Data
Data Science
Our Data Science course helps you to master data analysis and AI [Artificial Intelligence] training, including practical experience with Python libraries, machine learning, and deep learning. With experienced instructors, you’ll develop skills to tackle complex data challenges and help to take the lead in introducing new digital elements to any industry.
Why this track
Built for learners who want practical business-facing data skills, not theory only.
Core path
- Python for data
- EDA
- ML basics
- Presentation
Course curriculum
What you will learn
Data Analysis Module
Data Analysis Module
Start with IT and programming essentials, then move into Python for data work. Topics covered: IT Fundamentals, Introduction to IT, Hardware & Software Basics, Network Basics, Programming Basics, Python Core — including data types, string operations, lists, dictionaries, and control flow. You will practise data manipulation and begin exploratory data analysis with real datasets.
Machine Learning Module
Machine Learning Module
Learn to train, evaluate and compare predictive models using core supervised learning techniques. Topics covered: Machine Learning fundamentals, Supervised Learning, Naive Bayes, Linear Regression, Logistic Regression, Support Vector Machines (SVM), K-Nearest Neighbor (KNN), and Decision Trees. You will work with scikit-learn, understand model evaluation metrics, and apply these algorithms to practical classification and regression tasks.
Artificial Intelligence & Deep Learning Module
Artificial Intelligence & Deep Learning Module
Extend your ML skills into neural networks and applied AI. Topics covered: Neural network architecture, backpropagation, activation functions, deep learning frameworks, Convolutional Neural Networks (CNNs) for image recognition, sequence and time-series modelling, and transfer learning techniques. Coursework includes hands-on AI projects to consolidate and demonstrate your skills.
FAQ
Common questions
Do you need a Maths background to be a Data Scientist?
No, it is not necessary. Basic knowledge of Mathematics at high school level is enough. Although machine learning algorithms have a mathematical background, you do not need to master this mathematical content to use these algorithms. However, you may need to learn more about these topics if you want to do academic work or wish to deepen your knowledge of the mathematical side of things. Apart from that, you do not need an advanced knowledge of mathematics to work as a data scientist, data analyst, or in a similar position in the industry. The basic concepts of statistics necessary to understan
Why should I choose Data Science?
1. There is a huge need for professsionals in this field around the world. 2. More than 150 zettabytes of data will need to be analysed by 2025 alone. (statista.com) 3. According to the 3rd World Economic Forum, the top three professions of the near future will be:
What opportunities are offered in the training apart from the courses?
1. A one-year as an intern project in companies in Europe at the end of the course. 2. The opportunity to get a job and work in Silicon Valley Artificial Intelligence projects with professors from Stanford University, 3. Support and individual mentoring to help you develop your career (Resumé or CV, Interview, LinkedIn), small study groups, and much more...
How much does a Data Scientist usually earn?
In this field, entry-level salaries begin at $124,000 a year (in the USA).
Ready to enroll?
Need help choosing the right path?
Our advisory team can help you compare programs, schedules and enrollment routes without unnecessary back-and-forth.
Why choose us?
Let's build your career together.
Working hours
- Mon – Fri: 09:00 – 18:00
- Sat: 10:00 – 14:00
- Online support 24/7 active