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.

Who this course is for

Students, Fresh graduates, Working Professsionals, Business Analysts

Decision Makers

Business_Analytics_and_Machine_Learning_with_R_programming.part1.rar - 995.0 MB

Business_Analytics_and_Machine_Learning_with_R_programming.part2.rar - 267.8 MB