Data Science for Everyone

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Takuma Kimura

9:25:46

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1 - Introduction

1 -Data Science for Everyone Materials.zip

1 -Why Do We Need Data Literacy.mp4

07:57

2 -What is Data Science.mp4

08:29

3 -Data Science Workflow.mp4

05:38

4 -Data Type.mp4

07:05

5 -DIKW Pyramid.mp4

07:23

2 - Analytics for Decision Making

1 -Data-Driven Decision Making.mp4

11:26

2 -Business Analytics.mp4

07:37

3 -Machine Learning.mp4

11:32

3 - Exploratory Data Analysis Part 1

1 -What is EDA.mp4

10:17

2 -Stevens Typology.mp4

05:52

3 -Univariate Analysis.mp4

22:28

4 -Multivariate Analysis.mp4

05:14

4 - Exploratory Data Analysis Part 2

1 -Probability Basics.mp4

14:50

2 -Conditional Probability.mp4

11:48

3 -Bayes Theorem.mp4

15:30

5 - Data Preprocessing

1 -Data Cleaning.mp4

23:32

2 -Handling Missing Data.mp4

09:50

3 -Data Transformation.mp4

08:00

4 -Data Reduction.mp4

13:26

6 - Data Visualization

1 -Data Visualization for Univariate Analysis Part 1.mp4

09:44

2 -Data Visualization for Univariate Analysis Part 2.mp4

09:07

3 -Data Visualization for Univariate Analysis Part 3.mp4

07:30

4 -Data Visualization for Bivariate Analysis.mp4

08:39

5 -Data Visualization for Higher Dimensions.mp4

07:17

7 - Diagnostic Analytics Part 1

1 -Statistical Hypothesis Testing.mp4

07:20

2 -Probability Distribution.mp4

09:50

3 -Law of Large Numbers and Central Limit Theorem.mp4

06:31

4 -Hypothesis Testing Part 1.mp4

14:36

5 -Hypothesis Testing Part 2.mp4

07:40

8 - Diagnostic Analytics Part 2

1 -t-test.mp4

10:15

2 -Two-sample t-test.mp4

10:40

3 -Chi-Squared Test.mp4

10:55

9 - Diagnostic Analytics Part 3

1 -Correlation.mp4

14:28

2 -Regression.mp4

08:29

3 -Hypothesis Testing by Correlation and Hypothesis.mp4

09:01

4 -Multiple Regression Analysis.mp4

12:36

10 - Predictive Analytics Part 1

1 -Types of Machine Learning.mp4

07:52

2 -Regression.mp4

12:56

3 -Performance Metrics of Regression Models Part 1.mp4

08:18

4 -Performance Metrics of Regression Models Part 2.mp4

09:56

11 - Predictive Analytics Part 2

1 -What is Classification.mp4

04:53

2 -Logistic Regression.mp4

04:08

3 -Decision Tree.mp4

10:48

4 -Ensemble Learning.mp4

07:50

5 -Performance Metrics of Classification Models Part 1.mp4

06:33

6 -Performance Metrics of Classification Models Part 2.mp4

07:35

12 - Cluster Analysis

1 -What is Clustering.mp4

12:03

2 -Distance-Based Clustering.mp4

10:30

3 -K-Means Clustering.mp4

11:45

4 -Example Customer Segmentation.mp4

06:24

13 - Deep Learning Part 1

1 -What is Deep Learning.mp4

09:12

2 -Perceptron.mp4

07:59

3 -Multilayer Perceptron.mp4

08:22

4 -Artiricial Neural Network.mp4

10:41

14 - Deep Learning Part 2

1 -How to Train a Neural Network.mp4

09:33

2 -Optimization.mp4

11:55

3 -Regularization Part 1.mp4

08:24

4 -Reguralization Part 2.mp4

05:37

Description

Data Science Essentials for Beginners

What You'll Learn?

Basics of data science
Basics of machine learning
Basics of statistical inference
Basics of data-driven decision making

Who is this for?

Anyone who want to learn data science

Managers who implement data-driven management

What You Need to Know?

None

More details

Description
Welcome to this course, data science for everyone. In this series of lectures, I will provide you with essentials of data science.

This course is targeted for managers who are not data scientist but need to manage data analytic projects.Â It is also targeted for managers who want to introduce data-driven management.Â So, the knowledge provided in this course is both theoretical and pragmatic, but not includes details of mathematics and coding.Â However, anyone who are beginners in data science are also welcome because this course can provide you with essentials for learning technical aspects of data science.

You will learn:
- Essentials concepts and theories for learning technical aspects of data science.
- Pragmatic knowledge for interpreting data and results of data analytics.
- Not includes mathematics details and coding.

Target Audience:
- Managers who are not data scientist but need to manage data analytic projects.
- Managers who want to introduce data-driven management.
- Anyone who are beginners in data science

This course covers the following topics.Â As you can see, the contents include fundamental concepts of data science, and basics of descriptive, diagnostic, and predictive analytics.Â This course also covers the very basics of deep learning.Â In the final two chapters, you can gain a basic but essential and robust understanding of artificial neural networks.
I hope you enjoy this course.

Contents:
- Data Literacy and DIKW
- Data-Driven Decision Making
- Exploratory Data Analysis: Probability theory, Descriptive Statistics
- Data Preprocessing
- Data Visualization
- Diagnostic Analytics: Hypothesis Testing (Theory and Methods)
- Predictive Analytics: Machine Learning, Deep Learning
Who this course is for:
Anyone who want to learn data science
Managers who implement data-driven management

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Data Science

Takuma Kimura

Instructor's Courses

Profile Summary:Dr. Takuma Kimura is an internationally recognized scholar in business and management fields. His expertise includes research in organizational behavior, and practical business analytics in human resource management and marketing. He teaches these subjects in universities and industrial companies. Professional Details:He published more than 10 academic papers in internationally prominent journals such as Journal of Business Ethics, International Journal of Management Reviews, Industrial Marketing Management.He is awarded as one of the World Top Reviewers from Publons, and as a Recognized Reviewer from European Management Journal.He is technically skilled for Statistical Analysis, Machine Learning, Data Science, Qualitative Analysis. And he has abundant knowledge in management theory, especially in organizational behavior and psychology.

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