Training on Data Mining and Analysis with Python

Module 1: Introduction to Data Mining and Python for Analytics

• Overview of data mining, analytics, and business intelligence.

• Setting up the Python environment (Anaconda, Jupyter Notebook).

• Overview of key libraries: Pandas, NumPy, Matplotlib, Scikit-learn.

• Understanding structured vs. unstructured data.

• Data types, loading, and reading different file formats (CSV, Excel, SQL, JSON).

• Practical Lab: Exploring and loading datasets in Python.

Module 2: Data Cleaning and Preprocessing Techniques

• Identifying and handling missing, inconsistent, and duplicate data.

• Data transformation, normalization, and standardization.

• Feature engineering and encoding categorical data.

• Handling outliers and skewed data.

• Automating cleaning workflows for large datasets.

• Case Study: Preparing demographic survey data for analysis.

Module 3: Exploratory Data Analysis (EDA)

• Statistical summaries and distribution analysis.

• Visualization for exploration (histograms, boxplots, heatmaps).

• Detecting relationships and correlations in data.

• Identifying key drivers and hidden trends.

• Feature selection using EDA results.

• Hands-on Project: Analyzing customer behavior data.

Module 4: Data Mining Techniques and Algorithms

• Introduction to classification, clustering, and association rules.

• Supervised vs. unsupervised learning.

• Implementing decision trees, Naïve Bayes, and K-means.

• Association rule mining (Apriori and FP-Growth).

• Interpreting model results for business insights.

• Practical Exercise: Clustering and classifying social program data.

Module 5: Predictive Analytics and Machine Learning Models

• Building regression and classification models.

• Model training, testing, and evaluation using cross-validation.

• Key metrics: accuracy, precision, recall, F1-score, ROC curves.

• Feature importance and model optimization.

• Model deployment basics.

• Lab Activity: Predicting economic indicators using regression models.

Module 6: Dimensionality Reduction and Feature Selection

• Handling high-dimensional data challenges.

• Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA).

• Feature importance ranking and correlation-based elimination.

• Evaluating feature subsets for model efficiency.

• Project: Simplifying a high-dimensional dataset for efficient model building.

Module 7: Advanced Data Visualization and Storytelling

• Visualization best practices for analysis and reporting.

• Using Matplotlib, Seaborn, and Plotly for advanced visuals.

• Building interactive dashboards and charts.

• Storytelling through data — crafting insights for decision-makers.

• Hands-on Project: Developing an executive dashboard in Plotly Dash.

Module 8: Time Series and Text Data Mining

• Understanding time series components and trends.

• Forecasting using ARIMA and Prophet models.

• Introduction to text mining and sentiment analysis.

• Tokenization, frequency analysis, and visualization of text data.

• Exercise: Analyzing social media sentiment and forecasting engagement.

Module 9: Automating Data Analysis Workflows

• Building reusable Python scripts for data mining tasks.

• Scheduling automated reports and dashboards.

• Integrating APIs for live data collection and updates.

• Version control and reproducibility using Git.

• Lab Session: Automating monthly report generation using Python.

Module 10: Ethics, Data Governance, and Real-World Projects

• Ethical principles in data collection, analysis, and sharing.

• Bias detection and mitigation in data-driven models.

• Data privacy laws (GDPR, data protection policies).

• Final group project: Designing and presenting a complete data mining pipeline.

• Capstone Presentation: Applying data mining to solve a real business/research problem.