• Overview of Data Analytics and its importance
• History of Data Analytics and its evolution
• Basic concepts and applications of Data Analytics

• Introduction
• Get Started
• Syntax, Comments
• Variables
• Data Types
• Casting, Operators
• List, Tuple, Set, Dictionaries
• Conditions
• Loops
• Functions, Lambda
• Classes/Objects
• Inheritance
• Scope
• Modules
• Dates, Math
• PIP
• Try ..Excep

• Introduction
• Get Started & Overview
• Syntax & Range Move, Add, Delete Cells
• Undo, Formulas
• Relative, Absolute Ref
• Arithmetic Operators
• Parentheses
• Functions
• Excel Formatting
• Sort, Filter
• Tables
• Conditional
• Highlight Cell Rules
• Data Bars
• Color Scales
• Icon Sets
• Charts
• Charts Customization

• Introduction
• Gathering Data
• Describing Data
• Conclusions, Prediction
• Populations Samples
• Parameters & Statistics
• Data Types
• Measurement Levels
• Descriptive Statistics
• Frequency Tables
• Histograms
• Bar Graphs, Pie charts
• Box Plots, Average
• Mean, Median, Mode
• Variation, Range
• Interquartile Range
• Standard Deviation

• Introduction
• Get Started
• Create Database
• Create Table
• Insert
• Select
• Where
• Order By
• Delete
• Drop Table
• Update
• Limit
• Join

• Introduction
• Getting and Transforming Data
• Modelling with Power BI
• DAX Visualising Data with Reports
• Introduction to Power BI Service
• Sharing and Collaboration Tools in Power BI Service
• Power BI Gateway and Refreshing DatasetsForms

• Real-world applications of Data Analytics in various industries
• Overview of Data Analytics ethics and bias
• Capstone project: Applying AI concepts to solve a simple problem

• Each week could consist of video lectures, reading materials, and quizzes.
• The syllabus focuses on providing a basic understanding of AI concepts and practical applications within a one-month timeframe.