Python for Data Modelling Training Course

This course introduces participants to the use of Python for developing and applying data modelling techniques. It covers Python programming essentials for data analysis, statistical modelling, predictive modelling, and machine learning. Participants will gain hands-on experience in preparing data, building models, validating outcomes, and applying models to solve business and research problems. The course blends theoretical knowledge with practical exercises using popular Python libraries and tools.

Target Groups

• Data analysts and data scientists
• Business intelligence professionals
• Statisticians and quantitative researchers
• IT and software professionals moving into analytics
• Finance, marketing, and operations analysts
• Students and professionals pursuing careers in data science
• Anyone interested in learning data modelling with Python

Course Objectives

By the end of this course, participants will be able to:

• Understand the role of data modelling in analytics and decision-making.
• Apply Python for data exploration, transformation, and preparation.
• Build and validate statistical and predictive models using Python libraries.
• Use regression, classification, clustering, and time series techniques.
• Evaluate models with appropriate metrics and validation techniques.
• Implement feature engineering and dimensionality reduction methods.
• Apply machine learning algorithms for advanced data modelling.
• Automate workflows for reproducibility and scalability.
• Communicate model results effectively using visualizations.
• Solve real-world business problems using Python data modelling approaches.

Course Modules

Module 1: Introduction to Python for Data Modelling

• Overview of Python for analytics and modelling
• Setting up the Python environment (Jupyter, Anaconda, IDEs)
• Essential Python packages: NumPy, Pandas, Matplotlib, Scikit-learn
• Basics of Python programming for data handling

Module 2: Data Preparation and Cleaning

• Importing and handling datasets in Python
• Data wrangling with Pandas
• Handling missing values and outliers
• Feature scaling, normalization, and encoding

Module 3: Exploratory Data Analysis (EDA)

• Descriptive statistics and data distributions
• Correlation analysis and feature relationships
• Data visualization with Matplotlib and Seaborn
• Identifying trends and patterns in data

Module 4: Introduction to Statistical Modelling

• Linear regression concepts and implementation
• Logistic regression for classification tasks
• Model assumptions and diagnostics
• Case study applications in finance and business

Module 5: Machine Learning for Data Modelling

• Supervised vs. unsupervised learning
• Decision trees, random forests, and ensemble methods
• K-means and hierarchical clustering
• Model selection and hyperparameter tuning

Module 6: Advanced Modelling Techniques

• Regularization: Lasso and Ridge regression
• Principal Component Analysis (PCA) and dimensionality reduction
• Neural networks basics with Keras/TensorFlow
• Time series forecasting with ARIMA and Prophet

Module 7: Model Evaluation and Validation

• Train-test split and cross-validation techniques
• Confusion matrix, precision, recall, and F1-score
• ROC curve and AUC for model performance
• Overfitting, underfitting, and bias-variance trade-off

Module 8: Feature Engineering and Optimization

• Creating and transforming features
• Feature selection methods
• Handling categorical and text data
• Optimizing models with GridSearchCV and RandomizedSearchCV

Module 9: Automating and Deploying Models

• Saving and loading models with Pickle/Joblib
• Workflow automation in Python
• Introduction to model deployment frameworks (Flask, FastAPI)
• Version control and reproducibility

Module 10: Case Studies and Practical Applications

• End-to-end data modelling project using Python
• Business-focused modelling (finance, marketing, operations)
• Interpreting and presenting model outcomes
• Best practices for production-ready models