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.
Requirements
Statistics – Linear and Logistic Regression
Basic Python
Description
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!
Overview
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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