22 practice based lessons
In this module, students will be placed in the role of a real
data scientist, tasked with solving a problem. They will follow the instructor's guidance and go through the steps taken by a data scientist - from obtaining the data and describing the problem to solving it.
This discipline, also known as data science, is one of the branches of computer science that has thrived in the past decade and serves as the foundation for many of the capabilities that the internet offers today. It is closely related to popular concepts such as
artificial intelligence, machine learning, big data, predictions, deep learning, and more. It is a field in which major companies like Google, Facebook, Amazon, Apple, IBM, Microsoft, and others invest substantial amounts of money.
Upon completing the module, students
will learn: - Various methods of data structuring.
- Different types of data with their advantages and disadvantages.
- How to explore a dataset and consider relevant criteria.
- Simple prediction algorithms (decision tree, Random Forest, SVM, KNN).
- The concept of statistics for result interpretation.
- Simple clustering/grouping algorithms (K-Means, DBScan) with their pros and cons.
- Ways to present results and basic types of graphs.
- Soft skills: Working with information, managing attention, and understanding one's role in a teamwork environment.