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Data Science Bootcamp
Our Data Science course provides an education in order to be a Data Scientist, what Harvard Business Review calls the “Sexiest job of the 21st Century”. 
Book a Demo Class
  • Instructor-Led Class Room and Online Training
  • 24*7*365 Coding Support
  • Lifetime Membership to our Coding Community
  • Week Day & Week End Batches
  • New York Curriculum in India

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Duration

Summer: 8 to 12 Weeks 

Full Time: 12 Weeks

Part Time: 24 Weeks

Next Batch

 Summer | FT: 7th Aug | 4th Sept | 9th Oct

PT:  31st Aug | 28th Sep

Careers

Data Scientist, Data Analyst, Machine Learning Engineer, Data and Analytics Engineer

Highlights

480 Hours Live Classes
200+ Coding Assignment
Loan and 0% EMI Payment Option
24/7 coding support

 

Data Science Bootcamp

Our Data Science course provides an education in order to be a Data Scientist, what Harvard Business Review calls the “Sexiest job of the 21st Century”. Graduates go into roles such as Data Scientist, Data Analyst, Data Engineer and Data Architect after our program. We’ll emphasize Python in our course and also cover data acquisition, data analysis, Pandas, prediction and machine learning, data wrangling, statistical modelling, Hadoop, SQL, No SQL and more. No prior data science experience is required.

Download Syllabus
Phase 0 : Pre Course Work
  • Environment setup (Setup Ubuntu, GitHub & Python Development Environment)
  • Fundamentals of Python & Javascript programming language
  • Fundamentals of Writing Software and System Operations (Iteration, Control Flow, Editing and Executing Files)
  • Foundations of Mathematics and Statistics for Data Science
Phase 1 - 1 : Python Basics
  • Learning Bash commands and Bash Development Environment
  • Programming Fundamentals Review: Iteration, Control flow, Python Data Structures
  • Python Modular Programming: Python Standard Library (standard libraries typically include definitions for commonly used algorithms, data structures, and mechanisms for input and output.).
  • Python Functional Programming: Anonymous Functions, Decorator Functions, Iterators, Generators, Functional Objects
  • Python Object Oriented Programming: Classes, Objects, Inheritance
  • Best Practices: Keeping it simple, DRY code, naming conventions, comments and documentation
  • Python mini project: Well- documented Python module; (those are one to two-hour mini project which could be finished in class so that the students can discuss with peers and get feedback from the instructor.)
Phase 1 - 2 : Computer Science, Beyond the basics
  • Introduction to Computer Science
  • Big O Notation, Data Structures, Sorts and Searches
  • MVC – Model Views Controller
  • Introduction to Database Management System and SQL Introduction
  • (Weekend) – Building a terminal application utilizing the MVC Design pattern and a SQL Database for persistent data
Phase 1 - 3 : Databases
  • SQL Relationships, Joins
  • CRUD operations and Introduction to APIs
  • DOM (Document Object Model)
  • Data Formats – XML, JSON, CSV
  • Object Relational Mapping, ETL Concepts
  • No-SQL (MongoDB)
  • (Weekend) – Building a terminal application with the MVC Design pattern, persisting data in SQL, and utilizing APIs to grab data in JSON format
Phase 1 - 4 : Web Scraping and Phase 1 Assessment
  • Introduction to Web Scraping using Beautiful Soup, Requests and Selenium
  • Phase 1 Assessment
Phase 2 - 1 : Hypothesis Testing and Linear Regression(s)
  • Probability Distributions
  • Hypothesis testing (test of statistical significance)
  • Ordinary Least Squares
  • BLUE Assumptions
  • Evaluating performance
  • Python Libraries – statsmodels, linalg
Phase 2 - 2 : More Regression(s), Regularizations, Optimization
  • Logistic Regression
  • Minimizing Error
  • Ridge and Lasso Regressions
  • Gradient Descent
  • Python Libraries – Scikit-learn
Phase 2 - 3 : Classification methods
  • Naive Bayes
  • KNN
  • LDA
  • Support Vector Machines
  • Cross-validation techniques for tuning
Phase 2 - 4 : Ensemble methods
  • Decision Trees
  • Bagging and boosting
  • Perceptron
  • Supervised Neural Networks
Phase 2 - 5 : Unsupervised and Deep Learning
  • K-means Clustering
  • Dimensionality Reduction
  • Auto encoding, Convolution Neural Networks
  • Recurrent Neural Networks
  • LTSM
Phase: 2 - 6 : Natural Language Processing
  • Preparing textual data using Regular Expressions
  • Entity Extraction, Lemmatization
  • Textual Classifiers (Naive Bayes, SVM)
  • Sentiment Analysis
Phase 3 : Big Data and Final Projects
  • Foundations
    • Monolithic & Micro Service Architecture
    • Cluster & Cloud Computing principles
  • Big Data
    • Map Reduce
    • Hadoop
    • Spark
  • Cloud Computing Platforms:
    • Digital Ocean, AWS
    • Google Cloud
    • Azure
  • Final Project Demo

Career Focus

Project based curriculum lets you solve real problems for companies and build a portfolio of work to showcase

Regular campus visits by company founders and industry veterans, providing tips and industry insights

Mentorship and internship programs

Resume and self-branding help from Phase 1

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