Introduction to Business Analytics and R Programming

Objective: You will understand the fundamental concepts of Business Analytics & R. In addition to this, we will set-up the complete R environment & packages.

Topics: Introduction to Analytics, Types & Applications of Analytics, Concept of R Programming, Industry Usage, Installation of R Environment, R Products & Packages, CRAN Directory, IDE for R, R Studio, Basic R Operations, Command Line and GUI.

R Programming Concepts

Objective: You will understand the complete data & function ecosystem in R Programming. These will be done theoretically as well as with practical exercises.

Topics: Data Types in R, Vectors, Factors, Strings, Lists, Arrays, Matrices, Data Frames, Functions in R, User Defined Functions, In-built Functions, Sortings, Column Bind, Row Bind, Merge Functions, Subscripting/Subsetting in R, Use-Cases, Applications & Problem Solving.

Data Manipulation in R

Objective: You will understand the process & phases in the data cleaning process, inspection functions and manipulation packages.

Topics: Data Cleaning Process, Data Cleaning Phases, readLines Function, Data Inspection, Data Cleaning Steps, String Manipulation Functions, Type Conversion, Data Inspection, Apply() Family.

Data Import Techniques in R

Objective: You will understand data import techniqiues via direct imports, web scraping, RDBMS via ODBC, SQL Queries and R Commander.

Topics: Data Import in R – different functions, Importing Data from Spreadsheet, Reading data from Tables, read.table parameters, SAS Data sets, RODBC Package, Reading Data to ODBC Tables, Query Operations, Web Scrapping, Using R Commander.

Exploratory Data Analysis

Objective: You will understand EDA technique, its parameters, goals & important. In addition to this, they will be exhaustive coverage of EDA functions.

Topics: Exploratory Data Analysis (EDA), Implementing of EDA, EDA Functions, Packages for Data Analysis, Correlation Analysis in R, Visualizing Data, Segment Plot, HC Plot, Box Plots & Special Plots.

Data Visualization in R

Objective: You will understand the data visualization ecosystem in R Programming; including all packages, GUIs and functions.

Topics: Data Visualization, Graphical functions in R, Plotting Graphs, Customizing Graphical Parameters, Various GUIs, Spatial Analysis, Advanced Graphs, R Commander and Plugin KMGGPLOT2, Advanced Spatial Analysis.

Statistical Techniques in R

Objective: You will learn the complete process of Linear & Logistic Regression

Topics: Linear Regression, Equation for a Regression Line, Mean Calculation, Summarizing the Model, Running the Regression, Logistic Regression, Linear Regression vs Logistic Regression.

Statistical Techniques in R - II

Objective: You will understand advanced statistical functions with an emphasis on predictive analytics.

Topics: Variance, ANOVA, One-Way Variance Analysis, F-Test, T-Test, Predictive Regression, Applications, Use-Cases & Practicals.

Data Mining Concepts in R

Objective: You will understand Data Mining, Association Rules, Pruning Rules & Visulization-Association Assets.

Topics: Introduction To Data Mining, Data Mining Process, The Uses of Data Mining, Association Rule Mining, Types of Association Rules, Use of Association Rule Mining, Removing Redundancy, Interpreting Rules, Visualizing Association Rules.

Implementing Machine Learning

Objective: You will understand Machine Learning - Features, Types & Algorithms.

Topics: PIntroduction To Machine Learning, Applications of Machine Learning, Types of Machine Learning, Supervised Learning, Workflow of Supervised Learning, Applications of Supervised learning, Artificial Neural Network, Unsupervised Learning, Workflow of Unsupervised learning, Sentiment Analysis, Project Discussion: Problem Statement, Data Fields.