Data Mining with Python
( 25 Modules )

Module #1
Introduction to Data Mining
Overview of data mining, its applications, and importance in business decision-making

Module #2
Introduction to Python for Data Mining
Basics of Python programming, setup, and essential libraries for data mining

Module #3
Data Preprocessing
Importance of data preprocessing, handling missing values, and data normalization

Module #4
Data Visualization with Python
Introduction to data visualization, plotting, and charting using popular Python libraries

Module #5
Supervised Learning Fundamentals
Introduction to supervised learning, regression, and classification

Module #6
Regression Analysis with Python
implemening linear and multiple regression using Python

Module #7
Classification Algorithms with Python
Implementing logistic regression, decision trees, and random forests for classification

Module #8
Model Evaluation and Selection
Evaluating model performance, bias-variance tradeoff, and model selection techniques

Module #9
Unsupervised Learning Fundamentals
Introduction to unsupervised learning, clustering, and dimensionality reduction

Module #10
K-Means Clustering with Python
Implementing k-means clustering algorithm using Python

Module #11
Hierarchical Clustering with Python
Implementing hierarchical clustering algorithm using Python

Module #12
Principal Component Analysis (PCA) with Python
Implementing PCA for dimensionality reduction using Python

Module #13
Association Rule Mining
Introduction to association rule mining, Apriori algorithm, and Eclat algorithm

Module #14
Text Mining with Python
Introduction to text mining, text preprocessing, and sentiment analysis using Python

Module #15
Time Series Analysis with Python
Introduction to time series analysis, forecasting, and seasonal decomposition using Python

Module #16
Big Data and NoSQL Databases
Introduction to big data, Hadoop, and NoSQL databases such as MongoDB and Cassandra

Module #17
Data Mining with PySpark
Introduction to PySpark, distributed data processing, and data mining with PySpark

Module #18
Deep Learning for Data Mining
Introduction to deep learning, neural networks, and their applications in data mining

Module #19
Data Mining Applications
Real-world applications of data mining in finance, healthcare, and marketing

Module #20
Case Studies in Data Mining
Real-world case studies and projects in data mining using Python

Module #21
Data Mining Ethics and Privacy
Ethical considerations in data mining, privacy, and fairness in algorithmic decision-making

Module #22
Python Libraries for Data Mining
Overview of popular Python libraries for data mining, including scikit-learn, pandas, and NumPy

Module #23
Data Preprocessing Techniques
Advanced data preprocessing techniques, including feature scaling and selection

Module #24
Model Deployment and Integration
Deploying data mining models, model integration, and API development using Python

Module #25
Course Wrap-Up & Conclusion
Planning next steps in Data Mining with Python career

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