﻿﻿ Data Science Training Institute in Bangalore| Data Science | Blue Ocean Learning
﻿Data Science Training in Bangalore | Data Science Course in JP Nagar

## Data Science

### 1 – 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

### 2 – Data Acquisition

• Discussion on Boxplot- also Explain
• Example to understand variable Distributions
• What is Percentile? – Example using Rstudio tool
• What is Percentile? – Example using Rstudio tool
• 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

### 3 – 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 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

### 4 – 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

### 5 – 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

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

• 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

### 7 – 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