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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
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2.Reasons To Use, Project Lifecycle

What is Data Science?

What Kind of Problems can you solve?

Data Science Project Life Cycle

Data ScienceBasic Principles

Data Acquisition

Data Collection

Understanding Data Attributes in a Data, Different types of Variables

Build the Variable type Hierarchy

Two Dimensional Problem

Corelation 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? â€“ 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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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
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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 â€“ 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 Allocationpart of Disproportionate Sampling

Systematic Sampling

Cluster Sampling

2 angels of Data ScienceStatistical Learning, Machine Learning
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6,Testing And Assessment, Production Deployment And More

Multi variable analysis

linear regration

Simple linear regration

Hypothesis testing

Speculation vs. claim(Query)

Sample
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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
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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
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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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