Introduction to User Experience

This Course by Digitorious will helps you gain expertise in R Programming, Data Manipulation, Exploratory Data Analysis(EDA), Data Visualization, Data Mining, Regression, Sentiment Analysis and using R Studio for real life case studies on Retail, Social Media. This course is implied for every students and professionals who are interested in working in analytics industry and are keen to enhance their technical skills with exposure to cutting-edge practices.

Introduction of Business Analytics and R

  • What is Business Analytics?
  • Example and Career opportunities in Analytics
  • Installation Procedure of R Cmdr. and RStudio interface
  • Introduction to R Programming

  • Basic Mathematical Operations
  • Variables
  • Factors
  • Data Frames
  • Matrices
  • Loops and Control Statements
  • Built in Function
  • Subsetting
  • Summarizing
  • Statistical Operations

  • Introduction to Statistics
  • Central Tendency Measures
  • Variance
  • Skewness
  • Kurtosis
  • Correlation
  • probability Distribution
  • Summary Statistics
  • Importing Data

  • Importing CSV file
  • Importing text file
  • Importing excel file
  • Importing from Mysql database
  • Importing from statistical tools
  • Importing from GUI
  • WebScrapping
  • Data Manipulation

  • Apply Family
  • Aggregate function
  • DPLYR package
  • Data Reshaping
  • String Manipulation
  • Data Visualization

  • Introduction to Data Visualization
  • Univariate Graphs
  • Bivariate Graphs
  • Multivariate
  • Types of Graphs
  • Data Exploration

  • Data Preparation
  • Exploratory Data Analysis(EDA
  • Data Transformation
  • Cross Tabulation
  • Graphical analysis
  • Anomaly Detection
  • Missing Values
  • Outliers
  • Data Mining and Machine Learning

  • Introduction to Data Mining
  • KDD process
  • Application of Data Mining
  • Introduction to Machine Learning
  • Types of Machine Learning(Supervised and Unsupervised)
  • Clustering
  • Association Rule Mining and Sentiment Analysis

  • Association Rule Mining
  • Apriori's Algorithm
  • Introduction to Sentiment Analysis
  • Data Mining from Twitter
  • Predictive Modelling in R

  • Hypothesis Testing
  • Z-Test
  • T-Test
  • One Sample Test
  • Two Sample Test
  • Chi Square Test
  • Regression Modelling in R

  • Concepts of Regression Modelling
  • Linear Regression
  • Types of Linear Regression(Simple linear and Multiple Linear Regression)
  • Logistic Regression
  • Decision Tree and Random Forest

  • Decision Trees and its Terminology
  • Gini Coefficient
  • Information Gain
  • Introduction to Random Forest
  • Party package
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