Module 1 Introduction to Data Science 

• Overview of Data Science
• Importance of Data Science
• Fundamentals of R Programming 

Module 2 Data Structures

• Overview of Data Structures 
• Locating the Data Structures
• Value Allocation to the Data Structures

Module 3 Concepts of Data Manipulation

• Importance of Data Manipulation 
• dplyr Package & Execute various data manipulation operations

Module 4 Data Visualization

• Overview of Data Visualisation
• Different types of graphs
• Graphics grammar
• Univariant analysis and Ggplot2 package
• Multivariant analysis with geom_boxplot
• Barplot & Histogram
• Multivariate distribution and density plot
• Bar plots designed for the categorical variables using geop_bar() and the theme() layer

Module 5 Basics of Python 

• Overview of Python language
• Python Functions
• Indentations in Python
• Data types and operators in Python

Module 6 Concepts of Statistics

• What is Statistics & its Importance?
• Statistics classification & terminology
• Data types & Probability types
• Measures of speed and central tendency
• Correlation & Covariance
• Binary and Normal distribution
• Data Sampling and Confidence levels
• Significance levels
• Hypothesis and Parametric testing

Module 7 Understanding Logistic Regression

• What is Logistic Regression?
• Diff B/w Logistic and Linear Regression
• Poisson Regression
• Bivariate Logistic Regression
• False and true positive rate
• Math related to logistic regression
• Multivariate Logistic Regression
• Building Logistic Models
• Real-time applications of Logistic Regression

Module 8 Machine Learning Introduction

• Basics of Machine Learning 
• Supervised Learning and its Classification
• Linear Regression and mathematical concepts of linear regression
• Classification Algorithms
• Ensemble Learning techniques

Module 9 Decision Trees & Random Forest

• Techniques of Classification
• Decision Tree Induction Algorithm
• Implementation of Random Forest in R
• Diff b/w classification tree and regression tree
• Naive Bayes and SVM
• Concepts of Entropy and Gini Index
• Understand Information Gain

Module 10 Understand Unsupervised Learning

• What is Unsupervised learning?
• Concepts of Clustering
• K-means clustering and Canopy Clustering
• Hierarchical Clustering
• Clustering algorithm
• K-means clustering algorithm
• K-means theoretical concepts
• K-means process flow
• Implementing K-means 
• PCA-(Principal Component Analysis) Implementation in R
• Implementing Historical Clustering in R

Module 11 Concepts of NLP (Natural Language Processing)

• What is NLP?
• Basics of Text mining
• Importance of Text mining
• Text mining use-cases
• Pre-processing, text-classification and cleaning in Text Mining
• NPL working with text mining and Language Toolkit (NLTK)

Module 12 Denial-of-Service

• Concepts of DoS & DDoS 
• Attack Techniques of DoS & DDoS 
• Botnets
• Case Studies in DDoS
• Countermeasures for DoS/DDoS 

Module 13 Integrating Python with Spark

• Basics of Pyspark and its installation
• Need of Pyspark and its Uses
• Pyspark benefits over MapReduce
• Pyspark Applications

Module 14 Scientific Computing with Scipy

• Overview of Scipy & its Features
• Concepts of Integration, Cluster, and Signal
• Concepts of Fftpack
• Bayes Theorem

Module 15 Data Science Mathematics

• Numpy Basics
• Mathematical Functions of Numpy
• Correlation and Regression
• Probability Basics and Notation
• Joint Probabilities
• Bayes Theorem
• Understand Conditional Probability
• Sum rule and product rule

Module 16 Introduction to Keras and TensorFlow API

• Tensorflow overview 
• Open-source libraries of Tensorflow
• Deep Learning Models 
• Concepts of TPU-Tensor Processing Unit 
• Graph Visualisation and Keras
• Keras neural-network
• Define and Compose multi-complex output models using Keras
• Batch normalization concepts
• Functional and Sequential composition
• Implement neural networks with TensorFlow API
• Implement Keras using tensorboard

Module 17 Artificial Intelligence (AI) & Deep Learning

• Machine Learning Effect on AI
• Overview of Deep Learning 
• Working on Deep Learning
• Regression & Classification within Supervised Learning
• Association & Clustering in Unsupervised Learning
• Fundamentals of AI & Neural Networks
• Supervised Learning in Neural Networks & multi-layer network
• Reinforcement Learning
• Deep & Convolutional Neural Networks
• Recurrent Neural Networks
• Deep learning (GPU) graphics processing unit
• Deep Learning Applications
• Time series modeling

Module 18 RBM-Restricted Boltzmann Machine & Autoencoders

• Overview of Autoencoders
• Understanding RBM concepts
• Application of RBM for the DNN-Deep Neural Networks
• Features and Applications of Autoencoders 

Module 19 Big Data Hadoop and Spark

• Fundamentals of Big Data and Hadoop 
• Hadoop Architecture
• Concepts of HDFS
• MapReduce Framework
• What is a Pig?
• Concepts of Hive and HBase
• Overview of Scala and Functional Programming
• Spark Overview
• Spark RDD Operations and writing spark programs
• Overview of Spark streaming 
• Spark Streaming Architecture & Features
• Spark Transformations
• Structured streaming Architecture
• Structured streaming Dstreams and Spark Graphx

Module 19 Kafka Overview

• Basics of Kafka 
• Kafka Architecture
• Kafka cluster and Integrating Kafka with Flume

Module 20 Tableau

• Overview of Data Visualisation 
• Applications of Data Visualisation 
• Tableau Installation and Interface
• Tableau Data Types & Data Preparation
• Architecture of Tableau
• Get Started with Tableau
• Creating sets, Metadata and Data Blending
• Parameters and Tableau prep
• Arranging visual and data analytics
• Expressions, Stories, Mapping, and Calculations
• Graphs & charts
• Dashboards & Filters
• Integrate Tableau with Hadoop & R

Module 21 Concepts of MongoDB

• Fundamentals of MongoDB and NoSQL 
• Installing MongoDB 
• NoSQL Importance
• CRUD Operations
• Data Modeling and Management
• Data Aggregation Schema
• Data Indexing and Management
• MongoDB Security
• Collaborating with Unstructured Data

Module 22 Fundamentals of SAS

• SAS Enterprise Guide
• Functions & Operators of SAS
• Data Sets compilation & creation in SAS
• SAS Procedures
• SAS Graphs & Macros
• PROC SQL
• Advance SAS

Module 23 MS Excel

• Entering Data
• Logical Functions
• Conditional Formatting
• Data Sorting & Filtering
• Concepts of Pivot Tables
• Validation & Excel Formulas
• Creating charts & Charting techniques
• File and Data security in Excel
• VBA Macros and IF condition
• VBA Loops, IF condition, and For loop
• Debugging & Messaging in VBA