Data Mining for Business Analytics Data Analysis in Python

Python for Data Analytics & Explainable Artificial Intelligence. Data Mining for Business Data Analytics & Intelligence.
Data Mining for Business Analytics Data Analysis in Python
File Size :
3.16 GB
Total length :
9h 0m



Diogo Alves de Resende


Last update




Data Mining for Business Analytics Data Analysis in Python

What you’ll learn

Identify the value of data mining for quickly analyzing and interpreting data.
Apply data mining algorithms using Python programming language for Business Analytics.
Explain the principles behind various data mining algorithms, including supervised and unsupervised machine learning, and explainable AI
Explain the results of data mining models using explainable artificial intelligence models: LIME and SHAP.
Practice applying data mining techniques through hands-on exercises and case studies.
Implement cluster analysis, dimension reduction, and association rule learning using Python.
Perform survival analysis, Cox proportional hazard regression, and CHAID using Python.
Use random forest and feature selection to improve the accuracy of data mining models.
Develop a portfolio of data mining projects for Business Data Analytics and Intelligence.
Use data mining techniques to inform business decisions and strategies.

Data Mining for Business Analytics Data Analysis in Python


Statistics – Linear and Logistic Regression
Basic Python


Are you looking to learn how to do Data Mining like a pro? Do you want to find actionable business insights using data science and analytics and explainable artificial intelligence? You have come to the right place.I will show you the most impactful Data Mining algorithms using Python that I have witnessed in my professional career to derive meaningful insights and interpret data.In the age of endless spreadsheets, it is easy to feel overwhelmed with so much data. This is where Data Mining techniques come in. To swiftly analyze, find patterns, and deliver an outcome to you. For me, the Data Mining value added is that you stop the number crunching and pivot table creation, leaving time to come with actionable plans based on the insights.Now, why should you enroll in the course? Let me give you four reasons.The first is that you will learn the models’ intuition without focusing too much on the math. It is crucial that you know why a model makes sense and the underlying assumptions behind it. I will explain to you each model using words, graphs, and metaphors, leaving math and the Greek alphabet to the bare minimum.The second reason is the thorough course structure of the most impactful Data Mining techniques for Data Science and Business Analytics. Based on my experience, the course curriculum has the algorithms I believe to be most impactful, up-to-date, and sought after. Here is the list of the algorithms we will learn:Supervised Machine LearningSurvival AnalysisCox Proportional Hazard RegressionCHAIDUnsupervised Machine LearningCluster Analysis – Gaussian Mixture ModelDimension Reduction – PCA and Manifold LearningAssociation Rule Learning· Explainable Artificial IntelligenceRandom Forest and Feature Seletion and ImportanceLIMEXGBoost and SHAPThe third reason is that we code Python together, line by line. Programming is challenging, especially for beginners. I will guide you through every Python code snippet. I will also explain all parameters and functions that you need to use, step by step. In the end, you will have code templates ready to use in your problems.The final reason is that you practice, practice, practice. At the end of each section, there is a challenge. The goal is that you apply immediately what you have learned. I give you a dataset and a list of actions you need to take to solve it. I think it is the best way to really cement all the techniques in you. Hence, there will be 2 case studies per technique.I hope to have spiked your interest, and I am looking forward to seeing you inside!


Section 1: Introduction

Lecture 1 Introduction to Data Mining course for Business Analytics & Data Analysis

Lecture 2 Your resources

Lecture 3 Course Resources, Material, and Colab setup – Important!

