Data Science and Analytics with AI (Cohort-01)

Class 01 – SQL Main Clauses | Order of Execution | Commenting Queries | AS & TOPN Keywords

Data Science (Batch-01) – Session 01

02:04:25

Class 02 – TOPN PERCENT | Aggregate Functions (Basic) | Numeric Functions (Basic) | DISTINCT | Date Functions | Text Functions (Basic)

Class 03 – Text Functions (Advance) | DECLARE, NVARCHAR, SET | Conditional Functions (Basic)

Class 04 – Conditional Functions (Advance) | CAST Function

Class 05 – SQL Theory (DBMS vs RDBMS, SQL Server, Instances, Databases, Data Security) | Data Normalization | Fact vs Dimension Tables | Relationships & Keys | Star vs Snowflake Schema | ER Diagrams | JOINS Theory

Class 06 – SQL JOINS | VIEWS | UNION & UNION ALL | Subqueries | Common Table Expression | CRUD Operations (INSERT, UPDATE, DELETE) | Window Functions

Class 07 – Python Basics (Punctuations, Operators, Brackets, Logical & Comparison) | Syntax & Semantics | Conditional Statements | Variables | Common Errors & Examples

Class 08 – Python Data Structures – Lists (Create, Access, Modify) | List Methods (Insert, Remove, Pop, Random, Append)

Class 09 – Python Data Types | Advanced Data Types | Operators | Conditional Statements | Practical – Employee Bonus Calculator

Class 10 – Python Loops | Iterating | Loop Controls | Nested Loops | List Comprehension | Practical – To-Do List

Class 11 – Practical Examples with Lists (Inventory & Feedback) | Tuples: Creation, Access, Operations | Mutable vs Immutable | Tuple Methods | Packing & Unpacking | Nested Tuples

Class 12 – Dictionaries: Create, Modify, Methods | Looping & Nested Dictionaries | Dictionary Comprehension | Functions: Defining, Calling, Parameters, Return Types

Class 13 – Lambda, Map, Filter Functions | Recursion | Palindrome Check

Class 14 – Python Modules & Packages | File Handling | Custom Packages | Standard Libraries | Working with Paths | Intro to OOP

Class 15 – OOP: Classes & Objects | Inheritance (Single, Multiple) | Polymorphism (Method Overriding)

Class 16 – Polymorphism (Functions & ABCs) | Encapsulation (Access Modifiers) | Abstraction | Magic Methods & Operator Overloading

Class 17 – Data Visualization (Matplotlib & Seaborn) | Pakistan Covid-19 Case Study

Class 18 – Python Visualization: Matplotlib (Line, Bar, Histogram, Pie, Scatter) | Analysis with Pandas & Matplotlib | Seaborn Visuals (BoxPlot, BarPlot, ViolinPlot, PairPlot, Heatmap) | Covid-19 Case Study (Pakistan)

Class 19 – Project: Car Price Prediction – Analyzing Data and Predicting Car Price using Pandas, NumPy, Matplotlib, SeaBorn, SkLearn (Data Manipulation and Pre-processing, Visualizing Dataset) | ExtraTreesRegressor | Random Forest Regressor

Class 20 – Project: Car Price Prediction (Advance Visualization) | Prediction Model Training | Hyperparameter Tuning | Predictions (RandomizedSearchCV)

Class 21 – Project: Car Prediction (Finalizing) | Natural Language Processing (NLP): Introduction | Tokenizing and Basic Terminology | Corpus and Documents | Vocabulary | Words

Class 22 – Roadmap to Natural Learning Processing (NLP) – Discussing Theories, Concepts and Practical Implications | Tokenizing and Basic Terminology | Corpus and Documents | Vocabulary | Words

Class 23 – Tokenization | Stemming (Snowball, RegeXp) | Wordnet Lemmatizer | Stopwords | Parts of Speech Tagging

Class 24 – Bag of Words Technique: Tokenization | Vocabulary Building | CountVectorizer | Document–Term Matrix | Stop-Words & N-Grams Impact on Feature Space

Class 25 – Named Entity Recognition (NER) using spaCy | Entity Visualization | Custom Entities | Evaluation Metrics (Precision, Recall, F1)

Class 26 – SMS Spam Detection: Data Loading & Cleaning | Text Preprocessing | TF-IDF Vectorization | Model Training (Multinomial Naive Bayes) | Evaluation (Accuracy, Confusion Matrix, Report)

Class 27 – Initiated ANN Project: Designed Feedforward Architecture (Input → Hidden → Output) | Set Activation & Loss Functions | Compiled Model with Optimizer | Trained with Epochs & Validation Split | Plotted Loss & Accuracy to Track Overfitting

