3. Data Tables.mp4

07:20

4.1 Solver_Suite.xlsx

4. Introduction to Excel Solver Tool.mp4

09:54

5. Excel Solver Tool - The Backpack Problem.mp4

11:07

6. Excel Solver Tool - The Mixing Problem.mp4

12:53

7. Excel Solver Tool - The Traveling Salesman Problem.mp4

10:58

8.1 Coffee - Solution.xlsx

8.2 Coffee.xlsx

8. Module 6 - Practice & Solutions.mp4

34:29

Description

A complete data analysis course using the tools you already have.

What You'll Learn?

Data Analytics
Data Visualization
Hypothesis Testing
Statistics
Forecasting
Excel
Regression
Monte Carlo Simulation
Linear Programming
Pivot Tables
Business Intelligence
Excel Analytics

Who is this for?

Operations managers

Quality professionals

Industrial Engineers

Data Analysts

casual Excel users curious about data science

What You Need to Know?

Basic Math

More details

Description
Do you think you need to learn new software to perform data analysis, statistics, simulations? You don't! Everything you need to get started in the world of data analytics is already on your computer in the wonderful application called Excel.
This course will first acquaint you with the Excel environment including how to use simple and complex functions, hot-key shortcuts and navigation tips to make sure you work efficiently and effectively. From there, I lead you in lectures and practice exercises on the fundamental topics of data analytics.

Data Visualizations - Visualizing data is an important part in analyzing data as well as presenting and explaining what it means. Lectures in this course Included how to craft and use bar charts, line charts, radial charts, histograms, box and whisker charts, pie charts, conditional formatting and Sparklines.
Pivot Tables - Pivot Tables are very powerful, simple to use tools built into excel. The lectures will include how to structure data to get the most out of pivot tables, and then a few use cases including root cause analysis, comparative analysis, along with other visualization tools specific to pivot tables.
Statistics - The Statistics module is by far the most densely packed module in this course. As a statistician by training, I present a deep dive into the data behind the statistics and how to use different statistical tests based on different circumstances. The module is filled with different lectures and exercises involving ANOVA tests, T Tests, Chi-Squared Tests, Tests for Normality, Regression Analysis and more.
Forecasting - Organizations have to be able to anticipate the future. The forecasting tools presented in this course help us accomplish this feat. Forecasting is presented in two different ways - Factor forecasting and Time Series forecasting. For each approach to forecasting, many tools and techniques are introduced and reviewed including Regression analysis, Monte Carlo Simulation, Simple Moving Averages, and Auto-Regressive techniques.
Excel Tools - Excel has a lot of useful tools that don't quite fit neatly into any of the other modules. The last module of this course explores the use cases of using these tools. In particular, several archetypal problems are introduced and solved using the miscellaneous data analysis tools found in excel, including Excel Solver.
Who this course is for:
Operations managers
Quality professionals
Industrial Engineers
Data Analysts
casual Excel users curious about data science

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Michael Parent is a Lean Six Sigma Black belt and Principal at Michael Parent Consulting Company, a Management Consulting Firm. He has consulted in many functions and industries including manufacturing, HR, insurance, sales, and logistics/supply chain. Additionally, Michael is the author of the forthcoming book “The Lean Innovation Cycle” to be published by Taylor & Francis.Michael holds a degree in Industrial & Operations Engineering from the University of Michigan, Ann Arbor, an MBA from William & Mary in Williamsburg Virginia, and is received his Lean Six Sigma Master Black Belt from Lawrence Tech University in Southfield, MI.

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