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Online Data Science Bootcamp Course

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14-Week Course

14-Week Course

Live Online Sessions. Become job ready in Python in less than 3 months

Evening Sessions

Evening Sessions

Timings that suit both professionals and Students


Career Support

Free career support through our counsellors and our exclusive community on Nestria



Complete the course by paying online ₹15K, pay rest after you are hired

Companies that hire our graduates

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Something for everyone

Byte Academy has a course for everyone across all skill levels. Whether you are a complete beginner with little or no programming experience, or an experienced developer looking to add a new language to your toolkit, our offerings of full bootcamps and short courses include something that will help you take your skills to the next level.

Student A: Wants to start a career in programming

Student B: Looking to fill a specific knowledge gap in Software Engineering

Student C: Wants to advance their career

Student D: Preparing for a career change.


Byte Course Options

  • Full Stack Python
  • Full Stack Data Science
  • MERN Stack

All courses include 4-Week Internship

Python, MERN and Data Science Options


Phase 1-1: Setting up your Environment
  • Introduction and Environment
  • Python Development Environments
  • Anaconda/Jupyter Notebook and VS Code
  • Setting up python development environment
  • Version Control - Github and Git
  • Setting up your Git repository
  • Unix basics
  • Bash commands
Phase 1-2: Introduction to Python
  • Python variables, data types, input/output
  • Control flow – Conditional flow & Loops
  • Functional programming – Python Functions
  • Recursive functions
  • File handling & Exception handling
  • Object Oriented Programming: Classes, Objects, Inheritance
  • Class protocols
  • MVC (Model View Controller) Architecture
  • Weekend – Assignments on Python Basics, Functions, Classes
Phase 1-3: Data Structures
  • Introduction to Data Structures in Python
  • List, Tuple, Set, Dictionary
  • Arrays & Hash Tables
  • Stacks and Queues
  • Linked Lists
  • Tree – Binary Tree & Heap
  • Graphs
  • Weekend – Assignments on Data Structures

Phase 1- 4: Algorithms

  • Introduction to Algorithms
  • Sorting Algorithms
  • Traversal Algorithms
  • Greedy Algorithms
  • Divide and Conquer Algorithms
  • Dynamic Programming
  • Weekend: Assignments on Algorithms

Phase 1- 5: Advanced Topics

  • Python Libraries: date & time handling
  • Regular Expressions
  • JSON
  • Introduction and usage of APIs
  • Web Scraping
  • Data base – RDMS concepts
  • SQL basics
  • Using Data Base in python – Sqlite3 & MySQL
  • Phase 1 Assessment: Building a terminal application with the MVC
    Design pattern, persisting data in SQL, and utilizing APIs to grab data
    in JSON format


Phase 2 - 1: Introduction to Data Science

  • Intro to statistics
  • Descriptive statistics
  • Frequency distribution and skewness
  • Probability (permutation and combination) distributions
  • Feature Analysis (Univariate/Bivariate and Multivariate)
  • Hypothesis testing ( t-test, ANOVA, chi-squared ) using scipy module
  • Weekend Assignment Perform statistical summary and testing on a real life

Phase 2 - 2: Data Analysis and Visualization

  • Vector and matrix operations using numpy module
  • Data exploration using pandas module
  • Data splitting , aggregating and combining analysis
  • Data Visualization using matplotlib and seaborn module
  • IPL data analysis
  • Weekend Assignment – Perform Exploratory data analysis on real life dataset


Phase 2 - 3: Machine Learning

  • Intro to machine learning using sklearn module
  • Supervised learning
  • Regression algorithms (Linear / Polynomial)
  • Classification algorithms (KNN, DT,RF,LR,NVB)


Phase 3 - 1: Deeper into Machine Learning

  • Unsupervised learning
  • Dimensionality reduction and PCA
  • Hyper parameter tuning techniques
  • Minimizing error and performance tuning techniques
  • Machine learning project workflow using Network Intrusion detection dataset
  • Weekend Assignment – End to end machine learning regression or classification project on a real life dataset.


Phase 3 - 2: Deep learning

  • Introduction to Neural Networks
  • Concurrent neural networks and recurrent neural networks
  • Gesture recognition
  • Application-Natural language processing
  • Feature extraction and analysis
  • Textual classifiers (SVM, Naïve Bayes)
  • Sentiment analysis using NLTK module
  • Weekend Assignment- Applying Neural network algorithm on a real life dataset
    and evaluate its metrics.


Phase 3 - 3: Final Project Phase, Deep Dive Lectures, and Mock Interviews

  • Students will work on 1 individual project
  • Student can work on 1 group project
  • Project done in 2 week sprint
  • Final Demo of the Project.