Data Science and Analytics with AI (Cohort-05)

SQL Pre-Requisite Videos

Introduction of Data Analytics | Databases | Type of Databases – Session 01

01:04:31
Introduction of SQL | Basic SQL Clauses (SELECT, FROM) – Session 02

01:01:02
WHERE and GROUP BY Clauses in SQL – Session 03 (Part 1)

58:08
WHERE, GROUP BY (Revision), HAVING, ORDER BY – Session 3 (Part 2)

44:51
Aggregation (SUM, COUNT, AVG) | Numeric (ROUND, CEILING, FLOOR) – Session 04

01:00:11
Date Types & Parts | Date Function (DATEDIFF, GETDATE, DATEADD) – Session 05

56:14
String Functions (CONCAT, LEFT, UPPER, LOWER, REPLACE) – Session 06

56:14
String Functions (RIGHT, SUBSTRING, Dynamic String Functions – Session 07

50:23
Comparison Operators | Conditional Functions (AND, OR, IS) – Session 08

01:00:35
Conditional Functions (LIKE, IIF Function) | Analytic Buckets – Session 09

59:40
Conditional Functions (Nested IIF, CASE Statement) – Sessio 10

53:43
Basics of Data Normalization – Session 11

30:10
Foundations of SQL Joins | ERD Diagram | Fact vs Dim Tables – Session 12

55:34
Advanced Analysis with JOINS | Exporting Results | RIGHT JOIN – Session 13

01:03:53
Star and Snowflake Schema | Appending Data (UNION and UNION ALL) – Session 14

01:13:23
Creating VIEWS in SQL Server | Exporting SQL Results to POWER BI – Session 15

59:57
Sub Queries (Scalar, Multi Rows, Derived Column) – Session 16

56:20
Sub Queries (Derived Column , Derived Table & Co-Related) – Session 17

01:10:42
Advanced Co-Related Sub Query | Common Table Expressions (CTE) – Session 18

01:06:33
Derived Table | Windows Function (OVER, PARTITION BY, ORDER BY) – Session 19

01:09:47
WINDOW Functions (ROW_NUMBER, RANK, DENSE_RANK, LAG) – Session 20

01:12:32
Introduction of MySQL Software | Data Analysis using MySQL – Session 21

01:02:02
WINDOWS Functions | Sub Queries (Column/ Table) – Session 22

01:05:10

Power BI Lectures

Session 01

02:26:44
Session 02

01:55:45
Session 03 – Power Query (Basic, Text & Number Tools)

01:58:16
Session 04 – Power Query (Data Profiling, Column Quality, Date Tools, Conditional Columns)

02:12:52
Session 05 – Power Query (Introduction to M-Code, Column From Examples, Group By, Merge and Append Queries, Parameters)

02:08:51
Session 06 – Power BI Fundamentals (Data Modelling Basics with Normalization, Star vs Snowflake Schema)

02:16:18
Session 07 – Data Analysis Expression (DAX): TEXT Functions (LEFT, RIGHT, UPPER, LOWER, SUBSTITUTE), Nested DAX Functions (LEFT with SEARCH)

02:11:45
Session 08 – DAX: Text functions (LEFT, RIGHT) | Date & Time Functions (DATEDIFF, TODAY, EOMONTH, NETWORKDAYS, WEEKDAY, FORMAT)

02:11:09
Session 09 – Logical Operators | Functions | Order of Operation (Lower & Upper Bound) | IF Functions | Nested IF | Multiple Conditions | Multiple Conditions & Multiple Logics | Uses of AND

02:05:07
Session 10 – SWITCH Function | MID Function | Data Categorization | Hiding Foreign Keys

01:28:04
Session 11.1 – Measures | Implicit vs Explicit Measures | Filter Context | Filter Flow | Aggregation Functions (SUM, COUNT, COUNTROWS, AVERAGE, DISTINCTCOUNT, DIVIDE, MAX, MIN)

