Data Science training Program makes you proficient in tools and systems used by Data Science Professionals. It includes training on Statistics, Machine Learning, Data Science, Python,. Gain hands-on exposure to key technologies including R, Python, Tableau, Hadoop, and Spark. Become an expert Data Scientist today.

• 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

• 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

• 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

• 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

• 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

• Multi variable analysis
• linear regration
• Simple linear regration
• Hypothesis testing
• Speculation vs. claim(Query)
• Sample

• 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

• 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
• R+Hadoop
• Ways to connect with R and Hadoop
• Products
• Case Study
• Architecture
• Steps for Installing RIMPALA
• How to create IMPALA packages