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DATA SCIENCE

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

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

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

Goal: Increase Profits of a Store

Areas of increasing the efficiency

Data Request

Business Problem: To maximize shop Profits

What are Interlinked variables

What is Strategy

Interaction b/w the Variables

Univariate analysis

Multivariate analysis

Bivariate analysis

Relation b/w Variables

Standardize Variables

What is Hypothesis?

Interpret the Correlation

Negative Correlation

Machine Learning

Contingency Table

What is Expected Value?

What is Mean?

How Expected Value is differ from Mean

Experiment – Controlled Experiment, Uncontrolled Experiment

Degree of Freedom

Dependency b/w Nominal Variable & Continuous Variable

Linear Regression

Extrapolation and Interpolation

Univariate Analysis for Linear Regression

Building Model for Linear Regression

Pattern of Data means?

Data Processing Operation

What is sampling?

Sampling Distribution

Stratified Sampling Technique

Disproportionate Sampling Technique

Balanced Allocation-part of Disproportionate Sampling

Systematic Sampling

Cluster Sampling

2 angels of Data Science-Statistical Learning, Machine Learning

linear regration

Simple linear regration

Hypothesis testing

Speculation vs. claim(Query)

Sample

Step to test your hypothesis

performance measure

Generate null hypothesis

alternative hypothesis

Testing the hypothesis

Threshold value

Hypothesis testing explanation by example

Null Hypothesis

Alternative Hypothesis

Probability

Histogram of mean value

Revisit CHI-SQUARE independence test

Correlation between Nominal Variable

Importance of Algorithms

Supervised and Unsupervised Learning

Various Algorithms on Business

Simple approaches to Prediction

Predict Algorithms

Population data

sampling

Disproportionate Sampling

Steps in Model Building

Sample the data

What is K?

Training Data

Test Data

Validation data

Model Building

Find the accuracy

Rules

Iteration

Deploy the model

Linear regression

Cluster and Clustering with Example

Data Points, Grouping Data Points

Manual Profiling

Horizontal & Vertical Slicing

Clustering Algorithm

Criteria for take into Consideration before doing Clustering

Graphical Example

Clustering & Classification: Exclusive Clustering, Overlapping Clustering, Hierarchy Clustering

Simple Approaches to Prediction

Different types of Distances: 1.Manhattan, 2.Euclidean, 3.Consine Similarity

Clustering Algorithm in Mahout

Probabilistic Clustering

Pattern Learning

Nearest Neighbor Prediction

Nearest Neighbor Analysis

How R is typically used

Features of R

Introduction to Big data

R+Hadoop

Ways to connect with R and Hadoop

Products

Case Study

Architecture

Steps for Installing RIMPALA

How to create IMPALA packages