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6-month programme

Data Science Bootcamp

Become a data scientist — with project-based training

  • Master data science skills with 100% real-life, project-based training.
  • Learn end-to-end project execution the way a data scientist runs it.
  • Graduate in 6 months, part time, alongside your job.
  • Placement preparation included — mock interviews and resume work.
Enrol now See the syllabus
Working professionals in a data science session

Our data scientists are hired by

Graduates from this programme now work across these teams.

GTS
Informatica
Accelerant
Tolaram Group
Snuviktechnologies
Safexpress
COLORCON
Barclays
ADP
EY
Talent Maestro
De Facto Infotech
Turnitin
Thought Focus
Atul Infotech Pvt Ltd
NetApp
Qatari Diar Real Estate Investment Co.
Tavant
Pivot2 Solutions
P
BCG

Key learnings

The libraries and tools you will actually build with.

What the 6-month track covers

Foundations first, then the analysis work that fills a data scientist's week.

01

Foundations

Start your journey in this prerequisite beginner's course by going over the fundamentals of data science and exposing you to the breadth of skills and tools in the industry professional's arsenal. In these first units, you will be introduced to the scientific programming environment, as well as the key concepts of both programming and statistical analysis.

02

Getting Started

Local Setup and Development Environment

03

SciPy Stack

NumPy, pandas and matplotlib

04

Mathematics

Statistics, Probability, Calculus and Linear Algebra

05

Data Analysis

Students will tackle a wide variety of topics under the umbrella of exploratory data analysis. Getting, cleaning, analyzing and visualizing raw data is the main job responsibility of industry data scientists. Here you will learn how to discover patterns and trends that influence your future modeling decisions.

06

Getting and Cleaning Data

Static Files, SQL, Web Scraping, APIs and Messy Data

07

Summarizing and Visualizing Data

Descriptive Statistics, Univariate and Multivariate Exploratory Data Analysis

08

Python Programming & Computer Science

Types, Flow Control, Data Structures, Functions, OOP and Time Complexity

09

Statistical Inference

Event Space, Probability, Distributions and Hypothesis Testing

Key features

What comes with the programme, beyond the classes.

Live Industry Based Projects
100% Placement Assistance
CV building
Confidence Building
Online & offline
Weekly & Weekend Classes
Interview Session
Mock Interview Session
Course Completion Certificate
Recorded Class
Group Discussion
Communication Skills
Interview Session
6 Monthe internship
Access to Class & Training Until Placement

Top skills and tools covered

Every one of these appears in the coursework, not just the brochure.

Module 1 — Python for Data Analytics

Open a section to see everything it covers.

6 sections 86 topics
01 Python Basics 18 topics
  • Need for Programming
  • Advantages of Programming
  • Overview of Python
  • Organizations using Python
  • Python Applications in Various Domains
  • Python Installation
  • Variables
  • Operands and Expressions
  • Conditional Statements
  • Loops
  • User-Defined Functions
  • Concept of Return Statement
  • Concept of name =" main "
  • Function Parameters
  • Different Types of Arguments
  • Global Variables
  • Global Keyword
  • Command Line Arguments
02 Working with Modules and Handling Exceptions 11 topics
  • Standard Libraries
  • Packages and Import Statements
  • Reload Function
  • Important Modules in Python
  • Sys Module
  • Os Module
  • Math Module
  • Working with Modules and
  • Handling Exceptions
  • Date-Time Module
  • Random Module
03 Functions and Object Oriented Programming 23 topics
  • User-Defined Functions
  • Concept of Return Statement
  • Concept of name =" main "
  • Function Parameters
  • Different Types of Arguments
  • Global Variables
  • Global Keyword
  • Variable Scope and Returning Values
  • Lambda Functions
  • Various Built-In Functions
  • Introduction to Object-Oriented Concepts
  • Built-In Class Attributes
  • Public, Protected and Private Attributes,and Methods
  • Class Variable and Instance Variable
  • Constructor and Destructor
  • Decorator in Python
  • Core Object-Oriented Principles
  • Inheritance and Its Types
  • Method Resolution Order
  • Overloading
  • Overriding
  • Getter and Setter Methods
  • Inheritance-In-Class Case Study
04 Data Manipulation using pandas 11 topics
  • Introduction to pandas
  • Data structures in pandas
  • Series
  • Data Frames
  • Importing and Exporting Files in Python
  • Basic Functionalities of a Data Object
  • Merging of Data Objects
  • Concatenation of Data Objects
  • JSON Module
  • Regular Expression
  • Exception Handling
05 Data Structure and File Operations 7 topics
  • Method of Accepting User Input and eval Function
  • Python - Files Input/Output Functions
  • Lists and Related Operations
  • Tuples and Related Operations
  • Strings and Related Operations
  • Sets and Related Operations
  • Dictionaries and Related Operations
06 Introduction to NumPy 16 topics
  • Basics of Data Analysis
  • NumPy - Arrays
  • Operations on Arrays
  • Indexing Slicing and Iterating
  • NumPy ArrayAttributes
  • Introduction to NumPy
  • Matrix Product
  • NumPy Functions
  • Functions
  • Array Manipulation
  • File Handling Using NumPy
  • Array Creation and Logic Functions
  • File Handling Using Numpy
  • Types of Joins on Data Objects
  • Data Cleaning using pandas
  • Exploring Datasets

Ready to start the bootcamp?

Tell us a little about your background and we will get you on the next batch.

Enrol now