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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 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

• 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

• R introduction

• How R is typically used

• Features of R

• Introduction to Big data

• Ways to connect with R and Hadoop

• Products

• Case Study

• Architecture

• Steps for Installing RIMPALA

• How to create IMPALA packages