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Business Analytics and R Programming

Online Classes : 24 Hours


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Assignment : 30 Hours


Project : 25 Hours


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Course Price: INR 16,000.00

Introduction to Business Analytics and R Programming

Weekend Batches


# Starts
1 17th Dec 2016 Buy Now
2 24th Dec 2016 Buy Now
3 14th Jan 2017 Buy Now
4 28th Jan 2017 Buy Now

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.

Course Contents

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.

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.

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.

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.

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.

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.

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.

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.

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.

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

Topics:Introduction 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.