Data science

Data Science Course Curriculum

Data science Introduction

• Data Science motivating examples -- Nate Silver, Netfilx, Money ball, 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 AnalysisPart-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

Data science training in india

data science classes hyderabad 

data science training in india