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## Data Science Training Institute : ### Data Science Course Content

Introduction, Data Science Overview, Recommender Overview

• Introduction

• Data Science Overview

• Use Cases

• Project Lifecycle

• Data Acquisition

• Evaluating Input Data

• Data Transformation

• Data Analysis and Statistical methods

• Fundamentals of Machine Learning

• Recommender Overview

• Basic Introduction to Apache Mahout

• 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 Data Acquisition

• 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

Machine Learning

• Discussion about Boxplot 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

Data Analysis and Statistical Methods, Implementing Recommenders with Apache Mahout, Data Transformation

• 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 Experimentation and Evaluation, Production Deployment and Beyond

• Multi variable analysis

• 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

Various Algorithms on Business, Simple approaches to Prediction, Model Building, Deploy the model

• 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

Prediction & Analysis Segmentation

• 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 ## About Ascent Data Science Training Institute :

Ascent Data Science Training Institute Providing high quality training at affordable fees is our core value. We offers classroom on niche technologies which are in high demand. All our trainers are IT professionals with rich experience. We work with our students in developing the right skills they need to build their career in present competitive environment. We have flexible batch times to suit the timings of graduating students and working professionals.

Ascent Data Science Training Institute Excellent Trainers and Fabulous Lab this is what you can expect from us.Free training materials, Free Career consulting, Career guidance by experienced IT professionals, Pleasant and modern training rooms.

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