Data Science Introduction and Toolbox :

• Getting Started with Github
  • Introduction to Git
  • Introduction to Github
  • Creating a Github Repository
  • Basic Git Commands
  • Basic Markdown
• Getting Started with R
  • Overview of R
  • R data types and Objects
  • Getting Data In and Out of R
  • Subsetting R Objects
  • Dates and Times
  • Control structures
  • Functions
  • Scoping rules of R
  • Coding Standards for R
  • Dates and times
  • Loop Functions
  • Vectorizing a Function
  • Debugging
  • Profiling R Code
  • Simulation

Data Extraction, Preparation and Manipulation ( R, MYSQL, HDFS, HIVE and SQOOP)

•  Data Extraction
  • Downloading Files
  • Reading Local Files
  • Reading Excel Files
  • Reading JSON
  • Reading XML
  • Reading From WEB
  • Reading From API
  • Reading From HDFS
  • Reading From MYSQL
  • SQOOP
  • Reading FROM HIVE
  • Saving and Transporting Object
  • Reading Complex Structure
• Data Preparation
  • Subsetting and Sorting
  • Summarizing Data
  • Creating New Variable
  • Regular Expression
  • Working With Dates
• Data Manipulation
  • Managing DataFrame with dplyr package
  • Reshaping Data
  • Merging Data
• Descriptive Statistics
  • Univariate Data and Bivariate Data
  • Categorical and Numerical Data
  • Frequency Histogram and Bar Charts
  • Summarizing Statistical Data
  • Box Plot, Scatter Plot, Bar Plot, Pie Chart
• Probability
  • Conditional Probability
  • Bayes Rule
  • Probability Distribution
  • Correlation vs Causation
  • Average
  • Variance
  • Outliers
• Statistical Distribution
  • Binomial Distribution
  • Central Limit Theorem
  • Normal Distribution
  • 68-95-99.7 % Rule
  • Relationship Between Binomial and Normal Distribution
• Hypothesis Testing
  • Hypothesis Testing
  • Case Studies

Inferential Statistics

• Testing of Hypothesis
• Level of Significance
• Comparison Between Sample Mean and Population Mean
• z- Test
• t- Test
• ANOVA (f- Test)
  • ANCOVA
  • MANOVA
  • MANCOVA
• Regression and Correlation
  • Regression
  • Correlation
• CHI-SQUARE

Principal Of Analytic Graph

•   Introduction to ggvis
  • Exploratory and Explainatory
  • Design Principle
  • Load ggvis and start to explore
  • Plotting System in R
  • ggvis – graphics grammar
• Lines and Syntax
  • Properties for Lines
  • Properties for Points
  • Display Model Fits
• Transformations
  • ggvis and dplyr
• HTMLWIDGET
  • Geo-Spatial Map
  • Time Series Chart
  • Network Node

Predictive Models and Machine Learning Algorithm – Supervised Regression

• Regression Analysis
  • Linear Regression
  • Non- Linear Regression
  • Polynomial Regression
  • Curvilinear Regression
• Multiple Linear Regression
  • Collect Data
  • Explore and Prepare the data
  • Train a model on the data
  • Evaluate Model Performance
  • Improve Model Performance
• Logistic Regression
  • Collect Data
  • Explore and Prepare the data
  • Train a model on the data
  • Evaluate Model Performance
  • Improve Model Performance
• Time Series Forecast
  • Collect Data
  • Explore and Prepare the data
  • Train a model on the data
  • Evaluate Model Performance
  • Improve Model Performance

Predictive Models and Machine Learning Algorithm – Supervised Classification

• Naive Bayes
  • Collect Data
  • Explore and Prepare the data
  • Train a model on the data
  • Evaluate Model Performance
  • Improve Model Performance
• Support Vector Machine
  • Collect Data
  • Explore and Prepare the data
  • Train a model on the data
  • Evaluate Model Performance
  • Improve Model Performance
• Random Forest
  • Collect Data
  • Explore and Prepare the data
  • Train a model on the data
  • Evaluate Model Performance
  • Improve Model Performance
• K- Nearest Neighbors
  • Collect Data
  • Explore and Prepare the data
  • Train a model on the data
  • Evaluate Model Performance
  • Improve Model Performance
• Classification and Regression Tree (CART)
  • Collect Data
  • Explore and Prepare the data
  • Train a model on the data
  • Evaluate Model Performance
  • Improve Model Performance

Predictive Models and Machine Learning Algorithm – Unsupervised

• K Mean Cluster
  • Collect Data
  • Explore and Prepare the data
  • Train a model on the data
  • Evaluate Model Performance
  • Improve Model Performance
• Apriori Algorithm
  • Collect Data
  • Explore and Prepare the data
  • Train a model on the data
  • Evaluate Model Performance
  • Improve Model Performance
• Case Study : Customer Analytic – Customer Lifetime Value
  • Collect Data
  • Explore and Prepare the data
  • Train a model on the data
  • Evaluate Model Performance
  • Improve Model Performance

Text Mining, Natural Language Processing and Social Network Analysis

• Natural Language Processing
  • Collect Data
  • Explore and Prepare the data
  • Train a model on the data
  • Evaluate Model Performance
  • Improve Model Performance
• Social Network Analysis
  • Collect Data
  • Explore and Prepare the data
  • Train a model on the data
  • Evaluate Model Performance
  • Improve Model Performance
• Capstone Project
  • Saving R Script
  • Scheduling R Script