Data Science Professional in-class training

Designed, developed and delivered by the best in the industry.

In this course, you will master the art of structuring data, storing it, analysing the information and processing it for a quantified solution. Data Science is influencing big organisations in their decision making. Be a part of quantified and qualified decision making.

Course Includes

  • Cloudera Hadoop
  • Scala
  • Apache Spark
  • Core Python with PySpark
  • Machine Learning With Python


Ideal for all Graduates

₹ 10 lacs - ₹ 17 lacs

Expected annual average salary as per industry standard

Expert Mentors

Designed, developed and delivered by industry experts with over 20 years of experience.

144 Course Hours

Classroom training, Labs, and Project.

Cloudera Hadoop

Duration: 24 hours

Intro. Big Data & Hadoop
Apache Hadoop   
Apache Hadoop Ecosystem   
Hadoop Core Components  
Hadoop Storage: HDFS
Hadoop Processing
MapReduce Framework
Cloudera’s Distribution Hadoop
CDH Architecture   
Hadoop Architecture and HDFS
HDFS Deployments: (HA) & Non-HA
HDFS (HA) Using (QJM)
Data Replication Rack-Awareness
HDFS Commands
HDFS Administration Commands
Hadoop MapReduce Framework
MapReduce Architecture
MapReduce Application Workflow
Data Locality Optimization in Hadoop
Resource Management Using YARN
YARN (MRv2) Architecture
Hive Arch. & Components
Deep Dive in Hive
Apache Sqoop, Sqoop Syntax
Cloudera Impala
Impala with Hive, HDFS, HBase


Duration: 24 hours

Functional Programming Paradigm
Introduction to Scala
Data Types and Control Structures
Functional Programming using Scala
Object Oriented Programming
Singletons and traits
Scala Indepth
Advanced Scala concepts
Extractors, Annotations & Parsing

Apache Spark

Duration: 16 hours

Introduction to Big Data and Spark
Foundation to Spark
Working with Resilient Distributed DataSets (RDD)
Spark Eco-system – Spark Streaming & Spark SQL

Core Python

Duration: 40 hours

Installing Python
The Basics of Python
Program Flow Control in Python
Lists, Ranges & Tuples in Python
The Binary number system
Python Dictionaries and Sets
Input and output (I/O) in Python
Modules and Functions in Python
Object Oriented Python
Using Databases in Python
Generators, Comprehensions and Lambda Expressions

Machine Learning with Python

Duration: 40 hours

Introduction of Data Science and Machine Learning
Introduction Python
Data Structure & Data Manipulation in Python
Statistics for Machine Learning
Simple Linear Regression
Multiple Linear Regression
Polynomial Regression
Support Vector Regression (SVR)
Decision Tree Regression
Random Forest Regression
Logistic Regression
K-Nearest Neighbors (K-NN)
Support Vector Machine (SVM)
Naive Bayes
Decision Tree Classification
Random Forest Classification
K-Means , Hierarchical Clustering
Deep Learning
Upper Confidence Bound (UCB)
Thompson Sampling
Natural Language Processing
Artificial Neural Networks

Data Science with R

R Programming

Introduction to R
Math, Variables, and Strings
Vectors and Factors
Vector operations

Data structures in R

Arrays & Matrices
Data frames

R programming fundamentals

Conditions and loops
Functions in R
Objects and Classes

Working with data in R

Reading CSV and Excel Files
Reading text files
Writing and saving data objects to file in R

Strings and Dates in R

String operations in R
Regular Expressions
Dates in R

Machine Learning with R

Machine learning vs. Statistical modeling & supervised vs. Unsupervised Learning

Machine Learning Languages, Types, and Examples
Machine Learning vs Statistical Modelling
Supervised vs Unsupervised Learning
Supervised Learning Classification
Unsupervised Learning
Supervised Learning
Understanding nearest neighbour classification

The KNN algorithm

Measuring similarity with distance
Choosing Appropriate K
Use Case
Classification Using Naïve Bayes
Classification using Decision Trees
The C5.0 decision tree algorithm
Understanding Classification Rules
Understanding Regression
Support Vector Machines
Neural Networks
Black Box Methods

Unsupervised Learning

Association Rules – Pattern detection
K-Means Clustering plus Advantages & Disadvantages
Hierarchical Clustering plus Advantages & Disadvantages
Measuring the Distances Between Clusters – Single Linkage Clustering
Measuring the Distances Between Clusters – Algorithms for Hierarchy Clustering
Density-Based Clustering
Evaluating Model Performance
Improving Model Performance

Tableau Desktop

Introduction to Tableau Desktop
Connecting to Data
Customizing a Data Source

Filtering Your Data
Sorting Your Data
Creating Groups in Your Data
Creating Hierarchies in Your Data
Working with Date Fields: Discrete and Continuous
Working with Date Fields: Custom Dates
Working with Multiple Measures: Dual Axis and
Combo Charts
Working with Multiple Measures: Combined Axis
Showing Relationships between Numerical Values
Mapping Data Geographically
Using Crosstabs: Totals and Aggregation