34:21
Session 11.2 – Measures | Implicit vs Explicit Measures | Filter Context | Filter Flow | Aggregation Functions (SUM, COUNT, COUNTROWS, AVERAGE, DISTINCTCOUNT, DIVIDE, MAX, MIN)

38:41
Session 11.3 – Measures | Implicit vs Explicit Measures | Filter Context | Filter Flow | Aggregation Functions (SUM, COUNT, COUNTROWS, AVERAGE, DISTINCTCOUNT, DIVIDE, MAX, MIN)

34:04
Session 12 – Data Analysis Expression (DAX) Measures: Filter Modifier CALCULATE Function | ALL Function

02:03:38
Session 13 – Measures: Table Function | Default Nature of Measures | Making Calculate Function Dynamic Through FILTER Function

02:10:00
Session 14 – Measures: Time Intelligence Functions (Time Series Analysis & Categorical Analysis) | MoM | SamePeriodLastYear | DateAdd | ToDate Analysis | DatesYTD | DatesMTD | Rolling Revenue | DATESINPERIOD

02:18:56
Session 15 – Error Handling (IFERROR) | Dashboard: Color Palette | Formatting | New Card Visual | Line Chart (Trend Line, Reference Line: Comparison Analysis)

02:13:14
Session 16 – Dashboard: Categorical Analysis (Corporate Standards) | Bar Chart | KPI Card | Donut Chart | Pie Chart

02:06:02
Session 17 – Data Visualization: Filters (Basic, Cross, Advanced, TOPN) | Visuals (Table, Matrix, Cards) | Differences (Matrix vs. Table, TOPN, Cards)

02:29:34
Session 18 – Data Visualization: Gauge Chart | Drilling (Up, Down, Through) | Drillthrough Page | Bookmarks | Page Navigation | Custom Tooltips | Conditional Formatting | Slicer | Area Chart

02:38:50

Class 01 – Course overview | Learning Roadmap | Introduction to Python and its role in Data Science | Python installation and setup | Visual Studio Code installation and configuration | Virtual environment (venv) setup | Environment verification and readiness for hands-on work

Class 02 – Python Syntax and Semantics | Working with Virtual Environment (venv) | Basic Python Syntax Rules | Variables and Naming Conventions | Data Types | Type Conversion | User Input | Operators in Python | Conditional Statements

Class 03 – Conditional Statements in Python | Nested Conditional Statements | Practical Examples Using Conditions | Hands-on Practice Examples | Key Concepts Reinforced

Class 04 – Introduction to Loops | For Loops | While Loop | Loop Control Statements | Nested Loops | Practical Loop Examples | Introduction to Lists | List Operations and Methods | List Slicing | Iterating Over Lists | List Comprehensions | Real-World Examples Using Lists

Class 05 – Practice + Deep Dive: Loops | Loop Control Statements | Nested Loops | Practical Loop Example | Lists in Python | List Methods | List Comprehension | Real-World Use Cases

Class 06 – Introduction to Functions | Function Parameters and Arguments | Return Statement | Practical Function Examples | Introduction to Object-Oriented Programming (OOP) | Core OOP Concepts Covered |

Class 07 – OOP Recap | Class and Object in Detail | 3. Constructor Method (init) | Instance Attributes and Methods | Self Keyword | Practical OOP Examples | 7. Common OOP Mistakes and Best Practices

Class 08 – Introduction to Streamlit | Basic Structure of a Streamlit App | Core Streamlit Components | Working with User Input Widgets | Data Handling in Streamlit | Practical Demonstrations | Real-World Use Case Discussion | OOP and Pandad Data Manipulation

Class 09 – Introduction to Machine Learning | Classification vs Regression | Logistic Regression (Conceptual Overview) | Train-Test Split | Evaluation Metrics for Classification | Confusion Matrix (In-Depth)

Class 10 – Loan Approval ML Project | Connected Machine Learning Model with Streamlit | Loading and Preprocessing of Real-World Dataset | Use of Pipeline for Clean ML Workflow | Trained Logistic Regression Model | Model Evaluation using Accuracy, Precision, and Recall | Interpretation of Confusion Matrix Results | User Input from Streamlit UI | Generated Real-Time Predictions | Deployed ML Logic inside a Web App

