Data Science Full Stack

The Data Science Full Stack course covers SQL, Power BI, statistics, Python for data analysis, machine learning, deep learning, and NLP. It equips learners with practical skills in data handling, visualization, and AI techniques to work on real-world datasets and build complete Data Science solutions.

Curriculum

  • Introduction And Basics
  • Operators
  • Conditional Statements
  • While Loop
  • Lists
  • Strings
  • For Loops
  • Functions
  • Dictionary
  • Tuple
  • Set
  • Object-Oriented Programming
  • File Handling
  • Exception Handling
  • Regular Expression
  • Modules & Packages
  • Data Structures
  • Higher-Order Functions
  • Python Web Scraping
  • Virtual Environment
  • Web Application Project
  • Git And Github
  • Deployment
  • Python Package Manager
  • Statistics With Numpy
  • Data Analysis With Pandas
  • Data Visualization With Matplotlib

  • Stack
  • Queue
  • Linked List
  • Tree
  • Graph
  • List and Array
  • Swapping and Sorting
  • Searching
  • Recursion
  • Hashing
  • Strings
  • Dynamic Programming

  • Numpy
  • Pandas
  • matplotlib

  • Descriptive Statistics
  • Inferential Statistics

  • Methods Of Imputation
  • Encoding
  • Feature Scaling
  • Handling With Outliers

  • Introduction to Power BI
  • Power BI Desktop Fundamentals
  • Power Query (Data Preparation)
  • Power Pivot (Data Modeling)
  • Power View (Report Creation)
  • Power BI Visualizations
  • Power BI Services

  • Introduction
  • Operations On Table
  • Data Modifications
  • Constraints
  • Indexes
  • Views
  • Data Retrieval
  • Case Statements
  • Joins
  • Functions
  • Set Operator
  • Sub Queries
  • Stored Procedure
  • Advance Sql Topics

  • Supervised Learning Regression
  • Unsupervised Learning
  • Ensemble Models
  • Metrics
  • Over Sampling And Under Sampling
  • Cross Validation
  • Hyper Parameter Tuning

  • Text Processing
  • Noise Entity
  • Feature Engineering

  • Introduction to Deep Learning
  • Fundamentals of Neural Networks
  • Building Blocks of Neural Networks (Pooling, Padding, Activation, etc.)
  • Basic Neural Network Architectures (Perceptron, Feed Forward, Backpropagation, ANN)
  • Convolutional Neural Networks (CNN)
  • Recurrent Neural Networks (RNN)
  • Generative Adversarial Networks (GAN)

No sub-topics.

  • Bigdata
  • Pyspark
  • Data Modeling

  • Understanding Git
  • How to Use GitHub
  • Open-Source Contribution
  • Team Collaboration with GitHub
  • Creating a GitHub Profile
  • Building and Showcasing Repositories
  • Personal Portfolio with GitHub Pages
  • Growing Followers on GitHub

No sub-topics.

  • Live Kaggle Competition
  • Hands On Experience On Datasets
  • End To End Unique Projects

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Data Science Full Stack
Price: ₹45000.00
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