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 Byte Academy Logo

Online Data Science Bootcamp Course

 
Call: +91 76194 15917  Connect on Whatsapp

 

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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

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Career Support

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

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I.S.A.

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

Curriculum

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
    dataset

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.