Business Analytics and Machine Learning with R programming

Business Analytics and Machine Learning with R programming, available at $19.99, with 45 lectures, 3 quizzes, and has 3 subscribers.

You will learn about Business Analytics R programming CRISP-DM(Cross-Industry Standard Process for Data Mining) Introduction to Statistics This course is ideal for individuals who are Students, Fresh graduates, Working Professsionals, Business Analysts or Decision Makers It is particularly useful for Students, Fresh graduates, Working Professsionals, Business Analysts or Decision Makers.

Enroll now: Business Analytics and Machine Learning with R programming

Summary

Title: Business Analytics and Machine Learning with R programming

Price: $19.99

Number of Lectures: 45

Number of Quizzes: 3

Number of Published Lectures: 45

Number of Published Quizzes: 3

Number of Curriculum Items: 48

Number of Published Curriculum Objects: 48

Original Price: $19.99

Quality Status: approved

Status: Live

What You Will Learn

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

Who Should Attend

Students, Fresh graduates, Working Professsionals, Business Analysts
Decision Makers

Target Audiences

Students, Fresh graduates, Working Professsionals, Business Analysts
Decision Makers

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:

· Business Understanding

· Data Understanding

· Data Preparation

· Modeling

· Evaluation

· 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:

· Define a variety of basic statistical terms and concepts

· Perform fundamental statistical calculations

· 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.

Course Curriculum

Chapter 1: Business Analytics

Lecture 1: Course Introduction

Lecture 2: Business Analytics

Lecture 3: Business Understanding

Lecture 4: Data understanding

Lecture 5: Data preparation

Lecture 6: Modeling

Lecture 7: Evaluation

Lecture 8: Deployment

Chapter 2: Introduction to Statistics

Lecture 1: Introduction to Statistics

Lecture 2: Measures of Central Tendency

Lecture 3: Measures of Spread

Lecture 4: Hypothesis Testing

Lecture 5: Correlation Analysis

Lecture 6: Simple Linear Regression

Lecture 7: Analysis of Variance (Anova)

Lecture 8: Multiple Linear Regression

Lecture 9: KNN(K Nearest Neighbor)

Lecture 10: Chi Square Test

Lecture 11: T Test

Lecture 12: Time Series

Lecture 13: Data Sampling

Chapter 3: Introduction to R

Lecture 1: Introduction to R

Lecture 2: R studio

Lecture 3: Data Type

Lecture 4: Variable

Lecture 5: Operators and Functions

Lecture 6: Strings

Lecture 7: Conditional Statement

Lecture 8: Loop

Lecture 9: Vectors

Lecture 10: Lists

Lecture 11: Matrices

Lecture 12: Arrays

Lecture 13: Factors

Lecture 14: Data frames

Lecture 15: Packages

Lecture 16: Import and Export Data in R

Lecture 17: Data Understanding

Lecture 18: Data Cleaning

Lecture 19: Data Formatting

Lecture 20: Data Normalization

Chapter 4: Machine Learning

Lecture 1: Case Study: KNN

Lecture 2: KNN in R Studio

Lecture 3: Cae Study : Simple Linear Regresson

Lecture 4: Simple Linear Regression in R Studio

Instructors

Batbayar Ragchaa
Consultant

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Frequently Asked Questions

How long do I have access to the course materials?

You can view and review the lecture materials indefinitely, like an on-demand channel.

Can I take my courses with me wherever I go?

Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don’t have an internet connection, some instructors also let their students download course lectures. That’s up to the instructor though, so make sure you get on their good side!

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