FUNDAMENTALS OF INFORMATION SYSTEMS

The course is structured into 3 submodules:
- Python Programming (for Data Science)
This submodule provides students with the foundational coding skills they need as data scientists. First, the basics of the Python programming language are covered (i.e., built-in data types, functions, I/O, etc.) along with the environment which is used throughout the class (i.e., Jupyter Notebook). Afterwards, students will dig into a set of the most up-to-date data science Python packages; those are: numpy/scipy (for numerical/scientific computing), pandas (for data manipulation), matplotlib/seaborn (for data visualization), and finally scikit-learn (for learning from data). Eventually, at the end of this submodule students will be able to implement all the stages of a typical machine learning pipeline: from collecting data to building predictive models for solving either a classification or a regression problem.
- Databases
This submodule is dedicated to data storage, and it covers the following topics:
Architecture of Database management systems (DBMS). Relational modeling.
Logical and Physical Design of a Relational Database.
SQL Language: Data Definition and Data Manipulation Language, Database Query
The PostgreSQL database: Creation and Definition of a Database, SQL Queries.
- Networking
This submodule allows students to get familiar with computer networking. In particular, it focuses on the following topics:
Networking Fundamentals: Network architectures (OSI Model); TCP and UDP Transport layer protocols; IP Addressing and Routing; Link Layer Forwarding; DNS and DHCP.
Advanced Networking: Virtual LAN (VLAN) and Virtual eXtensible Lan (VXLAN), Software Defined Networking: control, data plane and virtualization; concepts on Cloud Computing: service and deployment models: data centers architectures, topologies, addressing, routing, traffic characteristics; Case Study: The Web of Things (IoT standards and protocols).