Python: From Basics To Data Analysis.

About the Course

This comprehensive course takes you from Python basics to professional use of libraries for data analysis and first machine learning algorithms, with a practical and step-by-step approach.

What You Will Learn

• Master Python basics: variables, functions, loops, and fundamental data structures
• Create and manipulate multidimensional arrays with NumPy for scientific computing
• Use Pandas to manage, clean, and analyze large datasets efficiently
• Generate random numbers and statistical simulations with NumPy Random
• Work with Series and DataFrame to structure data professionally
• Select and filter data using loc and iloc for precise extractions
• Create professional visualizations with Matplotlib and Seaborn for exploratory analysis and communicate insights
• Implement first Machine Learning algorithms with Scikit-Learn
• Complete practical projects from data analysis to predictive models

Course Prerequisites

• No previous programming knowledge required
• Stable Internet connection
• Recommended: dual monitor or tablet to follow the lesson and replicate the practical exercises on your PC

Projects

Different projects will be developed within the course. Examples from past editions include:

• K-Nearest Neighbors Scatter Plot with Regression Line
• Categorical Regression Plot
• Uniform Distribution Handling Missing Data
• Pie Charts with Matplotlib

Course Content

• Introduction to Python and development environment setup
  • Variables, data types, and fundamental operators
  • Control structures: conditions, loops, and programming logic
  • Lists, dictionaries, and essential data structures
  • Practical exercises
• Introduction to NumPy and importance in scientific computing
  • Creation and access to multidimensional NumPy arrays
  • Array shaping and restructuring techniques
  • Mathematical formulas and vectorization for optimal performance
  • Random number generation and simulations with NumPy Random
• Introduction to Pandas
  • Series and DataFrames
  • Creating and loading DataFrames from different data sources
  • First basic operations with DataFrames
  • Practical exercises
  • Advanced selection for rows and columns with loc and iloc
  • Data cleaning and preprocessing techniques
  • Handling missing and duplicate data in datasets
  • Data grouping and aggregation operations
  • Practical exercises
• Data Visualization
  • Introduction to Matplotlib for data visualization
  • Creating basic charts: lines, bars, and scatter plots
  • Graphic customization with colors, labels, and titles
  • Subplots and advanced layouts for professional reports
  • Seaborn for professional statistical visualizations
  • Creating heatmaps, pairplots, and violin plots
  • Distribution and correlation charts for exploratory analysis
  • Integration between Matplotlib and Seaborn for advanced dashboards
  • Practical exercises
• Machine Learning Fundamentals
  • Fundamental concepts of Machine Learning and types of learning
  • Installation and first steps with Scikit-Learn
  • Data preprocessing for machine learning algorithms
  • Classification algorithms: Decision Tree and Random Forest
  • Linear regression algorithms and performance evaluation
  • Practical exercises
• Integrated Project
  • Cleaning and preprocessing of a real dataset
  • Advanced visualizations to present insights
  • Implementation and evaluation of a machine learning model
  • Practical exercises

Schedule

All times shown refer to the Rome and Madrid time zone.

• Standard:
  • Dates: Updating
  • Time: 9:00 - 13:00 / 14:00 - 17:00
  • Duration: 5 days
• Weekend:
  • Buy and request a date
  • Saturday: 9:00 - 13:00 / 14:00 - 17:00
  • Sunday: 10:00 - 13:30
  • Duration: 3 weekends

Instructor

Barbara Callegari

Microsoft Certified Trainer with over ten years of experience in Data Analysis. Barbara is a highly qualified and multi-certified teacher, collaborating as a consultant for leading national companies. She has trained students at all levels, creating tailored programs that meet specific needs. Her courses are practical and focused on real-world application, emphasizing skills and techniques immediately usable in the workplace.