Class 28 – Compared ANN vs RNN | Explained Forward & Backward Propagation | Initiated Churn Prediction Project (Environment Setup & Library Installation)

Class 29 – ANN Project Continued: Feature Engineering | Concepts of Activation Functions, Dense & Sequential Networks | Initiated Model Training and Parameter Tuning

Class 30 – ANN Project Finalization: Built and Trained Churn Prediction Model | Implemented EarlyStopping | Visualized Training with TensorBoard | Managed Logs and Saved Model with Keras (`.h5`)

Class 31 – Artificial Neural Network (ANN) Project: Churn Prediction – Finalizing and Deploying the Model

Class 32 – ANN Project Deployment: Used Hardcoded Inputs for Prediction | Developed `app.py` for Streamlit Interface | Deployed Project on GitHub and Live Testing Environment

Class 33 – Project: Salary Prediction using Regression (Data Understanding & Interpretation) | LSTM RNN: Overview, Architecture & Forget Gate Explained

Class 34 – LSTM RNN Architecture | Forget Gate | Input Gate & Candidate Memory | Output Gate

Class 35 – End-to-End LSTM & GRU RNN Project: Next Word Prediction | Data Collection & Preprocessing | Model Building & Training | Evaluation | Deployment

Class 36 – Introduction to Generative AI & LLMs | AI vs ML vs DL vs GenAI | Training Processes of ChatGPT & LLaMA 3 | Supervised Fine-Tuning | AI Model Evolution | LangChain Ecosystem Overview | Initiated Virtual Environment Setup

Class 37 – LLM App Development with LangChain LCEL | Effective Use of Language Models | PromptTemplates & OutputParsers | Message Structuring (Human/System) | Component Chaining | Clean Output Extraction | Input Simplification with Templates

Class 38 – LLM Chatbot with LangChain | Session-Based Conversation Design | Message History & Session IDs | Input Formatting with PromptTemplates | Custom System Messages | Input Variable Handling | Context Retention in Interactions

Class 39 –

Class 40 –

Class 41 –

Class 42 –

Power BI (Class 01) – Introduction of Power BI | Basics of Power Query | ETL | Basic Transformations in Power Query

Power BI (Class 02) – Text Functions | Number Functions | Basic Group By | Column Profiling, Quality and Distribution

Power BI (Class 03) – Date Tools | Categorical vs Time Series Analysis | Conditional Column | Column from Example | Orders of Operations

Power BI (Class 04) – Merge Queries | Appending Queries | Appending by Folders | Advanced Group By | Parameters

Power BI (Class 05) – Data Modelling | Star & Snow Flake Schema | Data Formatting

Power BI (Class 06) – DAX – Calculated Columns | Text Functions | Date Functions (Basic)

Power BI (Class 07) – DAX – Calculated Columns | Date Functions (Advance) | Logical Functions | Conditional Operators

Power BI (Class 08) – Advanced SWITCH Function | MID Function | Measures | Implicit vs Explicit Measures | Filter Context | Filter Flow | Aggregation Functions (SUM)

Power BI (Class 09) – Aggregation Functions | Filter Modifier Function (CALCULATE Function)

Power BI (Class 10) – CALCULATE | ALL | FILTER | Iterators (SUMX, COUNTX)

Power BI (Class 11) – Time Intelligence Functions | IFERROR | IS Functions

Power BI (Class 12) – Power BI Visual – Card Visual | Line Chart | Bar Chart

Power BI (Class 13) – Power BI Visuals – KPI Charts | Pie and Donut Chart | Table and Matrix Visuals | Filters (Basic to Advanced, TOPN) | Filters on Card Visual

Power BI (Class 14) – Power BI Visual – Slicers | Area Chart | Guage Chart | Conditional Formatting | Drill Through and Cross Filter | Multi-Row Card Visual | Tooltips | Buttons | Bookmarks

Power BI (Class 15) – Variables in DAX | Iterator Function (RANKX for Dynamic TOPN Rankings) | Advanced RANKX Function | Create Table

Power BI (Class 16) – Maven Roasters: Rounding Functions | Information Functions | Conversion Functions

Power BI (Class 17) – CALCULATE Modifier Functions

Power BI (Class 18) – Maven Roasters (SELECTCOLUMNS, ADDCOLUMNS, SUMMARIZE, Case Study (SUMMARIZE)) | Relationship Functions | Automatic Calendar Table | COALESE Function

Power BI (Class 19) – Power BI Service | PL-300 Exam Preparation

Class 43 – Tools & Agents in LangChain | Understanding Tools as External Resources | Role of Agents in Deciding Tool Usage & Timing | Strategies to Extend LLM with Real-Time Data | Laying Groundwork for Future Enhancements

