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Data Science Course Curriculum

Data science Introduction

  • Data Science motivating examples — Nate Silver, Netflix, Moneyball, okcupid, LinkedIn,
  • Introduction to Analytics, Types of Analytics,
  • Introduction to Analytics Methodology
  • Analytics Terminology, Analytics Tools
  • Introduction to Big Data
  • Introduction to Machine Learning

R software: 
1. Introduction and Overview of R Language :

  • Origin of R, Interface of R,R coding Practices
  • R Downloading and Installing R
  • Getting Help on a function
  • Viewing Documentation

2 . Data Inputting in R Data Types

  • Data Types, Data Objects, Data Structures
  • Creating a vector and vector operations
  • Sub-setting
  • Writing data
  • Reading tabular data files
  • Reading from csv files
  • Initializing a data frame
  • Selecting data frame cols by position and name
  • Changing directories
  • Re-directing R output

3 . Data Manipulation in R

  • Appending data to a vector
  • Combining multiple vectors
  • Merging data frames
  • Data transformation
  • Control structures
  • Nested Loops

splitting

  • Strings and dates
  • Handling NAs and Missing Values
  • Matrices and Arrays
  • The str Function
  • Logical operations
  • Relational operators
  • generating Random Variables
  • Accessing Variables
  • Matrix Multiplication and Inversion
  • Managing Subset of data
  • Character manipulation
  • Data aggregation
  • Subscripting

Functions and Programming in R

  • Flow Control: For loop
  • If condition
  • While conditions and repeat loop
  • Debugging tools
  • Concatenation of Data
  • Combining Vars, cbind, rbind
  • sapply, lapply, tapply functions

Basic Statistics in R :
Part-I Session 1

  • Descriptive Statistics Introduction to Advanced Data Analytics
  • Statistical inferences for various Business problems
  • Types of Variables, measures of central tendency and dispersion
  • Variable Distributions and Probability Distributions
  • Normal Distribution and Properties
  • Computing basic statistics
  • Comparing means of two samples
  • Testing a correlation for significance
  • Testing a proportion
  • Classical tests (t,z,F)
  • ANOVA
  • Summarizing Data
  • Data Munging Basics

Part-I Session 2

  • Test of Hypothesis Null/Alternative Hypothesis formulation 7
  • One Sample, two sample (Paired and Independent) T/Z Test
  • P Value Interpretation
  • Analysis of Variance (ANOVA)
  • Non Parametric Tests (Chi-Square, Kruskal-Wallis, Mann-Whitney.)

Part-I Session 3

  • Introduction to Correlation – Karl Pearson
  • Spearman Rank Correlation

Advanced Analytics with real world examples (Mini Projects)Part-II Session 1

  • Regression Theory
  • Linear regression
  • Logistic Regression Non Linear Regressions using Link functions
  • Logit Link Function
  • Binomial Propensity Modeling
  • Training-Validation approach

Part-II Session 2

  • Factor Analysis Introduction to Factor Analysis – PCA
  • Reliability Test 4
  • KMO MSA tests, Eigen Value Interpretation
  • Factor Rotation and Extraction

Part-II Session 3

  • Cluster Analysis Introduction to Cluster Techniques
  • Distance Methodologies
  • Hierarchical and Non-Hierarchical Procedures
  • K-Means clustering
  • Wards Method

Time Series Analysis Part-III Session 1

  • Introduction and Exponential Smoothening Introduction to Time Series Data and

Analysis

  • Decomposition of Time Series
  • Trend and Seasonality detection and forecasting
  • Exponential Smoothing (Single, double and triple)

Part-III Session 2

  • ARIMA Modeling Box – Jenkins Methodology
  • Introduction to Auto Regression and Moving Averages, ACF, PACF

Data Mining : Machine learning with R:Part IV Session 1

  • Introduction to Machine learning and various machine learning techniques
  • Introduction to Data Mining
  • Introduction to Text Mining
  • Text analytic Process
  • Sentiment Analysis

Part IV

  • Statistical Analysis & Data Mining/Machine Learning
  • Cluster Analysis using R-Rattle
  • Association Rule Mining
  • Predictive Modeling using Decision Trees
  • Supervised learning
  • Un- Supervised learning
  • Reinforcement learning
  • Neural Network
  • Support Vector machine

Part IV Session 3

  • Evaluating & Deploying Models Evaluating performance of Model on Training and Validation data
  • ROC, Sensitivity, Specificity, Lift charts, Error Matrix
  • Deploying models using Score options
  • Opening and Saving models using Rattle

Analytics in Excel – 3 days

  • Data Preparation and Data Exploration in Excel
  • Network Analysis using NodeXL

Data Visualization in R

  • Creating a bar chart, dot plot
  • Creating a scatter plot, pie chart
  • Creating a histogram and box plot
  • Other plotting functions
  • Plotting with base graphics
  • Plotting with Lattice graphics
  • Plotting and coloring in R

Tableau with Case studies SAS E Miner with use cases Project : Financial Project, Health care Project, Retail Project

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