Python for Data Science and AI
Three-day hands-on training for data-driven development with Python – from data analysis and algorithm design to deploying AI applications
3 days
Beginner to Intermediate
Python for Data Science and AI
Course Overview
This intensive 3-day course is aimed at developers with basic programming experience who want to dive into the world of data science and AI using Python. The course focuses on practical data analysis, building and evaluating machine learning models, and using popular libraries such as NumPy, Pandas, Scikit-learn, and TensorFlow. A dedicated module covers deployment strategies for turning models into usable AI services.
What You Will Learn
- Data Analysis with Python: Importing, cleaning, transforming and visualizing data
- Algorithm Design: Building and training machine learning models with real datasets
- Core Libraries: NumPy, Pandas, Matplotlib, Scikit-learn, TensorFlow, Keras
- Model Evaluation & Optimization: Metrics, cross-validation, hyperparameter tuning
- Production Deployment: Exporting models, APIs with FastAPI/Flask, containerization
Course Modules
Foundations: Data Science with Python
Working with Data
- Importing data (CSV, Excel, JSON, APIs)
- DataFrames and Series with Pandas
- Data cleaning: handling missing values, duplicates, formatting
- Data transformation and aggregation
- Exercise: Load and clean a real-world dataset (e.g., Titanic, Iris, or a custom one)
Visualization & Statistics
- Basic plotting with Matplotlib and Seaborn
- Descriptive statistics and data distributions
- Correlations, groupings, and pivot tables
- Exercise: Build a data dashboard showing insights from a dataset
Machine Learning & Algorithm Design
Introduction to Machine Learning
- Supervised vs. Unsupervised Learning
- Feature engineering and preprocessing
- Train/test split, model training and prediction
- Frameworks: Scikit-learn, Keras for beginners
Model Development
- Classification: Logistic Regression, Decision Trees, Random Forest
- Regression: Linear Regression, Ridge/Lasso
- Clustering: K-Means, DBSCAN
- Exercise: Train and evaluate multiple models on a sample dataset
Model Evaluation
- Accuracy, precision, recall, F1-score
- Confusion matrix and ROC curves
- Cross-validation and overfitting avoidance
- Exercise: Optimize a model using GridSearchCV
AI Application Deployment
From Notebook to Application
- Exporting trained models: Pickle, Joblib, SavedModel
- Creating an API endpoint with FastAPI or Flask
- Building a simple frontend or CLI tool for inference
- Exercise: Wrap a model in an API and test predictions with HTTP requests
Production Readiness
- Model versioning and documentation
- Environment management with virtualenv/conda
- Deployment via Docker
- Logging, error handling, and monitoring basics
- Exercise: Containerize and run an AI model as a local microservice
Prerequisites
- Basic programming knowledge (any language, ideally some Python)
- Understanding of data structures, loops, functions, and basic logic
- No prior experience in data science or machine learning required
Who Should Attend
- Developers and software engineers exploring data-driven applications
- Junior data scientists and ML enthusiasts starting their AI journey
- Technical professionals working with business data and KPIs
- Teams aiming to prototype or deploy AI solutions in production