Data Science

Harvard Business Review named data scientist the "sexiest job of the 21st century".This Data Science course will cover the whole data life cycle ranging from Data Acquisition and Data Storage using R-Hadoop concepts, Applying modelling through R programming using Machine learning algorithms and illustrate impeccable Data Visualization by leveraging on 'R' capabilities.With companies across industries striving to bring their research and analysis (R&A) departments up to speed, the demand for qualified data scientists is rising.

Getting Started With Data Science And Recommender Systems

Data Science Overview

Reasons to use Data Science

Project Lifecycle

Data Acquirement

Evaluation of Input Data

Transforming Data

Statistical and analytical methods to work with data

Machine Learning basics

Introduction to Recommender systems

Apache Mahout Overview

Reasons To Use, Project Lifecycle

What is Data Science?

What Kind of Problems can you solve?

Data Science Project Life Cycle

Data Science-Basic Principles

Data Acquisition

Data Collection

Understanding Data- Attributes in a Data, Different types of Variables

Build the Variable type Hierarchy

Two Dimensional Problem

Co-relation b/w the Variables- explain using Paint Tool

Outliers, Outlier Treatment

Boxplot, How to Draw a Boxplot

Acquiring Data

Discussion on Boxplot- also Explain

Example to understand variable Distributions

What is Percentile? – Example using Rstudio tool

How do we identify outliers?

How do we handle outliers?

Outlier Treatment: Using Capping/Flooring General Method

Distribution- What is Normal Distribution

Why Normal Distribution is so popular

Uniform Distribution

Skewed Distribution

Transformation

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