- Overview
- Projects
- Contents
- Dates
- Instructor
What You Will Learn And Course Prerequisites
The world of Data Science and Machine Learning is rapidly establishing itself as an increasingly relevant skill in the job market. This comprehensive course will take 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.
- 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
The course follows a progressive teaching method with practical examples, exercises on real datasets and application projects that will allow you to start from scratch and reach Data Science and Machine Learning skills immediately applicable in the workplace.
- 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
Show More
What you will be able to achieve
Different projects will be developed within the course. Here are some realized in past editions.
K-Nearest Neighbors
Scatter Plot with Regression Line
Categorical Regression Plot
Uniform Distribution
Handling Missing Data
Pie Charts with Matplotlib
K-Nearest Neighbors
Scatter Plot with Regression Line
1 / 6
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 to get started
- 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 with Pandas
- Handling missing and duplicate data in datasets
- Data grouping and aggregation operations
- Practical exercises
- 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
- 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: from exploratory analysis to predictive model
- Cleaning and preprocessing of a real dataset
- Advanced visualizations to present insights
- Implementation and evaluation of a machine learning model
- Practical exercises
Upcoming Dates
- Standard:
- Dates updating
- Weekend:
- Buy and request a date!
Schedule
- Standard:
- 9:00 - 13:00 / 14:00 - 17:00
- Duration: 5 days
- Weekend:
- Sat 9:00 - 13:00 / 14:00 - 17:00
- Sun 10:00 - 13:30
- Duration: 3 weekends
- All times shown refer to the Rome and Madrid time zone.
Teacher
Barbara Callegari
Microsoft Certified Trainer
With over ten years of experience in Data Analysis, Barbara is a highly qualified and multi-certified teacher, with solid collaborations as a consultant for leading national companies. She has trained students at all levels, creating tailored programs that meet the specific needs of each participant and client. Her courses are practical and focused on real-world application: not just theory, but skills and techniques that can be put to use immediately in the workplace.
US
