PG Certificate Program in Data Science & Machine Learning

Creating UDF's in Python
Lambda's and its applications
Applications of lambda - map, reduce, filter
Object, Methods and Classes
Creating modules in Python
Exception handling in Python
Regular expressions in Python
File operations in Python

Understanding the need for NumPy package
Creating arrays in NumPy
NumPy Indexing and Slicing
Array operations in NumPy
Copies and Views
Broadcasting
Array shape Manipulation & Array sorting

Understanding the pandas library
Creating panda series and data frames
Operations on series and data frames

Basic Indexing
Label, Integer and mixed Indexing
Boolean Indexing
Operations on indexes
Using .loc and .iloc operators

Grouping of data
Merging and joining data frames
Pivoting and reshaping data

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

Introduction
Hive vs RDBMS
Hive Architecture
Hive Components
Hive Schema Model
Hive Integration with Hadoop
Hive Query Language
Transformation in Hive
Hive Database Creation - Demo
Hive Tables - Demo
Hive Queries - Demo
Advanced Hive
Partitioning Demo
Bucketing Demo
Advanced Concepts Demo
Manage an XML or JSON files - Demo
Use a predefined SERDE - Demo
Summary

Apache Sqoop Introduction
Sqoop Usage
Working with Sqoop – Demo
Advanced Sqoop
Hive Integration – Demo
Hbase Integration – Demo
Sqoop scripts - Demo
Apache Sqoop Summary
Apache Oozie Introduction
Oozie Client
Basic Workflow Setup
Types of Oozie Actions
Control Statements
Defining a Workflow
Run MapReduce with Oozie - Demo
Apache Oozie Summary
Apache Hue Introduction
Hue User Interface
Working with Hive using Hue
Working with Pig using Hue
Monitoring an Oozie Job using Hue
Apache Hue - Demo
Apache Hue Summary

Introduction
Categories of NoSQL Databases
Hbase Evolution
Hbase vs RDBMS
Hbase Architecture
Hbase Components
Column Family
Hbase Fundamentals
Hbase Client
Basic CRUD Operation – Demo
Basic CRUD Operation
Zookeeper
Zookeeper – Demo
Summary

Serialization Introduction
Uses of Serialization
Serialization Techniques
Serialization Summary
Compression Introduction
Uses of Compression
Compression Techniques
Compression - Demo
Compression Summary

Scala Programming Language
Introduction to Scala
Scala Basic Data Types
Scala Collections
Scala Functions
Scala Classes and Objects
Spark
Introduction to Spark
Spark SQL
Spark Streaming

Introduction
Apache Flume Introduction
Flume Core Components
Flume Basic Configuration
Launch Flume
Apache Flume – Demo
Apache Spark and Storm Introduction
Storm Concepts
Spark Streaming Concepts
Deployment Architecture

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

What is ML, Need for ML, classification of ML algorithms- supervised and unsupervised learning.
Types of ML algorithms

Classification and Regression – understanding classification and regression techniques with case studies

Introduction to Scikit- learn package in python.
Implementing ML algorithms in python using Scikit-learn

Linear, Logistic regression; Decision trees
Support Vector Machines. Python hand on using Scikit-learn

Creating training models using ML algorithms and deploying the models,
Understand how to deploy models after training

Basics of text processing, lexical processing, syntax and semantics of text processing,
other problems in text analytics

Introduction and applications of NLP.
Learning the business use cases of Natural language processing. Statistical NLP and text similarity,
syntax and parsing techniques, text summarization techniques, semantics and generation

Diagnostics of Linear Regression
Binary Logistic Regression

Introduction to Exploratory Data Analysis(EDA)
Importance of EDA in Data Science domain
Introduction Key EDA techniques

Basic concepts in business statistics
Sampling techniques & samples
Frequency distribution and central tendency Variability & shape

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

Handling categorical & numerical variables
Visualization in EDA
Dimension reduction
Association analysis
Clustering
Factor analysis & Principal Component Analysis

Visual Communication Design
Components of visual communication
Datasets and Graphs
Layout and Formatting
Classification of Visualization

Mediums of visualizing structured data –
Dashboards
Scorecards
Infographics
Metrics and KPIs
Visualizing big data

Data story and infographic design process
Tufte's design principle
Story telling with data
Story development and delivery