Data Science Course in Chennai

• Understanding Data Science
• The Data Science Life Cycle
• Understanding Artificial Intelligence (AI)
• Overview of Implementation of Artificial Intelligence

• Machine Learning
• Deep Learning
• Artificial Neural Networks (ANN)
• Natural Language Processing (NLP)

• How R connected to Machine Learning
• R - as a tool for Machine Learning Implementation

• What is Python and history of Python
• Python-2 and Python-3 differences
• Install Python and Environment Setup
• Python Identifiers, Keywords and Indentation
• Comments and document interlude in Python
• Command line arguments and Getting User Input
• Python Basic Data Types and Variables

• Understanding Lists in Python
• Understanding Iterators
• Generators, Comprehensions and Lambda Expressions
• Understanding and using Ranges

• Introduction to the section
• Python Dictionaries and More on Dictionaries
• Sets and Python Sets Examples

• Reading and writing text files
• Appending to Files
• Writing Binary Files Manually and using Pickle Module

• Python user defined functions
• Python packages functions
• The anonymous Functions 
• Loops and statement in Python
• Python Modules & Packages

• What is Exception?
• Handling an exception
• try….except…else
• try-finally clause
• Argument of an Exception
• Python Standard Exceptions
• Raising an exceptions
• User-Defined Exceptions 

• What are regular expressions?
• The match Function and the Search Function
• Matching vs Searching
• Search and Replace
• Extended Regular Expressions and Wildcard

• Collections – named tuples, default dicts
• Debugging and breakpoints, Using IDEs

• Understanding different types of Data
• Understanding Data Extraction
• Managing Raw and Processed Data
• Wrangling Data using Python
• Using Mean, Median and Mode
• Variation and Standard Deviation 
• Probability Density and Mass Functions
• Understanding Conditional Probability
• Exploratory Data Analysis (EDA)
• Working with Numpy, Scipy and Pandas

• Understand what is a Machine Learning Model
• Various Machine Learning Models
• Choosing the Right Model
• Training and Evaluating the Model
• Improving the Performance of the Model

• Understanding Predictive Model
• Working with Linear Regression
• Working with Polynomial Regression
• Understanding Multi Level Models
• Selecting the Right Model or Model Selection
• Need for selecting the Right Model
• Understanding Algorithm Boosting
• Various Types of Algorithm Boosting
• Understanding Adaptive Boosting

• Understanding the Machine Learning Algorithms
• Importance of Algorithms in Machine Learning
• Exploring different types of Machine Learning Algorithms

• Supervised Learning 
• Unsupervised Learning
• Reinforcement Learning

• Understanding the Supervised Learning Algorithm
• Understanding Classifications
• Working with different types of Classifications
• Learning and Implementing Classifications

• Logistic Regression
• Naïve Bayes Classifier
• Nearest Neighbour
• Support Vector Machines (SVM)
• Decision Trees
• Boosted Trees
• Random Forest

• Time Series Analysis (TSA)
• Understanding Time Series Analysis
• Advantages of using TSA
• Understanding various components of TSA
• AR and MA Models
• Understanding Stationarity
• Implementing Forecasting using TSA

• Understanding Unsupervised Learning
• Understanding Clustering and its uses
• Exploring K-means 

• What is K-means Clustering
• How K-means Clustering Algorithm Works
• Implementing K-means Clustering

• Exploring Hierarchical Clustering

• Understanding Hierarchical Clustering
• Implementing Hierarchical Clustering

• Understanding Dimensionality Reduction

• Importance of Dimensions
• Purpose and advantages of Dimensionality Reduction
• Understanding Principal Component Analysis (PCA)
• Understanding Linear Discriminant Analysis (LDA)

• What is Hypothesis Testing in Machine Learning
• Advantages of using Hypothesis Testing 
• Basics of Hypothesis

• Normalization
• Standard Normalization

• Parameters of Hypothesis Testing

• Null Hypothesis
• Alternative Hypothesis
• The P-Value
• Types of Tests
• T Test
• Z Test
• ANOVA Test
• Chi-Square Test

• Understanding Reinforcement Learning Algorithm
• Advantages of Reinforcement Learning Algorithm
• Components of Reinforcement Learning Algorithm
• Exploration Vs Exploitation tradeoff

• What is R?
• History and Features of R
• Introduction to R Studio
• Installing R and Environment Setup
• Command Prompt 
• Understanding R programming Syntax
• Understanding R Script Files

• Data types in R
• Creating and Managing Variables
• Understanding Operators

• Assignment Operators
• Arithmetic Operators
• Relational and Logical Operators
• Other Operators

• Understanding and using Decision Making Statements

• The IF Statement
• The IF…ELSE statement
• Switch Statement

• Understanding Loops and Loop Control 

• Repeat Loop
• While Loop 
• For Loop
• Controlling Loops with Break and Next Statements

