Data Science – Fundamentals and Components – Data Scientist – Terminologies Used in Big Data Environments – Types of Digital Data – Classification of Digital Data – Introduction to Big Data – Characteristics of Data – Evolution of Big Data – Big Data Analytics – Classification of Analytics – Top Challenges Facing Big Data – Importance of Big Data Analytics – Data Analytics Tools.

Introduction to Essential Data Science Packages: Numpy: Numpy Data types, Scipy, Jupyter, Statsmodels and Pandas Package – Scikit learn, R programming . Programs : Numpy - Creation of Arrays, Indexing and Slicing Operations, Copy and View Scipy – Manipulation of mathematical functions using special package, Pandas – Creation of Series, Creation of DataFrame

Introducing Hadoop – Hadoop Overview – RDBMS versus Hadoop – HDFS (Hadoop Distributed File System): Components and Block Replication – Processing Data with Hadoop – Introduction to MapReduce – Features of MapReduce, YARN, HBASE

Data Munging: Introduction to Data Munging, Data Pipeline and Machine Learning in Python – Data Visualization Using Matplotlib – Interactive Visualization with Advanced Data Learning Representation in Python Program - Creation of various plots using pyplo

Introduction to NoSQL: Types of NoSQL Databases-Key-value store, Document store, Column family, Graph store, CAP theorem – CAP Theorem NoSQL databases, MongoDB: RDBMS Vs MongoDB – Mongo DB Database Model – Data Types, Sharding –Types of sharding, Introduction to Hive – Hive Architecture – Hive Query Language (HQL).