Analytics

User defined functions
Functions with default arguments
Functions with variable number of arguments
Functions returning values

Reading data from a csv file
Reading data from XML file
Saving the contents of data frame into a file

Introduction
Various kinds of sampling
Central Limit Theorem
Standard error
Confidence intervals

Mann-Whitney-Wilcoxon test
Kruskal-Wallis test
Non-parametric tests
Chi-square test

Introduction
Correlation versus Causation

Describe central tendency of data and key statistics
Describe variability and shape of data in dataset
Identify association among variables
Develop insight into missing values and outliers

Data for EDA
Data types and formats
Data quality
Data analysis types and purpose
Handling missing values in data
Data transformation for EDA

Describe different types of data in enterprises
Describe the importance of data quality & cleansing
Explain the purpose of data analysis & types
Describe method to treat missing values

Handling categorical & numerical variables
Plotting EDA graphs
Dimension reduction
Association analysis
Clustering
Factor analysis & PCA

Visualize central tendency, shape, distribution of dataset
Identify outliers and missing values
Explain the need for data transformation and types
Implement the algorithms of association & clustering
Describe factor analysis and Principal Component
Analysis (PCA) for dimensionality reduction

Visualizing structured data
Mediums of visualization - dashboards, scorecards, infographics and others
Visualizing big data types

Data story design process
Edward Tufte's and other principles
Choosing the right graphs and interaction abilities
Story development and delivery

Introduction
Traditional approach
Overview of map reduce (MR1)
System architecture of map reduce (MR1)
Introduction to YARN
Map-Reduce job execution in YARN (MR2/Hadoop 2.x)

Introduction
Job flow
Job submission
Job initialization
Job scheduling
Map task execution
Sort and shuffle
Reduce task execution
Job clean-up
Scheduler

Introduction
Map Reduce and PIG
Modes of execution in PIG
Pig client
Data types in PIG
Operators in PIG
Pig Usage
Loading data into PIG demo
Pig dialects
Transformations in PIG demo
Debugging in PIG demo
Other capabilities in PIG demo

Real time deployment introduction
System architecture
Logical deployment overview
Physical deployment overview
Real time deployment summary
Big data software and tools introduction
Streaming tools
NoSQL tools
Administration tools
Other ecosystem tools
Big data software and tools summary

Introduction
Goodness Measures
ROC Curves
Derive the accuracy of the machine learning model using Goodness Measures & ROC

Introduction
K-means
Practical Issues in Clustering
Applications
Develop models from unstructured data using clustering techniques and understand it’s limitation

Introduction
Customer Life Cycle and Customer Behavior
Understand the concepts of customer lifecycle & behaviour which aids in building better customer analytics based ML models

SVM
Neural Networks