Business Analytics And Machine Learning With R Programming

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

Requirements

No programming experience needed. You will learn everything from scratch

General Business Knowledge

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 AnalyticsCross-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· DeploymentB. StatisticsAnalytics 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 Statistics2. Inferential StatisticsStatistics 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 dataC. Programming in RIn 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 scenariosD. Case StudyWith two case studies, you will practice machine learning techniques.

Overview

Section 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

Section 2: Introduction to Statistics

Lecture 9 Introduction to Statistics

Lecture 10 Measures of Central Tendency

Lecture 11 Measures of Spread

Lecture 12 Hypothesis Testing

Lecture 13 Correlation Analysis

Lecture 14 Simple Linear Regression

Lecture 15 Analysis of Variance (Anova)

Lecture 16 Multiple Linear Regression

Lecture 17 KNN(K Nearest Neighbor)

Lecture 18 Chi Square Test

Lecture 19 T Test

Lecture 20 Time Series

Lecture 21 Data Sampling

Section 3: Introduction to R

Lecture 22 Introduction to R

Lecture 23 R studio

Lecture 24 Data Type

Lecture 25 Variable

Lecture 26 Operators and Functions

Lecture 27 Strings

Lecture 28 Conditional Statement

Lecture 29 Loop

Lecture 30 Vectors

Lecture 31 Lists

Lecture 32 Matrices

Lecture 33 Arrays

Lecture 34 Factors

Lecture 35 Data frames

Lecture 36 Packages

Lecture 37 Import and Export Data in R

Lecture 38 Data Understanding

Lecture 39 Data Cleaning

Lecture 40 Data Formatting

Lecture 41 Data Normalization

Section 4: Machine Learning

Lecture 42 Case Study: KNN

Lecture 43 KNN in R Studio

Lecture 44 Cae Study : Simple Linear Regresson

Lecture 45 Simple Linear Regression in R Studio

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