Data Science with R

Module 01 - Introduction to Data Science with R

Module 02 - Data Exploration

Module 03 - Data Manipulation

Module 04 - Data Visualization

Module 05 - Introduction to Statistics

Module 06 - Machine Learning

Module 07 - Logistic Regression

Module 08 - Decision Trees and Random Forest

Module 09 - Unsupervised Learning

Module 10 - Association Rule Mining and Recommendation Engines

Module 11 - Introduction to Artificial Intelligence

Module 12 - Time Series Analysis

Module 13 - Support Vector Machine (SVM)

Module 14 - Naïve Bayes

Module 15 - Text Mining

Python for Data Science

Module 01 - Introduction to Data Science using Python

Module 02 - Python basic constructs

Module 03 - Maths for DS-Statistics & Probability

Module 04 - OOPs in Python (Self paced)

Module 05 - NumPy for mathematical computing

Module 06 - Scipy for scientific computing

Module 07 - Data manipulation

Module 08 - Data visualization with Matplotlib

Module 09 - Machine Learning using Python

Module 10 - Supervised learning

Module 11 - Unsupervised Learning

Module 12 - Python integration with Spark (Self paced)

Module 13 - Dimensionality Reduction

Module 14 - Time Series Forecasting

Machine Learning

Module 01 - Introduction to Machine Learning

Module 02 - Supervised Learning and Linear Regression

Module 03 - Classification and Logistic Regression

Module 04 - Decision Tree and Random Forest

Module 05 - Naïve Bayes and Support Vector Machine (self-paced)

Module 06 - Unsupervised Learning

Module 07 - Natural Language Processing and Text Mining (self-paced)

Module 08 - Introduction to Deep Learning

Module 09 - Time Series Analysis (self-paced)

AI & Deep Learning

Module 01 - Introduction to Deep Learning and Neural Networks

Module 02 - Multi-layered Neural Networks

Module 03 - Artificial Neural Networks and Various Methods

Module 04 - Deep Learning Libraries

Module 05 - Keras API

Module 06 - TFLearn API for TensorFlow

Module 07 - Dnns (deep neural networks)

Module 08 - Cnns (convolutional neural networks)

Module 09 - Rnns (recurrent neural networks)

Module 10 - Gpu in deep learning

Module 11- Autoencoders and restricted boltzmann machine (rbm)

Module 12 - Deep learning applications

Module 13 - Chatbots

Big Data Hadoop and Spark

Module 01 - Hadoop Installation and Setup

Module 02 - Introduction to Big Data Hadoop and Understanding HDFS and MapReduce

Module 03 - Deep Dive in MapReduce

Module 04 - Introduction to Hive

Module 05 - Advanced Hive and Impala

Module 06 - Introduction to Pig

Module 07 - Flume, Sqoop and HBase

Module 08 - Writing Spark Applications Using Scala

Module 09 - Spark framework

Module 10 - RDD in Spark

Module 11 - Data Frames and Spark SQL

Module 12 - Machine Learning Using Spark (MLlib)

Module 13 - Integrating Apache Flume and Apache Kafka

Module 14 - Spark Streaming

Module 15 - Hadoop Administration – Multi-node Cluster Setup Using Amazon EC2

Module 16 - Hadoop Administration – Cluster Configuration

Module 17 - Hadoop Administration – Maintenance, Monitoring and Troubleshooting

Module 18 - ETL Connectivity with Hadoop Ecosystem (Self-Paced)

Module 19 - Project Solution Discussion and Cloudera Certification Tips and Tricks

Module 20 - Hadoop Application Testing

Module 21 - Roles and Responsibilities of Hadoop Testing Professional

Module 22 - Framework Called MRUnit for Testing of MapReduce Programs

Module 23 - Unit Testing

Module 24 - Test Execution

Module 25 - Test Plan Strategy and Writing Test Cases for Testing Hadoop Application

Tableau Desktop 10

Module 1 - Introduction to Data Visualization and Power of Tableau

Module 2 - Architecture of Tableau

Module 3 - Working with Metadata and Data Blending

Module 4 - Creation of Sets

Module 5 - Working with Filters

Module 6 - Organizing Data and Visual Analytics

Module 7 - Working with Mapping

Module 8 - Working with Calculations and Expressions

Module 9 - Working with Parameters

Module 10 - Charts and Graphs

Module 11 - Dashboards and Stories

Module 12 - Tableau Prep

Module 13 - Integration of Tableau with R and Hadoop

MongoDB

Module 01- Introduction to NoSQL and MongoDB

Module 02- MongoDB Installation

Module 03-Importance of NoSQL

Module 04-CRUD Operations

Module 05-Data Modeling and Schema Design

Module 06-Data Management and Administration

Module 07-Data Indexing and Aggregation

Module 08-MongoDB Security

Module 09-Working with Unstructured Data

MS-SQL

Module 1 - Introduction to SQL

Module 2 - Database Normalization and Entity Relationship Model

Module 3 - SQL Operators

Module 4 - Working with SQL: Join, Tables, and Variables

Module 5 - Deep Dive into SQL Functions

Module 6 - Working with Subqueries

Module 7 - SQL Views, Functions, and Stored Procedures

Module 8 - Deep Dive into User-defined Functions

Module 9 - SQL Optimization and Performance

Module 10 - Managing Data with Transact-SQL

Module 11 - Querying Data with Advanced Transact-SQL Components

Module 12 - Programming Databases Using Transact-SQL

Module 13 - Designing and Implementing Database Objects

Module 14 - Implementing Programmability Objects

Module 15 - Managing Database Concurrency

Module 16 - Optimizing Database Objects

Module 17 - Advanced Topics

Module 18 - Microsoft Courses: Study Material