Data Literacy and Business Analytics for Business Leaders

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Diogo Alves de Resende

3:47:25

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1. Introduction

1. Introduction.mp4

02:46

2. What is Data Literacy.mp4

04:32

3. Why are Data Literacy and Analytics so important.mp4

05:45

4. CASE STUDY The Monty Hall Problem.mp4

06:29

5. Course Materials.mp4

03:56

6. Link to course materials.html

7. How to get more from the course.mp4

01:52

2. Key Statistical Concepts

1. Game Plan.mp4

01:37

2. Types of Variables.mp4

03:26

3. Variable Types.html

4. Case Study Briefing - Dioguinis Pizza restaurant.mp4

04:10

5. Mean.mp4

03:44

6. EXERCISE Mean.mp4

06:31

7. Median.mp4

02:55

8. EXERCISE Median.mp4

04:00

9. Mode.mp4

02:19

10. EXERCISE Mode.mp4

09:02

11. Standard Deviation.mp4

04:54

12. EXERCISE Standard Deviation.mp4

02:48

13. Correlation.mp4

04:14

14. EXERCISE Correlation.mp4

07:49

15. Normal Distribution.mp4

04:56

16. EXERCISE Normal Distribution.mp4

11:12

3. Analytics for Business

1. Game Plan.mp4

01:01

2. Law of Large Numbers.mp4

06:12

3. Tip from Diogo Law of Large Numbers Interview question.mp4

02:27

4. Probabilities.mp4

06:14

5. EXERCISE Decision Trees and Investment Decisions.mp4

04:39

6. Statistical Significance.mp4

05:46

7. CASE STUDY Pfizer and Publication Bias.mp4

05:18

8. AB Testing.mp4

09:31

9. CASE STUDY Booking.com Experimentation Culture.mp4

03:48

4. Data-Driven Decision-Making

1. Game Plan.mp4

01:03

2. Data-Driven Decision-Making Process.mp4

04:34

3. Problem Definition.mp4

04:43

4. Acquiring the Data - Censorship Bias.mp4

04:12

5. Acquiring the Data - Simpsons Paradox.mp4

04:07

6. Acquiring the Data - BI tools.mp4

05:56

7. Analyze.mp4

04:53

8. Challenge.mp4

02:17

9. Omitted Variable Bias.mp4

02:45

10. Correlation is not Causation.mp4

05:01

11. Mutual Causality Bias.mp4

02:56

12. Self-Selection Bias.mp4

02:58

13. Confirmation Bias.mp4

02:37

14. Decide.mp4

04:37

15. Iterate.mp4

05:13

5. Public Speaking with Data

1. Game Plan.mp4

02:10

2. Key Principles of Communicating with Data.mp4

06:41

3. How to not make terrible visualizations.mp4

05:34

4. How to face challenges.mp4

05:30

5. FINAL PROJECT Data Villains Briefing.mp4

05:45

6. FINAL PROJECT Data Villains.html

Description

Improve your Data Literacy and Business Analytics. Apply Business Statistics for Data Driven Decision Making.

What You'll Learn?

Understand the definition of data literacy and its importance in business.
Gain knowledge of key statistical concepts and their application in a business context.
Learn how to read and analyze data for business purposes.
Understand the process and common pitfalls of data-driven decision-making.
Be able to identify biases and distinguish between useful and misleading data.
Learn how to effectively communicate using data, including principles of public speaking and creating visualizations.
Develop hands-on skills in using Excel or Google Sheets to analyze data.
Apply statistical concepts to real-world business case studies.
Understand the role of probabilities in investment decisions.
Become proficient in data literacy and data-driven decision-making, improving your ability to excel in a business setting.

Who is this for?

Business Leaders looking to become Data Literate

Business Leaders keen to learn about Analytics

Professionals who want to improve their Data-Driven Decision-Making

Managers who would like to know more about Public Speaking with Data

What You Need to Know?

Basic knowledge of Excel or Google Sheets

More details

Description
Data need not be overwhelming. With fundamental data literacy skills, I will help you use data decision-making frameworks and statistics to create actionable business insights and presentations. Instead of being stuck, this course will help you become confident about data-driven decision-making. And, I will show you how to make simple, clear, and compelling data presentations by the end.
Do you feel confident in your ability to work with and analyze data? According to a recent survey by Business Intelligence tool Qlik, only 24% of people feel their data skills are up to par. The number drops to just 21% for younger people.
The course is designed to give you the skills, tools, and framework you need to become data literate and improve your data-driven decision-making skills. It is structured around the definition of data literacy: the ability to read, work with, and communicate using data.
In the Key Statistical Concepts section, we'll cover the most common concepts that form the foundation of analytics tools used in businesses. After learning each concept, you'll have the opportunity to apply what you've learned in a case study involving a pizza delivery business, using either Excel or Google Sheets.
The Analytics for Business section combines learning how to read and understand data with hands-on experience analyzing data. We'll cover concepts and see how they can be applied to business, such as using probabilities for investment decisions.
The largest section of the course is dedicated to data-driven decision-making. We'll learn the process and common pitfalls through case studies, and by the end of this section, you'll be able to identify biases in arguments and distinguish between useful and misleading data.
Finally, in the Communicating with Data section, you'll learn how to effectively communicate using data. We'll cover the general principles of public speaking with data, creating visualizations, and addressing challenges. By mastering these three pillars, you'll be able to deliver engaging and effective presentations using data.
The data revolution has already had a significant impact on businesses and it shows no signs of slowing down. Join me and become a part of this exciting movement!
Who this course is for:
Business Leaders looking to become Data Literate
Business Leaders keen to learn about Analytics
Professionals who want to improve their Data-Driven Decision-Making
Managers who would like to know more about Public Speaking with Data

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Diogo is a data analytics and business analytics professional with years of experience in the field. He has expertise in various methodologies, including time series forecasting for predicting sales trends, econometrics for analyzing economic data, and machine learning for optimizing marketing campaigns. His background includes working for a major e-commerce company, where he used these techniques to drive business growth, and collaborating with the United Nations on a Mobile Money project in Lesotho, where he helped increase financial inclusion in the country. In his courses, Diogo aims to provide practical and applicable knowledge through real-life examples and datasets. For example, he often uses case studies from his own work experiences to illustrate key concepts and demonstrate their relevance in the professional world. His goal is to equip students with the skills and tools necessary to succeed in their own careers in data science.

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