Course Content

SQL Pre-Requisite Videos

Introduction of Data Analytics | Databases | Type of Databases – Session 01

Introduction of SQL | Basic SQL Clauses (SELECT, FROM) – Session 02

WHERE and GROUP BY Clauses in SQL – Session 03 (Part 1)

WHERE, GROUP BY (Revision), HAVING, ORDER BY – Session 3 (Part 2)

Aggregation (SUM, COUNT, AVG) | Numeric (ROUND, CEILING, FLOOR) – Session 04

Date Types & Parts | Date Function (DATEDIFF, GETDATE, DATEADD) – Session 05

String Functions (CONCAT, LEFT, UPPER, LOWER, REPLACE) – Session 06

String Functions (RIGHT, SUBSTRING, Dynamic String Functions – Session 07

Comparison Operators | Conditional Functions (AND, OR, IS) – Session 08

Conditional Functions (LIKE, IIF Function) | Analytic Buckets – Session 09

Conditional Functions (Nested IIF, CASE Statement) – Sessio 10

Basics of Data Normalization – Session 11

Foundations of SQL Joins | ERD Diagram | Fact vs Dim Tables – Session 12

Advanced Analysis with JOINS | Exporting Results | RIGHT JOIN – Session 13

Star and Snowflake Schema | Appending Data (UNION and UNION ALL) – Session 14

Creating VIEWS in SQL Server | Exporting SQL Results to POWER BI – Session 15

Sub Queries (Scalar, Multi Rows, Derived Column) – Session 16

Sub Queries (Derived Column , Derived Table & Co-Related) – Session 17

Advanced Co-Related Sub Query | Common Table Expressions (CTE) – Session 18

Derived Table | Windows Function (OVER, PARTITION BY, ORDER BY) – Session 19

WINDOW Functions (ROW_NUMBER, RANK, DENSE_RANK, LAG) – Session 20

Introduction of MySQL Software | Data Analysis using MySQL – Session 21

WINDOWS Functions | Sub Queries (Column/ Table) – Session 22

Power BI Lectures

Session 01

Session 02

Session 03 – Power Query (Basic, Text & Number Tools)

Session 04 – Power Query (Data Profiling, Column Quality, Date Tools, Conditional Columns)

Session 05 – Power Query (Introduction to M-Code, Column From Examples, Group By, Merge and Append Queries, Parameters)

Session 06 – Power BI Fundamentals (Data Modelling Basics with Normalization, Star vs Snowflake Schema)

Session 07 – Data Analysis Expression (DAX): TEXT Functions (LEFT, RIGHT, UPPER, LOWER, SUBSTITUTE), Nested DAX Functions (LEFT with SEARCH)

Session 08 – DAX: Text functions (LEFT, RIGHT) | Date & Time Functions (DATEDIFF, TODAY, EOMONTH, NETWORKDAYS, WEEKDAY, FORMAT)

Session 09 – Logical Operators | Functions | Order of Operation (Lower & Upper Bound) | IF Functions | Nested IF | Multiple Conditions | Multiple Conditions & Multiple Logics | Uses of AND

Session 10 – SWITCH Function | MID Function | Data Categorization | Hiding Foreign Keys

Session 11.1 – Measures | Implicit vs Explicit Measures | Filter Context | Filter Flow | Aggregation Functions (SUM, COUNT, COUNTROWS, AVERAGE, DISTINCTCOUNT, DIVIDE, MAX, MIN)

Session 11.2 – Measures | Implicit vs Explicit Measures | Filter Context | Filter Flow | Aggregation Functions (SUM, COUNT, COUNTROWS, AVERAGE, DISTINCTCOUNT, DIVIDE, MAX, MIN)

Session 11.3 – Measures | Implicit vs Explicit Measures | Filter Context | Filter Flow | Aggregation Functions (SUM, COUNT, COUNTROWS, AVERAGE, DISTINCTCOUNT, DIVIDE, MAX, MIN)

