Python Programming with AI for Business Intelligence

Name: Python Programming with AI for Business Intelligence
Rating: 4.5 (49 reviews)

Learn Pandas, NumPy, Matplotlib and Statmodels with ChatGPT from Data Analytics to Strategic Decision-Making in Python.

Created byAnalytix AI

Last updated 2/2025

English

What you'll learn

Gain a solid understanding of Python for data analytics. Develop the skills to clean and preprocess real-world datasets, ensuring data quality and reliability.
Learn to use key libraries such as Pandas, NumPy, and Matplotlib for data manipulation, analysis, and visualization.
Master advanced data processing techniques, including feature engineering, data transformation, and handling missing values.
Explore how to implement statmodels API to build statistical models and determine the most influential factors to make effective decisions and recommendations.

Coding Exercises

This course includes our updated coding exercises so you can practice your skills as you learn.

Course content

11 sections • 46 lectures • 5h 26m total length

Install Python and Jupyter Notebook0:07
Setting Up ChatGPT for SMART Analysis0:06
Download Resources Used in this Course0:03
Resources for Complete Data Analyst!0:56

Getting started with Python coding4:36
Working with print()
Variables and naming conventions9:05
Working with variables
Data types: integers, float, strings, boolean7:22
Type conversion and casting9:49
Working with data types
Arithmetic operators (+, -, *, /, %, **)6:44
Arithmetic operation
Comparison operators (>, =, <=, ==, !=)7:35
Comparison operation
Logical operators (and, or, not)7:06
Explore how python logical operators and, or, and not combine conditional results to produce boolean outputs, with practical examples using greater than and less than comparisons.
Logical operation

Lists: creation, indexing, slicing, modifying15:59
Working with lists
Sets: unique elements, operations7:51
Working with sets
Dictionaries: key-value pairs, methods9:23
Working with dictionaries
Conditional statements (if, elif, else)7:04
Conditional statements
Logical expressions in conditions9:57
Logical expression
Looping structures (for loops, while loops)9:12
Working with loops
Defining, Creating and Calling functions5:11
Working with functions

Importing Sales Dataset in Jupyter Notebook7:06
Imputing missing values with scikit-learn method12:43
Identify missing values
Impute missing values
Finding out and dealing with inconsistent values11:17
Dealing with inconsistent data
Fixing wrong data types and assign the correct type7:43
Clean data by removing rows with inconsistent values in a numeric column using pandas. Then assign correct types, float for cost and date time 64 for date, with code examples.
Assigning correct data types
Dropping duplicates making dataset error free4:22
Dealing with duplicates

Understand exploratory data analysis3:39
Challenge 1: What is the country of residence for the majority of customers?10:18
Practicing frequency and percentage analysis
Challenge 2: Find the descriptives of order value, cost and refund.6:24
Practicing descriptive analysis
Challenge 3: Find top 3 product categories based on both order value and cost.9:50
Practicing group by analysis
Challenge 4: Who are the most loyal customers of your superstore?10:10
Practicing pivot table analysis
Challenge 5: Which sales manager sold product that has the highest sales volume?7:05
Practicing crosstabulation analysis
Challenge 6: Find the relationship between order value, cost and refund amount.5:33
Apply correlation analysis to three numeric variables—order value, cost, and refund—to compute and visualize a correlation matrix and heatmap, revealing strong positive relationships.
Practicing correlation analysis

Testing normal distribution of numeric variables8:29
Square root transformation for normal distribution conversion6:15
Square root transformation method
Logarithmic transformation for normal distribution conversion5:57
Logarithmic transformation method
Box-cox transformation for normal distribution conversion5:45
In this lesson, apply the Box-cox transformation in Python to order value, cost, and refund, compare with square root and logarithmic transforms, and prepare for the Johnson method.
Box-cox transformation method
Yeo-Johnson transformation for normal distribution conversion5:14
Yeo-johnson transformation method

Requirements

Basic Python Knowledge
Desire to Learn

Description

Embark on a transformative journey into the realm of Business Analytics and Statistics in Python & ChatGPT with our comprehensive course. In this dynamic learning experience, you will acquire a robust foundation in Python tailored for data analytics, gaining essential skills to navigate, clean, and preprocess real-world datasets effectively. Through hands-on exercises and real-life scenarios, you will become adept at ensuring data quality and reliability, laying the groundwork for informed decision-making in a data-driven world.

Dive deep into the heart of data manipulation, analysis, and visualization with key libraries such as Pandas, NumPy, and Matplotlib. Unlock the potential of these powerful tools to derive meaningful insights from complex datasets, equipping you with the capabilities to transform raw information into actionable intelligence. The course will guide you through advanced data processing techniques, including feature engineering, data transformation, and handling missing values, ensuring you possess the skill set needed to tackle the intricacies of real-world data.

Beyond foundational skills, the course elevates your proficiency by exploring the application of the statmodels API. Learn how to construct statistical models that unveil the most influential factors in a dataset, enabling you to make effective decisions and recommendations. Through practical applications and case studies, you will gain a holistic understanding of leveraging statistical insights for strategic decision-making in various business scenarios. By the end of the course, you will emerge not only with technical expertise but also with a strategic mindset to tackle business challenges head-on in today's competitive landscape.

Join us on this transformative learning journey, where theory meets practical application, and empower yourself with the tools and knowledge to navigate the complexities of real-world data analytics and drive meaningful impact within your organization.

Who this course is for:

Data analytics beginners who desire to master Python programming for data manipulation, analysis, and visualization.
Businesspeople who want to make data-driven judgments. The course shows how to use analytics to address business problems.
People seeking to improve decision-making and company value with data-driven insights. The course shows how to use data analytics to business problems.

Python Programming with AI for Business Intelligence

What you'll learn

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Coding Exercises

Course content

Setting Up Your Data Analysis Platform4 lectures • 1min

Necessary Python Development - Part 17 lectures • 52min

Necessary Python Development - Part 27 lectures • 1hr 5min

Phase 1: Steps in Data Cleaning for Real - World Projects5 lectures • 43min

Phase 2: Steps in Data Manipulation for Real - World Projects4 lectures • 23min

Phase 3: Steps in Exploratory Data Analysis for Real - World Challenges7 lectures • 53min

Phase 4: Understanding Statistical Analysis and Hypothesis Testing3 lectures • 21min

Phase 5. Transform Data into Normal Distribution Format5 lectures • 32min

Phase 6: Perform Statistical Data Analysis and Hypothesis Testing3 lectures • 35min

Your Next Journey of Learning1 lecture • 1min

Requirements

Description

Who this course is for: