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Curriculum

 

1.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

2.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

3.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

4.Machine Learning In Data Science

  • Discussion about Box plot and Outlier

  • 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

5.Statistical And Analytical Methods Dealing With Data, Implementation Of Recommenders Using Apache Mahout And Transforming Data

  • Correlation b/w Nominal Variables

  • 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

6,Testing And Assessment, Production Deployment And More

  • Multi variable analysis

  • linear regration

  • Simple linear regration

  • Hypothesis testing

  • Speculation vs. claim(Query)

  • Sample

7.Business Algorithms, Simple Approaches To Prediction, Building Model, Model Deployment

  • Machine Learning

  • 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

8.Getting Started With Segmentation Of Prediction And Analysis

  • Clustering

  • 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

9.Integration Of R And Hadoop

  • R introduction

  • 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

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