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.
- 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 Learning
Cox Proportional Hazard Regression
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
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
Diogo is a data analytics and business analytics professional with years of experience in the field. He has expertise in various methodologies, including time series forecasting for predicting sales trends, econometrics for analyzing economic data, and machine learning for optimizing marketing campaigns.
His background includes working for a major e-commerce company, where he used these techniques to drive business growth, and collaborating with the United Nations on a Mobile Money project in Lesotho, where he helped increase financial inclusion in the country.
In his courses, Diogo aims to provide practical and applicable knowledge through real-life examples and datasets. For example, he often uses case studies from his own work experiences to illustrate key concepts and demonstrate their relevance in the professional world. His goal is to equip students with the skills and tools necessary to succeed in their own careers in data science.