Data Mining for Business Analytics & Data Analysis in Python

Python for Data Analytics & Explainable Artificial Intelligence. Data Mining for Business Data Analytics & Intelligence.

4.60 (254 reviews)
Udemy
platform
English
language
Data & Analytics
category
2,777
students
9 hours
content
Mar 2024
last update
$84.99
regular price

What you will 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.

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 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!

Content

Introduction

Introduction
Knowledge Requirements
Your resources
Course Resources and Material - Important!

Survival Analysis

Game Plan
Survival Analyisis Introduction
Case Study Briefing and Step by Step Guide
Python - Importing Libraries
Python - Loading Data
Python - Transforming Dependent Variable
Kaplan-Meyer Estimator
Censoring
Python - Kaplan-Meyer Estimator
Python - Calculating Specific Events
Python - Plotting Survival Curves
Python - Plotting Cumulative Curves
Log Rank Test
Python - Subsetting Dataframe
Python - Plotting both Survival Curves
Python - Log Rank Test
Python - Kaplan-Meyer Estimator per Gender
Extra Resources and Survival Analysis Challenge
Python - Survival Analysis Challenge Solutions

Cox Proportional Hazard Regression

Game Plan
Cox Proportional Hazard Regression
Case Study Briefing and Step by Step Guide
Python - Preparing Script and Data
Python - Cox Proportional Hazard
Python - Regression Summary Visualization
Extra Resources and Challenge
Python - Solution Challenges

CHAID

Game Plan
Case Study Briefing and Step by Step Guide
Problem Statement
Python - Installing libraries
Python - Importing Libraries and Data
Introducing CHAID
CHAID Statistics and Quirks
Python - Removing column and unique values check
Python - Visualizing Jobs Variable
Python - Transforming Jobs Variable
Python - Transforming Experience Variable
Python - Transform Minimum Variable
Python - Modify other variables to dummy variables
Python - CHAID Preparation
Python - CHAID Model
Python - Visualizing CHAID Model
Extra Resources and Challenge
Python - Challenge solutions

Cluster Analysis - Gaussian Mixture Model

Game Plan
Case Study Briefing and Clustering
Gaussian Mixture Model vs. Kmeans
Python - Changing Directory and Importing Libraries
Python - Loading Data
AIC, BIC, and Step-by-Step Guide
Python - Optimal Clusters
Python - Gaussian Mixture Model
Python - Cluster Prediction
Python - Probability of belonging to each cluster
Python - Cluster Interpretation
Extra Resources and Challenge
Python - Challenge solutions

Dimension Reduction

Game Plan
What is Dimension Reduction?
Principal Component Analysis
Python - Importing Libraries
Python - Loading Data
Python - Transforming String Variables
Python - Correlation Matrix
Python - Standardizing Variables
Python - Optimal Number of Components
Python - Cumulative Explained Variance
Python - PCA
Python - PCA interpretation
Manifold Learning and t-SNE
Python - t-SNE
Python -Visualizing Manifold Learning
Extra Resources and Challenge
Python - Challenge Solutions

Association Rule Learning

Game Plan
Step by Step Guide and Case Study Briefing
Python - Importing Libraries
Python - Loading Data
Association Rule Learning
Python - Create Transaction List
Python - Encoding Transactions
Apriori algorithm
Python - Association Rule Learning
Python - Apriori Visualization
Extra Resources and Challenge
Python - Challenge Solutions

Random Forest and Feature Selection

Game Plan
Case Study Briefing and Step by Step Guide
Python - Importing Libraries
Python - Loading Data
Python - Transforming Categorical Variables
Random Forest
Python - Training and Test Set
Python - Random Forest
Confusion Matrix, AUC, and F1-Score
Python - Random Forest Predictions
Python - Classification Report
Python .- Feature Importance
Extra Resources and Challenge
Python - Challenge Solutions

LIME - Explainable AI

Game Plan
LIME
Python - Preparing LIME
Python - Explaining Predictions
Extra Resources and Challenge
Python - Challenge Solutions

XGBoost and SHAP

Game Plan
Case Study Briefing and Step by Step Guide
Python - Importing Libraries
Python - Loading Data
Introducing XGBoost
How XGBoost works part 1
How XGBoost works part 2
XGBoost quirks
Python - Isolate X and Y
Python - Training and Test Set
Python - XGBoost Matrices
XGBoost Parameters
Python - XGBoost Parameters
Python - XGBoost Model
Evaluate Regression-based Problems
Python - Predictions
Python - MAE and RSME
SHAP
Python - Preparing SHAP
Python - Local Interpretability
Python - Dependency Plots
Python - Global Interpretability
Extra Resources and Challenge
Python - Challenge Solutions

