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Overview

Course Description

Master’s in Data Science program will provide you with in-depth knowledge on Data Science, real-time analytics, statistical computing, SQL, parsing machine-generated data and finally the domain of Deep Learning in Artificial Intelligence. In this program, you will also learn how to leverage Big Data Analytics with Spark for Data Science.

Course Content

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

 

Student feedback

10 Reviews

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5

out of 5

Course Rating

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Farman Choudhary

Good course

Kudos to the trainer and I'm sure I will trust any other courses you guys publishes on which i'll require training.This is MUCH better and good than many courses where things just don't work and you can't continue.


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Atul Sharma

Great learning

It was a good course, learnt a lot of topics. Very practical, hands-on, and easy to digest. This course is perfect if you really want to learn. Thank you so much!


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Rehaan Roy

Amazing Course

Great course, recommended. Quickly get's you tinkering and accomplishing tasks relating Data Science. I'm now at a good place to solidify a lot of the concepts using practice projects.


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Nimisha Bindal

Good course

This is a very interesting course. It goes through many different useful techniques used nowadays for data science. Without digging too much into theoretical aspects, it provides many practical examples.


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Shobit Dubey

Great training

I am looking forward to get enrolled on other related course unders your Achademy. Loved the content of this one, I am really now well-familiar with the concepts and can do better in this field now.


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Tamanna Maheshwari

Amazing training

I enrolled myself as a novice in this course with a goal to build up myself in the field of Data Science. The Trainer is a amazing guy. I enjoyed the course throughout and learned a lot.


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Saloni Arora

Excellent

Excellent introduction into Data Science. Even for someone who had some data science background, I found the course very useful as it provides mastery in that. I recommend it.


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Shiva Spriha

Great training

Good practical examples. The FinalAssesment Exercise was really a good one to assemble all the learning in one project. I just loved the whole course and learning throughout. Great, must say!


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Arbaaz Khan

Perfect course

This is a perfect course for a broad introduction and mastering to the field of Data Science. I would recommend taking the course and Have learning. It'll provide you best content and training.


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Sushant Kamti

Excellent course

Excellent course. Precise and well organized presentation. Complete course is filled with lot of learning not only theoretical but also practical examples. Thanks to SparkAcademy for this course.


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Data Science with R

    Course Features

  • R
  • Python
  • MongoDB
  • MS-SQL
  • Machine Learning
  • AI & Deep Learning
  • Big Data Hadoop and Spark
  • Tableau
  •