Class 44 – Creating & Integrating Tools in LangChain | Custom Tools & Dynamic Agent Setup | AgentExecutor Wrapping | Streamlit UI for User Queries & Groq API Key | Defining Multiple Tools | Chain-of-Thought Reasoning | Handling Errors & Infinite Loops

Class 45 – Generative AI Project: Chat with SQL DB using LangChain SQL Toolkit & AgentType | Demo Walkthrough | Auto SQL Generation via Agents | SQLite DB Creation & Connection | MySQL Workbench Setup | Table Creation & Data Verification | Preparing Streamlit Integration for Natural Language SQL Querying

Class 46 – Chat with SQL DB: End-to-End Streamlit App Development | Developed App Supporting Both SQLite & MySQL | Fixed DB Configuration Function | Integrated User-Selected DB with SQL Agents | Displayed Query & Result in Streamlit Chat UI | Tested Agent-Generated Queries across Both DBs

Class 47 – Deployed YouTube & Website Summarization Bot with Streamlit | UI Setup (Sidebar, API Key, Summary Slider) | Content Loading via YouTube/URL Loader | Integrated ChatGroq LLM with PromptTemplate | Ran Summarizer Chain | Added Validation, UX Feedback & Structured Code

Class 48 –

Course Content

Class 01 – SQL Main Clauses | Order of Execution | Commenting Queries | AS & TOPN Keywords

Data Science (Batch-01) – Session 01

Class 02 – TOPN PERCENT | Aggregate Functions (Basic) | Numeric Functions (Basic) | DISTINCT | Date Functions | Text Functions (Basic)

Data Science (Batch-01) – Session 02

Class 03 – Text Functions (Advance) | DECLARE, NVARCHAR, SET | Conditional Functions (Basic)

Data Science (Batch-01) – Session 03

Class 04 – Conditional Functions (Advance) | CAST Function

Data Science (Batch-01) – Session 04

Class 05 – SQL Theory (DBMS vs RDBMS, SQL Server, Instances, Databases, Data Security) | Data Normalization | Fact vs Dimension Tables | Relationships & Keys | Star vs Snowflake Schema | ER Diagrams | JOINS Theory

Data Science (Batch-01) – Session 05

Class 06 – SQL JOINS | VIEWS | UNION & UNION ALL | Subqueries | Common Table Expression | CRUD Operations (INSERT, UPDATE, DELETE) | Window Functions

Data Science (Batch-01) – Session 06

Class 07 – Python Basics (Punctuations, Operators, Brackets, Logical & Comparison) | Syntax & Semantics | Conditional Statements | Variables | Common Errors & Examples

Data Science (Batch-01) – Session 07

Class 08 – Python Data Structures – Lists (Create, Access, Modify) | List Methods (Insert, Remove, Pop, Random, Append)

Data Science (Batch-01) – Session 08

Class 09 – Python Data Types | Advanced Data Types | Operators | Conditional Statements | Practical – Employee Bonus Calculator

Data Science (Batch-01) – Session 09

Class 10 – Python Loops | Iterating | Loop Controls | Nested Loops | List Comprehension | Practical – To-Do List

Data Science (Batch-01) – Session 10

Class 11 – Practical Examples with Lists (Inventory & Feedback) | Tuples: Creation, Access, Operations | Mutable vs Immutable | Tuple Methods | Packing & Unpacking | Nested Tuples

Data Science (Batch-01) – Session 11

Class 12 – Dictionaries: Create, Modify, Methods | Looping & Nested Dictionaries | Dictionary Comprehension | Functions: Defining, Calling, Parameters, Return Types

Data Science (Batch-01) – Session 12

Class 13 – Lambda, Map, Filter Functions | Recursion | Palindrome Check

Data Science (Batch-01) – Session 13

Class 14 – Python Modules & Packages | File Handling | Custom Packages | Standard Libraries | Working with Paths | Intro to OOP

Data Science (Batch-01) – Session 14

Class 15 – OOP: Classes & Objects | Inheritance (Single, Multiple) | Polymorphism (Method Overriding)

Data Science (Batch-01) – Session 15

Class 16 – Polymorphism (Functions & ABCs) | Encapsulation (Access Modifiers) | Abstraction | Magic Methods & Operator Overloading

Data Science (Batch-01) – Session 16

Class 17 – Data Visualization (Matplotlib & Seaborn) | Pakistan Covid-19 Case Study

Data Science (Batch-01) – Session 17

Class 18 – Python Visualization: Matplotlib (Line, Bar, Histogram, Pie, Scatter) | Analysis with Pandas & Matplotlib | Seaborn Visuals (BoxPlot, BarPlot, ViolinPlot, PairPlot, Heatmap) | Covid-19 Case Study (Pakistan)

