Python Programming with AI for Business Intelligence

Course Curriculum

Chapter 1: Setting Up Your Data Analysis Platform

Lecture 1: Install Python and Jupyter Notebook

Lecture 2: Setting Up ChatGPT for SMART Analysis

Lecture 3: Download Resources Used in this Course

Lecture 4: Connect with my youtube channel

Lecture 5: Get special handbooks

Chapter 2: Necessary Python Development – Part 1

Lecture 1: Getting started with Python coding

Lecture 2: Variables and naming conventions

Lecture 3: Data types: integers, float, strings, boolean

Lecture 4: Type conversion and casting

Lecture 5: Arithmetic operators (+, -, *, /, %, **)

Lecture 6: Comparison operators (>, =, <=, ==, !=)

Lecture 7: Logical operators (and, or, not)

Chapter 3: Necessary Python Development – Part 2

Lecture 1: Lists: creation, indexing, slicing, modifying

Lecture 2: Sets: unique elements, operations

Lecture 3: Dictionaries: key-value pairs, methods

Lecture 4: Conditional statements (if, elif, else)

Lecture 5: Logical expressions in conditions

Lecture 6: Looping structures (for loops, while loops)

Lecture 7: Defining, Creating and Calling functions

Chapter 4: Phase 1: Steps in Data Cleaning for Real – World Projects

Lecture 1: Importing Sales Dataset in Jupyter Notebook

Lecture 2: Imputing missing values with scikit-learn method

Lecture 3: Finding out and dealing with inconsistent values

Lecture 4: Fixing wrong data types and assign the correct type

Lecture 5: Dropping duplicates making dataset error free

Chapter 5: Phase 2: Steps in Data Manipulation for Real – World Projects

Lecture 1: Organinzing and Sorting dataset and finding insight

Lecture 2: Conditional filtering, data splitting, data partitioning

Lecture 3: Merge extra necessary variables to the dataset

Lecture 4: Concatenating extra necessary data within existing data

Chapter 6: Phase 3: Steps in Exploratory Data Analysis for Real – World Challenges

Lecture 1: Understand exploratory data analysis

Lecture 2: Challenge 1: What is the country of residence for the majority of customers?

Lecture 3: Challenge 2: Find the descriptives of order value, cost and refund.

Lecture 4: Challenge 3: Find top 3 product categories based on both order value and cost.

Lecture 5: Challenge 4: Who are the most loyal customers of your superstore?

Lecture 6: Challenge 5: Which sales manager sold product that has the highest sales volume?

Lecture 7: Challenge 6: Find the relationship between order value, cost and refund amount.

Chapter 7: Phase 4: Understanding Statistical Analysis and Hypothesis Testing

Lecture 1: Various aspects of hypothesis testing in statistics

Lecture 2: Understand confidence level, significance level and p-value

Lecture 3: Understand complete steps in hypothesis testing

Chapter 8: Phase 5. Transform Data into Normal Distribution Format

Lecture 1: Testing normal distribution of numeric variables

Lecture 2: Square root transformation for normal distribution conversion

Lecture 3: Logarithmic transformation for normal distribution conversion

Lecture 4: Box-cox transformation for normal distribution conversion

Lecture 5: Yeo-Johnson transformation for normal distribution conversion

Chapter 9: Phase 6: Perform Statistical Data Analysis and Hypothesis Testing

Lecture 1: One – way ANOVA: Testing the difference

Lecture 2: Pearson correlation test: Testing the relationship

Lecture 3: Regression analysis: Testing the influence

Chapter 10: Your Next Journey of Learning

Lecture 1: Resources for enhancing data analytics skill

Chapter 11: Final Project