Training that keeps pace with innovation, for a world that changes fast.

background

Python: from Basics to Data Analysis

Learn Python from scratch to Machine Learning: from basics to data analysis and first algorithms with NumPy, Pandas, Seaborn and Scikit-Learn.

Best Seller Icon Bestseller
  • Last update 02/2026
  • English
  • Max 5 per corso
  • Final certificate

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.

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

Author Images
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.

Other Courses by Barbara

Power BI Discover the Course
Power BI: from Zero to Expert.
Excel Discover the Course
Excel: work better, not more.
Video Images
Watch the course presentation
You will be able to review and confirm payment in the next step
  • Course Type
  • Available Dates
  • EnrolledMax 5
  • LanguageEnglish
  • Duration35 h
  • CertificateYes
  • Download MaterialsYes
FAQ

Frequently Asked Questions

Find answers to the most frequently asked questions about the courses.

To enroll in the course, go to the tab on the right, choose the course type you prefer (Standard, Evening, or Weekend), select one of the available dates, and click the 'Enroll' button. Fill out the form with all your details, such as first name, last name, email address, and phone number, then proceed with the payment. Once completed, you will receive an email confirming your enrollment along with all the information you'll need for the course.

Live course registrations follow a tiered system with progressive pricing:
  • Early Bird Rate: up to 30 days before the course starts, with a 20% discount
  • Standard Rate: from 30 days before until registration closes
  • Registration Deadline: 5 days before the course starts
We recommend enrolling as early as possible to take advantage of the Early Bird discount and secure your spot, as courses have a limited number of participants.

Usually, you won't find pre-scheduled dates for evening or weekend courses, as these formats are activated exclusively upon request. If you're interested, you can still select the course and proceed with the enrollment just like with any other option. Once your enrollment is complete, I will promptly schedule a date, which will be set with at least 30 days' notice before the course begins, to allow easier planning for all participants. This approach allows me to offer flexible and personalized solutions tailored to the actual needs of the attendees.

The cancellation policy includes:
  • More than 14 days before the start: full refund (100%)
  • From 13 to 7 days before: voucher for the full amount usable for other courses
  • Less than 7 days before: voucher for 50% of the amount
  • No-show without notice: no refund
Right of withdrawal: the 14-day right of withdrawal does not apply once the course has started. For more details, please see Terms and Conditions.

This course is designed with an extremely practical approach: the goal is to help you acquire concrete skills that are immediately applicable in your daily work. It doesn't follow the official course curriculum, but it enables you to master the tool effectively.

However, I am certified in all the tools I teach (including the Microsoft Certified Trainer qualification for Microsoft products) and I am also available to deliver official courses that specifically prepare for certification exams. If you're interested in this path, contact me or write to me at info@numberslab.net for more information.

Yes, the price you see is exactly what you'll pay. There are no additional costs, taxes, or hidden fees. The displayed price includes everything: full course access, learning materials, and certificate of attendance.

For your convenience, I have included specific FAQs in each section of the site, customized based on the topics covered. I recommend also consulting the other pages to find more detailed answers to your questions.

Haven't found what you were looking for? Write to me at info@numberslab.net or use the contact form that you can find here.