Data Science (Batch-01) – Session 18

Class 19 – Project: Car Price Prediction – Analyzing Data and Predicting Car Price using Pandas, NumPy, Matplotlib, SeaBorn, SkLearn (Data Manipulation and Pre-processing, Visualizing Dataset) | ExtraTreesRegressor | Random Forest Regressor

Data Science (Batch-01) – Session 19

Class 20 – Project: Car Price Prediction (Advance Visualization) | Prediction Model Training | Hyperparameter Tuning | Predictions (RandomizedSearchCV)

Data Science (Batch-01) – Session 20

Class 21 – Project: Car Prediction (Finalizing) | Natural Language Processing (NLP): Introduction | Tokenizing and Basic Terminology | Corpus and Documents | Vocabulary | Words

Data Science (Batch-01) – Session 21

Class 22 – Roadmap to Natural Learning Processing (NLP) – Discussing Theories, Concepts and Practical Implications | Tokenizing and Basic Terminology | Corpus and Documents | Vocabulary | Words

Data Science (Batch-01) – Session 22

Class 23 – Tokenization | Stemming (Snowball, RegeXp) | Wordnet Lemmatizer | Stopwords | Parts of Speech Tagging

Data Science (Batch-01) – Session 23

Class 24 – Bag of Words Technique: Tokenization | Vocabulary Building | CountVectorizer | Document–Term Matrix | Stop-Words & N-Grams Impact on Feature Space

Data Science (Batch-01) – Session 24

Class 25 – Named Entity Recognition (NER) using spaCy | Entity Visualization | Custom Entities | Evaluation Metrics (Precision, Recall, F1)

Data Science (Batch-01) – Session 25

Class 26 – SMS Spam Detection: Data Loading & Cleaning | Text Preprocessing | TF-IDF Vectorization | Model Training (Multinomial Naive Bayes) | Evaluation (Accuracy, Confusion Matrix, Report)

Data Science (Batch-01) – Session 26

Class 27 – Initiated ANN Project: Designed Feedforward Architecture (Input → Hidden → Output) | Set Activation & Loss Functions | Compiled Model with Optimizer | Trained with Epochs & Validation Split | Plotted Loss & Accuracy to Track Overfitting

Data Science (Batch-01) – Session 27

Class 28 – Compared ANN vs RNN | Explained Forward & Backward Propagation | Initiated Churn Prediction Project (Environment Setup & Library Installation)

Data Science (Batch-01) – Session 28

Class 29 – ANN Project Continued: Feature Engineering | Concepts of Activation Functions, Dense & Sequential Networks | Initiated Model Training and Parameter Tuning

Data Science (Batch – 01) Session 29

Class 30 – ANN Project Finalization: Built and Trained Churn Prediction Model | Implemented EarlyStopping | Visualized Training with TensorBoard | Managed Logs and Saved Model with Keras (`.h5`)

Data Science (Batch-01) – Session 30

Class 31 – Artificial Neural Network (ANN) Project: Churn Prediction – Finalizing and Deploying the Model

Data Science (Batch-01) – Session 31

Class 32 – ANN Project Deployment: Used Hardcoded Inputs for Prediction | Developed `app.py` for Streamlit Interface | Deployed Project on GitHub and Live Testing Environment

Data Science (Batch-01) – Session 32

Class 33 – Project: Salary Prediction using Regression (Data Understanding & Interpretation) | LSTM RNN: Overview, Architecture & Forget Gate Explained

Data Science (Batch-01) – Session 33

Class 34 – LSTM RNN Architecture | Forget Gate | Input Gate & Candidate Memory | Output Gate

Data Science (Batch-01) – Session 34

Class 35 – End-to-End LSTM & GRU RNN Project: Next Word Prediction | Data Collection & Preprocessing | Model Building & Training | Evaluation | Deployment

Data Science (Batch-01) – Session 35

Class 36 – Introduction to Generative AI & LLMs | AI vs ML vs DL vs GenAI | Training Processes of ChatGPT & LLaMA 3 | Supervised Fine-Tuning | AI Model Evolution | LangChain Ecosystem Overview | Initiated Virtual Environment Setup

Data Science (Batch-01) – Session 36

Class 37 – LLM App Development with LangChain LCEL | Effective Use of Language Models | PromptTemplates & OutputParsers | Message Structuring (Human/System) | Component Chaining | Clean Output Extraction | Input Simplification with Templates

Data Science (Batch-01) – Session 37

Class 38 – LLM Chatbot with LangChain | Session-Based Conversation Design | Message History & Session IDs | Input Formatting with PromptTemplates | Custom System Messages | Input Variable Handling | Context Retention in Interactions

Data Science (Batch-01) – Session 38

Class 39 –

Data Science (Batch-01) – Session 39

Class 40 –