Data Science with AI (Cohort-02)

Wishlist Share
Share Course
Page Link
Share On Social Media

Course Content

Introductory Class

  • Data Science (Batch-02) – Introductory Class
    02:00:44

Class 01 – Introduction to Python | Variables | Data Types

Class 02 – Conditional Statements (IF, ELIF, ELSE, Nested Conditions, Practical Examples) | Loops and Control Flow (FOR Loop, WHILE Loop, Control Statements, Nested Loops) | Environment Setup & Q&A

Class 03 – Python Lists (Introduction, Accessing & Modifying Elements, List Methods, List Comprehensions, Nested Lists) | Python Tuples (Introduction, Tuple Operations, Packing & Unpacking, Nested Tuples)

Class 04 – Python Dictionaries (Introduction, Accessing & Modifying Elements, Dictionary Methods, Nested Dictionaries, Dictionary Comprehensions) | Python Functions (Defining & Calling Functions, Parameters, Return Statements, Variable-Length Arguments)

Class 05 – Functional Programming (Real-World Function Examples, Lambda Functions, map(), filter()) | Introduction to NumPy (Purpose, Importing Modules for Data Handling)

Class 06 – NumPy (1D & 2D Arrays, Array Creation, Indexing & Slicing, Array Operations, Array Functions, Reshaping, Statistical Functions, Matrix Multiplication) | Pandas (Series & DataFrames, Reading CSVs, Accessing & Modifying Data, Handling Nulls, Sorting, Descriptive Statistics, Data Cleaning, Filtering, Grouping & Aggregation, Merging & Concatenating DataFrames)

Power BI – Introduction of Power BI – Power Query

Power BI – Text Tools | Number Tools | Reference Queries | Column Quality | Date Tools

Power BI – Conditional Columns | Column from Examples | Date Tools | Group By | Filtering & Sorting | Parameters

Power BI – Append Queries | Select from Folder | Data Normalization | Data Modelling

Power BI – Data Modelling | Star & Snowflake Schema | Data Formatting | DAX Calculated Columns (Text & Date Functions)

Power BI – Text Functions | Date Functions | Logical Operators | Logical Functions

Power BI – Logical Functions

Power BI – Measures | Implicit vs Explicit Measures | Filter Context & Flow | Aggregation Functions

Power BI – CALCULATE | ALL & FILTER Functions | Variables & Commenting

Power BI – Iterator Functions (SUMX, AVERAGEX, MINX, MAXX) | Time Intelligence Functions

Power BI – Handling Errors | Data Visualization (Navigation, Images, Cards, Formatting)

Power BI – Data Visualization (Charts, Forecasting, Reference Lines)

Power BI – Data Visualization (Filters, Tables, Matrix, Cards, Slicers, Area Chart)

Power BI – Gauge Chart | Drilling | Bookmarks & Page Navigation | Custom Tooltips | Conditional Formatting

Power BI – Project 01 – Oodles of Noodles

Power BI – Project 01 – Oodles of Noodles

Power BI – Project 02 – Market Mindz

SQL – Introduction to Databases | Clauses (SELECT, FROM, WHERE, GROUP BY) | TOP N & TOP N PERCENT

SQL – Clauses (HAVING, ORDER BY) | Order of Operations | Aggregation Functions | Numerical, Date & String Functions

SQL – Comparison & Conditional Operators | IS / IS NOT | ISNULL Function

SQL – JOINS | Data Modeling (ER Diagrams, Relationships) | Fact vs Dimension | UNION

SQL – LIKE Function | Views | Subqueries

SQL – Subqueries | Window Functions

SQL – Data Cleaning in MySQL

Class 07 – Object-Oriented Programming | NumPy & Pandas | Data Visualization with Matplotlib & Seaborn | COVID Data Analysis Project

Class 08 – OOP Concepts: Classes, Objects, Instance Variables & Methods | Single & Multiple Inheritance | Polymorphism with Method Overriding

Class 09 – OOP Continued: Polymorphism (Overriding & Interfaces) | Encapsulation (Public, Private, Protected) | Abstraction | Magic Methods | Operator Overloading

Class 10 – Completed OOP | Introduction to Streamlit (Basic Widgets & Component Development) | ML Classification Project Deployed on Streamlit

Class 11 – Streamlit Deployment Setup | GitHub Integration for Project | Live Deployment of ML Project | SQLite3 in Python: Importing, Creating DB & Tables, CRUD Operations (Create, Read, Update, Delete)

Class 12 – Logging in Python: Practical Use, Multiple Loggers & Real-World Scenarios | Introduction to Flask Framework (WSGI Architecture & Jinja Templates)

Class 13 – Flask App Structure | HTML Integration in Flask | Handling HTTP Methods: GET & POST

Class 14 – Flask App Revision (App Skeleton, HTML Integration, GET & POST) | Dynamic URLs with Variable Rules | Jinja2 Template Engine | Feature Engineering: Handling Missing Values

Class 15 – Data Encoding: Nominal, OneHot, Label, Ordinal, Target-Guided | NLP Roadmap: Tokenization & Text Preprocessing

Class 16 – Stemming (Porter & Snowball) | Lemmatization | Stopwords Removal | POS Tagging | Named Entity Recognition (NER)

Class 17 – Name Entity Recognition (NER) | One-Hot Encoding (OHE) | Bag of Words (BOW)

Class 18 – ANN Basics & Architecture | Sequential Network | Input, Hidden & Output Layers | Dense Layers (Fully Connected) | Trainable Parameters (Weights & Bias)

Class 19 – ANN Implementation Concepts | Sequential Model | Dense Layers | Activation Functions (Sigmoid, ReLU) | Forward Propagation | Bias

Class 20 – Optimizers (Adam) | Learning Rate | Dense Layers | Model Training (fit, epochs, history) | Early Stopping | TensorBoard | Saving Best Weights | Model Saving & Loading (.h5) | Predictions

Class 21 – LSTM & GRU RNN Training | End-to-End Project: Next Word Prediction | Data Collection, Preprocessing, Model Building, Training, Evaluation, and Deployment

Class 22 – Intro to Gen AI | AI/ML/DL/Gen AI Differences | LLM Models Overview | Training of OpenAI ChatGPT & LLaMA 3 (SFT) | Evolution of AI Models | Deep Dive into LangChain Ecosystem

Class 23 – LLM Apps with LCEL (Part 1) | Prompt Templates, Output Parsers, and Chaining | Chatbot Project with Conversational History and Session Handling

Class 24 – Build a chatbot with session memory (Buffer, Window, Summary) | Safe Prompts | Streaming Replies and basic tools | wrapped in a simple Streamlit/FastAPI UI; include logging and optional Redis/SQLite store.

Class 25 – Implement a full RAG pipeline (load, chunk, embed, store, retrieve, generate) with tuned embeddings | Vector DB and source citations | Deploy a Q&A app over PDFs/web pages with saved vector store and documented settings.

Class 26 – Ran a Streamlit RAG app (PDF upload → embed → retrieve → Q&A) and explored its uses | Built search agents with custom tools | LLM + Agent Executor, Streamlit UI | Live reasoning.

Class 27 – Created a Streamlit app where AI agents query SQLite via LangChain + Groq | Set up DB upload/preview | Safe SQL agent | Chat UI with history, and fixed auth/loop bugs | Final outcome: robust AI-SQL app with schema view | NL queries | and clear teaching demos.