Session 12 – Data Analysis Expression (DAX) Measures: Filter Modifier CALCULATE Function | ALL Function

Session 13 – Measures: Table Function | Default Nature of Measures | Making Calculate Function Dynamic Through FILTER Function

Session 14 – Measures: Time Intelligence Functions (Time Series Analysis & Categorical Analysis) | MoM | SamePeriodLastYear | DateAdd | ToDate Analysis | DatesYTD | DatesMTD | Rolling Revenue | DATESINPERIOD

Session 15 – Error Handling (IFERROR) | Dashboard: Color Palette | Formatting | New Card Visual | Line Chart (Trend Line, Reference Line: Comparison Analysis)

Session 16 – Dashboard: Categorical Analysis (Corporate Standards) | Bar Chart | KPI Card | Donut Chart | Pie Chart

Session 17 – Data Visualization: Filters (Basic, Cross, Advanced, TOPN) | Visuals (Table, Matrix, Cards) | Differences (Matrix vs. Table, TOPN, Cards)

Session 18 – Data Visualization: Gauge Chart | Drilling (Up, Down, Through) | Drillthrough Page | Bookmarks | Page Navigation | Custom Tooltips | Conditional Formatting | Slicer | Area Chart

Class 01 – Course overview | Learning Roadmap | Introduction to Python and its role in Data Science | Python installation and setup | Visual Studio Code installation and configuration | Virtual environment (venv) setup | Environment verification and readiness for hands-on work

Data Science with AI (Batch-05) – Session 01

Class 02 – Python Syntax and Semantics | Working with Virtual Environment (venv) | Basic Python Syntax Rules | Variables and Naming Conventions | Data Types | Type Conversion | User Input | Operators in Python | Conditional Statements

Data Science with AI (Batch-05) – Session 02

Class 03 – Conditional Statements in Python | Nested Conditional Statements | Practical Examples Using Conditions | Hands-on Practice Examples | Key Concepts Reinforced

Data Science with AI (Batch-05) – Session 03

Class 04 – Introduction to Loops | For Loops | While Loop | Loop Control Statements | Nested Loops | Practical Loop Examples | Introduction to Lists | List Operations and Methods | List Slicing | Iterating Over Lists | List Comprehensions | Real-World Examples Using Lists

Data Science with AI (Batch-05) – Session 04

Class 05 – Practice + Deep Dive: Loops | Loop Control Statements | Nested Loops | Practical Loop Example | Lists in Python | List Methods | List Comprehension | Real-World Use Cases

Data Science with AI (Batch-05) – Session 05

Class 06 – Introduction to Functions | Function Parameters and Arguments | Return Statement | Practical Function Examples | Introduction to Object-Oriented Programming (OOP) | Core OOP Concepts Covered |

Data Science with AI (Batch-05) – Session 06

Class 07 – OOP Recap | Class and Object in Detail | 3. Constructor Method (*init*) | Instance Attributes and Methods | Self Keyword | Practical OOP Examples | 7. Common OOP Mistakes and Best Practices

Data Science with AI (Batch-05) – Session 07

Class 08 – Introduction to Streamlit | Basic Structure of a Streamlit App | Core Streamlit Components | Working with User Input Widgets | Data Handling in Streamlit | Practical Demonstrations | Real-World Use Case Discussion | OOP and Pandad Data Manipulation

Data Science with AI (Batch-05) – Session 08

Class 09 – Introduction to Machine Learning | Classification vs Regression | Logistic Regression (Conceptual Overview) | Train-Test Split | Evaluation Metrics for Classification | Confusion Matrix (In-Depth)

Data Science with AI (Batch-05) – Session 09

Data Science with AI (Batch-05) – Session 10

Class 07 – OOP Recap | Class and Object in Detail | 3. Constructor Method (init) | Instance Attributes and Methods | Self Keyword | Practical OOP Examples | 7. Common OOP Mistakes and Best Practices