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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 y
ou 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 Learning
Survival Analysis
Cox Proportional Hazard Regression
CHAID
Unsupervised Machine Learning
Cluster Analysis - Gaussian Mixture Model
Dimension Reduction – PCA and Manifold Learning
Association Rule Learning
·
Explainable Artificial Intelligence
Random Forest and Feature Seletion and Importance
LIME
XGBoost and SHAP
The 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!
Who this course is for:
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

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

  • You will need a copy of Adobe XD 2019 or above. A free trial can be downloaded from Adobe.
  • No previous design experience is needed.
  • No previous Adobe XD skills are needed.

Course Content

27 sections • 95 lectures
Expand All Sections
1-Introduction
4
1.1-Introduction to Data Mining course for Business Analytics & Data Analysis
1.2-Your resources
1.3-Course Resources, Material, and Colab setup - Important!
1.4-Diogo's Introduction and Background
2-Survival Analysis
20
2.1-Game Plan for Survival Analysis section
2.2-Survival Analyisis Introduction
2.3-Case Study Briefing and Step by Step Guide
2.4-Python - Changing Directory
2.5-Python - Importing Libraries
2.6-Python - Loading Data
2.7-Python - Transforming Dependent Variable
2.8-Kaplan-Meyer Estimator
2.9-Censoring
2.10-Python - Kaplan-Meyer Estimator
2.11-Python - Calculating Specific Events
2.12-Python - Plotting Survival Curves
2.13-Python - Plotting Cumulative Curves
2.14-Log Rank Test
2.15-Python - Subsetting Dataframe
2.16-Python - Kaplan-Meyer Estimator per Gender
2.17-Python - Plotting both Survival Curves
2.18-Python - Log Rank Test
2.19-Extra Resources and Survival Analysis Challenge
2.20-Python - Survival Analysis Challenge Solutions
3-Cox Proportional Hazard Regression
8
3.1-Game Plan
3.2-Cox Proportional Hazard Regression
3.3-Case Study Briefing and Step by Step Guide
3.4-Python - Preparing Script and Data
3.5-Python - Cox Proportional Hazard
3.6-Python - Regression Summary Visualization
3.7-Extra Resources and Challenge
3.8-Python - Solution Challenges
4-CHAID
18
4.1-Game Plan
4.2-Case Study Briefing and Step by Step Guide
4.3-Problem Statement
4.4-Python - Installing libraries
4.5-Python - Importing Libraries and Data
4.6-Introducing CHAID
4.7-CHAID Statistics and Quirks
4.8-Python - Removing column and unique values check
4.9-Python - Visualizing Jobs Variable
4.10-Python - Transforming Jobs Variable
4.11-Python - Transforming Experience Variable
4.12-Python - Transform Minimum Variable
4.13-Python - Modify other variables to dummy variables
4.14-Python - CHAID Preparation
4.15-Python - CHAID Model
4.16-Python - Data Visualization with CHAID Model
4.17-Extra Resources and Challenge
4.18-Python - Challenge solutions
5-Cluster Analysis - Gaussian Mixture Model
15
5.1-Game Plan
5.2-Case Study Briefing and Clustering
5.3-Gaussian Mixture Model vs. Kmeans
5.4-Python - Changing Directory and Importing Libraries
5.5-Python - Loading Data
5.6-AIC, BIC, and Step-by-Step Guide
5.7-Python - Optimal Clusters
5.8-Python - Gaussian Mixture Model
5.9-Python - Cluster Prediction
5.10-Python - Probability of belonging to each cluster
5.11-Python - Cluster Interpretation
5.12-Extra Resources and Challenge
5.13-Python - Challenge solutions
5.14-Will you help me?
5.15-Your feedback is invaluable
6-Dimension Reduction
17
6.1-Game Plan
6.2-What is Dimension Reduction?
6.3-Principal Component Analysis
6.4-Python - Importing Libraries
6.5-Python - Loading Data
6.6-Python - Transforming String Variables
6.7-Python - Correlation Matrix
6.8-Python - Standardizing Variables
6.9-Python - Optimal Number of Components
6.10-Python - Cumulative Explained Variance
6.11-Python - PCA
6.12-Python - PCA interpretation
6.13-Manifold Learning and t-SNE
6.14-Python - t-SNE
6.15-Python -Visualizing Manifold Learning
6.16-Extra Resources and Challenge
6.17-Python - Challenge Solutions
7-Association Rule Learning
12
7.1-Game Plan
7.2-Step by Step Guide and Case Study Briefing
7.3-Python - Importing Libraries
7.4-Python - Loading Data
7.5-Association Rule Learning
7.6-Python - Create Transaction List
7.7-Python - Encoding Transactions
7.8-Apriori algorithm
7.9-Python - Association Rule Learning
7.10-Python - Apriori Visualization
7.11-Extra Resources and Challenge
7.12-Python - Challenge Solutions
8-Random Forest and Feature Selection
14
8.1-Game Plan for Random Forest
8.2-Case Study Briefing and Step by Step Guide
8.3-Python - Importing Libraries
8.4-Python - Loading Data
8.5-Python - Transforming Categorical Variables
8.6-Random Forest
8.7-Python - Training and Test Set
8.8-Python - Random Forest
8.9-Confusion Matrix, AUC, and F1-Score
8.10-Python - Random Forest Predictions
8.11-Python - Classification Report
8.12-Python .- Feature Importance for Business Analytics
8.13-Extra Resources and Challenge
8.14-Python - Challenge Solutions
9-LIME - Explainable Artificial Intelligence
6
9.1-Game Plan for Explainable Artificial Intelligence
9.2-LIME
9.3-Python - Preparing LIME
9.4-Python - Explaining Predictions
9.5-Extra Resources and Challenge
9.6-Python - Challenge Solutions
10-XGBoost and SHAP
25
10.1-Game Plan for XGBoost and SHAP
10.2-Case Study Briefing and Step by Step Guide
10.3-Python - Importing Libraries
10.4-Python - Loading Data
10.5-Introducing XGBoost
10.6-How XGBoost works part 1
10.7-How XGBoost works part 2
10.8-XGBoost quirks
10.9-Python - Isolate X and Y
10.10-Python - Training and Test Set
10.11-Python - XGBoost Matrices
10.12-XGBoost Parameters
10.13-Python - XGBoost Parameters
10.14-Python - XGBoost Model
10.15-Evaluate Regression-based Problems
10.16-Python - Predictions
10.17-Python - MAE and RSME
10.18-SHAP
10.19-Python - Preparing SHAP
10.20-Python - Local Interpretability
10.21-Python - Dependency Plots
10.22-Python - Global Interpretability
10.23-Extra Resources and Challenge
10.24-Python - Challenge Solutions
10.25-End of Course Feedback
11-Bonus Section
1
11.1-Bonus Lecture