Using Crosstabs: Highlight Tables

Using Crosstabs: Heat Maps
Using Calculations: Customize Your Data
Using Calculations: Working with Strings, Dates, and Type Conversion Functions
Using Calculations: Working with Aggregations
Using Quick Table Calculations to Analyze Data
Showing Breakdowns of the Whole
Highlighting Data with Reference Lines
Create a Dashboard: Combining Your Views
Create a Dashboard: Add Actions for Interactivity
Sharing Your Work

Working with a Data Extract

Joining Tables
Blending Multiple Data Sources
Blending Data without a Common Field
Using Split and Custom Split
Advanced Calculations: Aggregating Dimensions
Controlling Table Calculations
Showing the Biggest and Smallest Values
Using Level of Detail Expressions
Filtering and LOD Expressions
Using Parameters to Control Data in the View
Parameters: Swap Measures

Using Sets to Highlight Data

Advanced Mapping: Modifying Locations
Advanced Mapping: Customizing Tableau’s Geocoding
Advanced Mapping: Using a Background Image
Viewing Distributions
Comparing Measures Against a Goal
Showing Statistics and Forecasting
Telling Stories with Data


  • Immersive real-time project learning
  • Project-based use cases & development
  • Collaborative learning and coding
  • Coding challenges
  • Lifetime access to learning portal with reading material and course videos


What will you learn?
  • Use Microsoft Excel to explore data
  • Use Transact-SQL to query a relational database
  • Create data models and visualise data using Excel or Power BI
  • Apply statistical methods to data
  • Use R or Python to explore and transform data
  • Follow data science methodology
  • Create and validate machine learning models with Azure Machine Learning
  • Write R or Python code to build machine learning models
  • Apply data science techniques to common scenarios
  • Implement a machine learning solution for a given data problem


  • What background is required for the program?

We do not require the specific background, if you are familiar with computer science you can join in this course.

  • What GPA is required for admission into the program?

Not required, any graduate are eligible

  • Has he delivered any programmes on Data Science earlier?

Yes, he has delivered on all the trainings which required for the data science course

  • What is faculty experience on data science course and his track record in the training industry?

He has 5 years of experience on Data Science and overall he has 25 years of experience in IT trainings.

  • Where can I get help related to my Couse if any?

Always you can reach us for the queries and for technical queries faculty will help you and also you can post queries on chat on our web.

Admission / Registration

  • What are the documents you required for the admission?

ID and Address proof, mobile no, e-mail ID.

  • Is any fees offer on Early Registration?

We offer Rs.5000/- on free amount for the early registrants

  • What are the Instalments conditions?

Pay 30 % of the fee amount at the time of registration to block the seat and remaining within 15 days from date of batch starting date.

  • What is best price that you can offer if I pay in a single payment?

We give Rs.10000/- as discount for single payment registrants.

  • Do we offer conditional admission?

The payment made towards the course is non-refundable but we can help the student to continue with the next batch is available seats.

  • What is the admission cancellation Policy?

As a part of our registration policy we don’t entertain any cancellation or refunds.

Programme / Training

  • What is the total duration of the program?

Total Duration is 400 Hrs.

  • Is the program completely class room?

Yes, the training will be class room training.

  • Does the program offer any scholarship?

No, it is a Training institute and we don’t offer any scholarship.

  • Is the program STEM majors eligible?

Yes, after completion of the training you will be able perform like a master in Statistics, Maths & Computer Science.

  • Which batch should I choose, morning, afternoon, evening or weekend?

It is based on your convenient, if you are a working professional we suggest for the weekend batch.


  • Is your programme offer the certification on the course?

No, but the pattern and the course outline covers all the required certification

  • Where do I apply for certification of the course after the training and is any fee for that?

Microsoft is the platform for issuing the certification on Data Science.

  • What are the career advantages with the certification?

The companies gives importance to IT certifications candidates as the business expect they are skilled candidates in a rapidly changing field.

  • Do you provide any participate certificate?

Yes, upon completion of the training we will provide the participation certificate.

  • Where do I submit the participation certificate?

The participation certificate is not to submit, but it helps the job seeker that they are masters in set of standards.

Career / Placements

  • Do you provide any placement after the training?

No, but surely we will give the career advice and also will provide reference if any.

  • Do you provide any consultation for job after the course?

No, we are not associates with any consultancy.

  • What kind of career can I expect after the course as a data scientist?

The businesses believe that Data Scientist are the asset of the organization as the organization offer good salaries for Data Scientist.

  • What is the demand of a Data Scientist in the industry?

There is huge demand for Data Scientist in the market. In the market today demand of the data scientist is 10:6 (Demand: Supply)

  • What are industries that are looking for Data Scientist?

Not only the IT workplace but other industries are like Insurance, Banking, Pharmacy, FMCG industries etc.. Are also looking for Data Scientists.

Enquire Now

Course Includes

  • Cloudera Hadoop
  • Scala
  • Apache Spark
  • Core Python with PySpark
  • Machine Learning With Python

Become a Data Scientist