Business Analytics and Machine Learning with R programming

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Batbayar Ragchaa

3:00:27

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1. Business Analytics

1. Course Introduction.mp4

01:01

2.1 01 - 2023.04.20.mp4

08:27

2. Business Analytics.mp4

08:27

3.1 02 - 2023.04.20.mp4

03:57

3. Business Understanding.mp4

03:57

4. Data understanding.mp4

02:19

5. Data preparation.mp4

02:21

6. Modeling.mp4

02:20

7. Evaluation.mp4

02:29

8. Deployment.mp4

02:29

9. Quiz on Business Analytics.html

2. Introduction to Statistics

1. Introduction to Statistics.mp4

06:08

2. Measures of Central Tendency.mp4

07:19

3. Measures of Spread.mp4

04:32

4. Hypothesis Testing.mp4

01:24

5. Correlation Analysis.mp4

03:11

6. Simple Linear Regression.mp4

03:19

7. Analysis of Variance (Anova).mp4

02:07

8. Multiple Linear Regression.mp4

04:25

9. KNN(K Nearest Neighbor).mp4

04:05

10. Chi Square Test.mp4

02:37

11. T Test.mp4

06:36

12. Time Series.mp4

05:52

13. Data Sampling.mp4

08:21

14. Quiz on Statistics.html

3. Introduction to R

1. Introduction to R.mp4

02:40

2. R studio.mp4

03:22

3. Data Type.mp4

04:25

4. Variable.mp4

01:58

5. Operators and Functions.mp4

04:12

6. Strings.mp4

03:35

7. Conditional Statement.mp4

02:35

8. Loop.mp4

01:58

9. Vectors.mp4

01:43

10. Lists.mp4

01:15

11. Matrices.mp4

02:00

12. Arrays.mp4

01:00

13. Factors.mp4

00:54

14. Data frames.mp4

01:28

15. Packages.mp4

03:23

16. Import and Export Data in R.mp4

03:32

17. Data Understanding.mp4

01:57

18. Data Cleaning.mp4

03:28

19. Data Formatting.mp4

01:55

20. Data Normalization.mp4

04:04

21. Quiz on R programming.html

4. Machine Learning

1.1 ASSIGNMENT-CASE STUDY-KNN.pdf

1.2 knn algorithm.zip

1.3 Loan Data.csv

1. Case Study KNN.mp4

07:40

2. KNN in R Studio.mp4

11:00

3.1 CASE STUDY-Simple Linear Regression.pdf

3.2 simple linear regression.zip

3.3 Simple Regression.xlsx

3. Cae Study Simple Linear Regresson.mp4

06:53

4. Simple Linear Regression in R Studio.mp4

05:47

Description

Business Analytics, Statistics, R programming

What You'll Learn?

Business Analytics
R programming
CRISP-DM(Cross-Industry Standard Process for Data Mining)
Introduction to Statistics

Who is this for?

Students, Fresh graduates, Working Professsionals, Business Analysts

Decision Makers

What You Need to Know?

No programming experience needed. You will learn everything from scratch

General Business Knowledge

More details

Description
This course introduces techniques of Business Analytics to transform data into business intelligence and to use analytics to create business value. Students learn to develop solutions to real-world problems through a combination of videos, case studies, technology demonstrations to analyze and interpret real data. This course consists of four 4 sections: Business Analytics, Statistics, Programming in R, and Case Study.

A. Business Analytics
Cross-industry standard process for data mining (CRISP-DM) is explained.
CRISP-DM breaks the process of data mining or analytics into six major phases:
Â· Business Understanding
Â· Data Understanding
Â· Data Preparation
Â· Modeling
Â· Evaluation
Â· Deployment

B. Statistics
Analytics professionals need to be trained to use statistical methods not only to interpret numbers but to predict future business scenarios. Statistics is a set of mathematical methods and tools that enable us to answer important questions about data. It is divided into two categories:
1. Descriptive Statistics
2. Inferential Statistics
Statistics and machine learning are two closely related areas. Statistics is an important prerequisite for applied machine learning. It helps us select, evaluate and interpret predictive models. Upon completion of this section, you will be able to:
Â· Define a variety of basic statistical terms and concepts
Â· Perform fundamental statistical calculations
Â· Use your understanding of statistical fundamentals to interpret data

C. Programming in R
In this section, you wil learn the fundamentals of R. You will learn how to use R Studio by using tools and packages like Tidyverse, DataFrames, Tibbles, operators, expressions, and data visualization, graphs, plots, and charts. Finally, you will apply your skills to guided examples involving business scenarios

D. Case Study
With two case studies, you will practice machine learning techniques.

Who this course is for:
Students, Fresh graduates, Working Professsionals, Business Analysts
Decision Makers

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Batbayar Ragchaa is an experienced consultant and lecturer in Business Analytics/Intelligence field. With 25 plus years of a career that spans management, technology, and product strategy roles, Batbayar has successfully managed several IT projects at international organizations such as the World Bank, United Nations, and Asian Development Bank.Batbayar earned an MBA from University of Bridgeport in Bridgeport, CT, USA and worked as an IT Analyst at UBS Investment Bank for 4 years.He also has 10 years of experiencing teaching Accounting Information Systems and Business Intelligence.Batbayar is well-versed in demonstrated management best practices and is a leader who has been repeatedly tapped in the workplace for technological and operational leadership of key strategic initiatives. Batbayar’s work has provided him with extensive exposure and experience in international markets and he has developed a broad global mindset as a result of working in markets around the world, including North America, Asia, and Europe. Batbayar Ragchaa presently lives in Ulaanbaatar, Mongolia.

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