More on Data Types

• Understanding the Vector Data type

• Introduction to Vector Data type
• Types of Vectors
• Creating Vectors and Vectors with Multiple Elements
• Accessing Vector Elements

• Understanding Arrays in R

• Introduction to Arrays in R
• Creating Arrays
• Naming the Array Rows and Columns
• Accessing and manipulating Array Elements

• Understanding the Matrices in R

• Introduction to Matrices in R
• Creating Matrices
• Accessing Elements of Matrices
• Performing various computations using Matrices

• Understanding the List in R

• Understanding and Creating List 
• Naming the Elements of a List
• Accessing the List Elements
• Merging different Lists
• Manipulating the List Elements
• Converting Lists to Vectors

• Understanding and Working with Factors

• Creating Factors
• Data frame and Factors
• Generating Factor Levels
• Changing the Order of Levels

• Understanding Data Frames

• Creating Data Frames
• Matrix Vs Data Frames
• Sub setting data from a Data Frame
• Manipulating Data from a Data Frame
• Joining Columns and Rows in a Data Frame
• Merging Data Frames

• Converting Data Types using Various Functions
• Checking the Data Type using Various Functions

• Understanding Functions in R
• Definition of a Function and its Components
• Understanding Built in Functions

• Character/String Functions
• Numerical and Statistical Functions
• Date and Time Functions

• Understanding User Defined Functions (UDF)

• Creating a User Defined Function
• Calling a Function
• Understanding Lazy Evaluation of Functions

• Understanding External Data
• Understanding R Data Interfaces
• Working with Text Files
• Working with CSV Files
• Understanding Verify and Load for Excel Files
• Using WriteBin() and ReadBin() to manipulate Binary Files 
• Understanding the RMySQL Package to Connect and Manage MySQL Databases

• What is Data Visualization
• Understanding R Libraries for Charts and Graphs 
• Using Charts and Graphs for Data Visualizations
• Exploring Various Chart and Graph Types

• Pie Charts and Bar Charts
• Box Plots and Scatter Plots
• Histograms and Line Graphs

• Understanding the Basics of Statistical Analysis
• Uses and Advantages of Statistical Analysis
• Understanding and using Mean, Median and Mode
• Understanding and using Linear, Multiple and Logical Regressions
• Generating Normal and Binomial Distributions
• Understanding Inferential Statistics
• Understanding Descriptive Statistics and Measure of Central Tendency

• Understanding Packages
• Installing and Loading Packages
• Managing Packages

• Understand what is a Machine Learning Model
• Various Machine Learning Models
• Choosing the Right Model
• Training and Evaluating the Model
• Improving the Performance of the Model

• Understanding Predictive Model
• Working with Linear Regression
• Working with Polynomial Regression
• Understanding Multi Level Models
• Selecting the Right Model or Model Selection
• Need for selecting the Right Model
• Understanding Algorithm Boosting
• Various Types of Algorithm Boosting
• Understanding Adaptive Boosting

• Understanding the Machine Learning Algorithms
• Importance of Algorithms in Machine Learning
• Exploring different types of Machine Learning Algorithms

• Supervised Learning 
• Unsupervised Learning
• Reinforcement Learning

• Understanding the Supervised Learning Algorithm
• Understanding Classifications
• Working with different types of Classifications
• Learning and Implementing Classifications

• Logistic Regression
• Naïve Bayes Classifier
• Nearest Neighbor
• Support Vector Machines (SVM)
• Decision Trees
• Boosted Trees
• Random Forest
• Time Series Analysis (TSA)
• Understanding Time Series Analysis
• Advantages of using TSA
• Understanding various components of TSA
• AR and MA Models
• Understanding Stationarity
• Implementing Forecasting using TSA

• Understanding Unsupervised Learning
• Understanding Clustering and its uses
• Exploring K-means 

• What is K-means Clustering
• How K-means Clustering Algorithm Works
• Implementing K-means Clustering

• Exploring Hierarchical Clustering

• Understanding Hierarchical Clustering
• Implementing Hierarchical Clustering

• Understanding Dimensionality Reduction

• Importance of Dimensions
• Purpose and advantages of Dimensionality Reduction
• Understanding Principal Component Analysis (PCA)
• Understanding Linear Discriminant Analysis (LDA)

• What is Hypothesis Testing in Machine Learning
• Advantages of using Hypothesis Testing 
• Basics of Hypothesis

• Normalization
• Standard Normalization

• Parameters of Hypothesis Testing

• Null Hypothesis
• Alternative Hypothesis
• The P-Value
• Types of Tests
• T Test
• Z Test
• ANOVA Test
• Chi-Square Test

• Understanding Reinforcement Learning Algorithm
• Advantages of Reinforcement Learning Algorithm
• Components of Reinforcement Learning Algorithm
• Exploration Vs Exploitation tradeoff