Lecture 4 How to get more from the course

Lecture 5 Reviews and the future of the course

Section 2: Survival Analysis

Lecture 6 Game Plan for Survival Analysis section

Lecture 7 Survival Analyisis Introduction

Lecture 8 Case Study Briefing and Step by Step Guide

Lecture 9 Python – Changing Directory

Lecture 10 Python – Importing Libraries

Lecture 11 Python – Loading Data

Lecture 12 Python – Transforming Dependent Variable

Lecture 13 Kaplan-Meyer Estimator

Lecture 14 Censoring

Lecture 15 Python – Kaplan-Meyer Estimator

Lecture 16 Python – Calculating Specific Events

Lecture 17 Python – Plotting Survival Curves

Lecture 18 Python – Plotting Cumulative Curves

Lecture 19 Log Rank Test

Lecture 20 Python – Subsetting Dataframe

Lecture 21 Python – Plotting both Survival Curves

Lecture 22 Python – Log Rank Test

Lecture 23 Python – Kaplan-Meyer Estimator per Gender

Lecture 24 Extra Resources and Survival Analysis Challenge

Lecture 25 Python – Survival Analysis Challenge Solutions

Section 3: Cox Proportional Hazard Regression

Lecture 26 Game Plan

Lecture 27 Cox Proportional Hazard Regression

Lecture 28 Case Study Briefing and Step by Step Guide

Lecture 29 Python – Preparing Script and Data

Lecture 30 Python – Cox Proportional Hazard

Lecture 31 Python – Regression Summary Visualization

Lecture 32 Extra Resources and Challenge

Lecture 33 Python – Solution Challenges

Section 4: CHAID

Lecture 34 Game Plan

Lecture 35 Case Study Briefing and Step by Step Guide

Lecture 36 Problem Statement

Lecture 37 Python – Installing libraries

Lecture 38 Python – Importing Libraries and Data

Lecture 39 Introducing CHAID

Lecture 40 CHAID Statistics and Quirks

Lecture 41 Python – Removing column and unique values check

Lecture 42 Python – Visualizing Jobs Variable

Lecture 43 Python – Transforming Jobs Variable

Lecture 44 Python – Transforming Experience Variable

Lecture 45 Python – Transform Minimum Variable

Lecture 46 Python – Modify other variables to dummy variables

Lecture 47 Python – CHAID Preparation

Lecture 48 Python – CHAID Model

Lecture 49 Python – Data Visualization with CHAID Model

Lecture 50 Extra Resources and Challenge

Lecture 51 Python – Challenge solutions

Section 5: Cluster Analysis – Gaussian Mixture Model

Lecture 52 Game Plan

Lecture 53 Case Study Briefing and Clustering

Lecture 54 Gaussian Mixture Model vs. Kmeans

Lecture 55 Python – Changing Directory and Importing Libraries

Lecture 56 Python – Loading Data

Lecture 57 AIC, BIC, and Step-by-Step Guide

Lecture 58 Python – Optimal Clusters

Lecture 59 Python – Gaussian Mixture Model

Lecture 60 Python – Cluster Prediction

Lecture 61 Python – Probability of belonging to each cluster

Lecture 62 Python – Cluster Interpretation

Lecture 63 Extra Resources and Challenge

Lecture 64 Python – Challenge solutions

Section 6: Dimension Reduction

Lecture 65 Game Plan

Lecture 66 What is Dimension Reduction?

Lecture 67 Principal Component Analysis

Lecture 68 Python – Importing Libraries

Lecture 69 Python – Loading Data

Lecture 70 Python – Transforming String Variables

Lecture 71 Python – Correlation Matrix

Lecture 72 Python – Standardizing Variables

Lecture 73 Python – Optimal Number of Components

Lecture 74 Python – Cumulative Explained Variance

Lecture 75 Python – PCA

Lecture 76 Python – PCA interpretation

Lecture 77 Manifold Learning and t-SNE

Lecture 78 Python – t-SNE

Lecture 79 Python -Visualizing Manifold Learning

Lecture 80 Extra Resources and Challenge

Lecture 81 Python – Challenge Solutions

Section 7: Association Rule Learning

Lecture 82 Game Plan

Lecture 83 Step by Step Guide and Case Study Briefing

Lecture 84 Python – Importing Libraries

Lecture 85 Python – Loading Data

Lecture 86 Association Rule Learning

Lecture 87 Python – Create Transaction List

Lecture 88 Python – Encoding Transactions

Lecture 89 Apriori algorithm

Lecture 90 Python – Association Rule Learning

Lecture 91 Python – Apriori Visualization

Lecture 92 Extra Resources and Challenge

Lecture 93 Python – Challenge Solutions

Section 8: Random Forest and Feature Selection

Lecture 94 Game Plan for Random Forest

Lecture 95 Case Study Briefing and Step by Step Guide

Lecture 96 Python – Importing Libraries

Lecture 97 Python – Loading Data

Lecture 98 Python – Transforming Categorical Variables

Lecture 99 Random Forest

Lecture 100 Python – Training and Test Set

Lecture 101 Python – Random Forest

Lecture 102 Confusion Matrix, AUC, and F1-Score

Lecture 103 Python – Random Forest Predictions

Lecture 104 Python – Classification Report

Lecture 105 Python .- Feature Importance for Business Analytics

Lecture 106 Extra Resources and Challenge

Lecture 107 Python – Challenge Solutions

Section 9: LIME – Explainable Artificial Intelligence

Lecture 108 Game Plan for Explainable Artificial Intelligence

Lecture 109 LIME

Lecture 110 Python – Preparing LIME

Lecture 111 Python – Explaining Predictions

Lecture 112 Extra Resources and Challenge

Lecture 113 Python – Challenge Solutions

Section 10: XGBoost and SHAP

Lecture 114 Game Plan for XGBoost and SHAP

Lecture 115 Case Study Briefing and Step by Step Guide

Lecture 116 Python – Importing Libraries

Lecture 117 Python – Loading Data

Lecture 118 Introducing XGBoost

Lecture 119 How XGBoost works part 1

Lecture 120 How XGBoost works part 2

Lecture 121 XGBoost quirks

Lecture 122 Python – Isolate X and Y

Lecture 123 Python – Training and Test Set

Lecture 124 Python – XGBoost Matrices

Lecture 125 XGBoost Parameters

Lecture 126 Python – XGBoost Parameters

Lecture 127 Python – XGBoost Model

Lecture 128 Evaluate Regression-based Problems

Lecture 129 Python – Predictions

Lecture 130 Python – MAE and RSME

Lecture 131 SHAP

Lecture 132 Python – Preparing SHAP

Lecture 133 Python – Local Interpretability

Lecture 134 Python – Dependency Plots

Lecture 135 Python – Global Interpretability

Lecture 136 Extra Resources and Challenge

Lecture 137 Python – Challenge Solutions

Section 11: Bonus Section

Lecture 138 Bonus Lecture

Professionals looking to learn Data Mining algorithms,Data Analysts starting to learn Data Mining techniques,Business Analysts looking to learn algorithms on how to uncover business insights,Any Python programmer who would like to learn Data Mining tools

Course Information:

Udemy | English | 9h 0m | 3.16 GB
Created by: Diogo Alves de Resende

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