Screenshots

Data Mining for Business Analytics & Data Analysis in Python - Screenshot_01Data Mining for Business Analytics & Data Analysis in Python - Screenshot_02Data Mining for Business Analytics & Data Analysis in Python - Screenshot_03Data Mining for Business Analytics & Data Analysis in Python - Screenshot_04

Reviews

Julian
August 31, 2023
Great course, he leaves the complex math/Greek letters out of things and tries to keep things straightforward. Would've been nice to have some quizzes to drill the information in, but that's a minor nit-pick.
Armin
August 28, 2023
The four stars of my rating of this course are intended for the originality of the methods used rather than for a precise, deep and comprehensive explanation, analysis and discussion of the methods, coding or the results. I would have liked citations of the data source as well as weblinks to the additional papers. Despite these shortcomings, I enjoyed taking the course that allowed me to add some useful new tools to my toolkit and I would take the course again. Thanks to the author!
Towfeeq
August 15, 2023
So far, the introduction, the course material and arrangement seems to be good. I held back one star because I feel there is always room for improvement. Cheers!
Nam
August 6, 2023
Diogo is a very good teacher. I find the overall content of this course very relevant to my day-to-day work. I also highly recommend his other course Business Data analytics, which goes deep into other tools like time-series and RFM.
Paul
July 22, 2023
The course is very well organized, easy to understand and there are many useful resources for the course or for further study. However, I would have liked to have seen a more in-depth "final" application case, especially in terms of interpretations and subsequent choices made in relation to the basic objective. Overall: I highly recommend this very interesting and well-designed course to anyone who wants to know how to gain new and valuable business insights.
Marta
May 2, 2023
Very well structured and complete with examples and challenges. I wish I could have more time to better enjoy this course. Good job!
Adam
April 18, 2023
Early in the course: I am able to follow the explanations. The motivation for the material is good. I look forward to the upcoming explanations to see whether I am able to assimilate the material.
Eduards
February 10, 2023
I just started this course but based on my past experience with Diogo's courses, I can say he is a fantastic instructor who really cares about making sure his students get the most out of the course. He's always quick to respond and help clarify anything that's unclear. He's knowledgeable, engaging, and makes the content easy to understand. I'm confident that this course will be no different and I'm excited to see what I'll learn!
Chenrui
January 31, 2023
The content of course gives a very intuitive way to stew over many data mining concepts! A good balance between rigor and practicability.
Dmitry
December 19, 2022
This is excellent course, well organized and delivered. Complex topics are broken down and clearly explained. Not only the course guided me through the data mining models, but it also helped me to get a clear understanding of the potential use cases, pros and cons of each model and underlaying statistical concepts. Thank you very much!
Saboor
December 11, 2022
I really enjoyed this course. Diogo has a speciality in presenting the difficult topics in an easy to understand manner. This course is absolutely a valuable addition to my learning list.
Vladimir
December 28, 2021
Excellent course for business applications in Python. That was main reason I have choosen it and I was very satisfied. My suggestion to the author is to improve part when one is interpreting the results. Not the SHAP part, this was very well explained, but the previous ones. Just spend some more time explaining meaning of the results. I believe it will increase value of knowledge you deliver.
Maxon
November 25, 2021
His is a great tutor. He simplified his codes, connected everything to real life case, gave extra resources to work with amongst others.
Carlos
June 19, 2021
This is clearly your best course. At a high level, with material not so easy to find elsewhere. Great! Hopefully the author can follow with more advanced courses, such as replicating actual advanced econometrics techniques directly from recent papers, that can be used by data scientists today, instead of the vanilla techniques. Would look up to it!
Monika
May 10, 2021
This is the second Diogo’s course that I’ve done and, again, he managed to impress me with the quality of the content. Course covers a lot of exciting data mining techniques with an interesting examples. Explanations are detailed, but Diogo makes it easy to follow. Additionally, he provides cool challenges and a lot of material to further investigate the topics. Highly recommended!

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3731276
udemy ID
12/27/2020
course created date
3/31/2021
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