Applied Data Science Course (online)

  • Course level: Intermediate  
Group Online Sessions (GMT) Duration Registration


Mondays 14h00 to 18h00 (GMT)
Wednesdays 14h00 to 18h00 (GMT)

36 Hours
From : 03/06/2024 00:00
To : 26/07/2024 22:00

Available Seats: 30
Registration Deadline
31/05/2024 23:59


This is a project-based course designed for trainees with some programming background interested to learn machine learning techniques and predictive analytics. As a programming language, we will Python to develop the machine learning algorithms. This course is certified by EIT Tech Talent, European Union.


On successful completion of this course, students will be able to:

  1. Understand and describe the essential concepts of AI and Machine Learning
  2. Identify and implement appropriate Machine Learning techniques to build and validate the predictive models
  3. Clean the datasets and prepare them for machine Learning projects
  4. Understand and implement the supervised and un-supervised machine learning algorithms
  5. Practically design and run the Machine Learning projects in Python
  6. Practice all the techniques they learned through a final project applied to real-world datasets.


  1. Introduction to Data Science: Concepts and Applications
  2. Fundamentals of Machine Learning
  3. Machine Learning pipeline
  4. Data Preparation Techniques
  5. Feature Engineering Techniques
  6. How to manage a data science project
  7. How to design a machine learning project
  8. Train and Test Datasets
  9. Data preprocessing, Cleaning, and visualization
  10. Supervised and unsupervised algorithms
  11. Building and validating the Regression models
  12. Building and validating the Decision Trees
  13. Building Clustering and Classification Models
  14. Tuning the machine learning models
  15. Final Project: Applying Machine Learning techniques to  real datasets and building the predictive models

Registration fee :
€ 1700

Target audience

  • Company workforce, students and graduates in:
  • • Computer science and IT
  • • All engineering subjects
  • • Business, Accounting and Finance
  • • Bioinformatics & Biotechnology
  • • And other related fields


  • Basic Python
  • Basic Knowledge in IT