Introduction to Computational Analysis and Data Science

This course helps students with no prior knowledge in computer science to think computationally and write small programs to tackle basic and useful problems. It also gives students the fundamentals required for data science problems, including big data, machine learning, and data mining applications.

Course abbreviation: ICADS

Instructor: Dr. Abdelmonem Amer and Dr. Mohamed Tahoun

Dr. Abdelmonem Amer, PhD, Universität Düsseldorf. Dr. Mohamed Tahoun, PhD, Suez Canal University.

Workload: Total workload: 42 hours: 36 hours of lectures/tutorials and 6 hours of self studies.

Entrance requirements: No prior knowledge is required.

Used media: PowerPoint presentations / lecture notes

Objectives

• Think computationally and solve basic problems using programming
• Understand fundamentals of computing and programming
• Learn basic data structures and problem solving approaches
• Gain an overview of data mining and machine learning for data science
• Use Python for data analytics, machine learning, and bioinformatics basics

Competences to be Developed

• Basics of computer software and hardware
• Fundamentals of computing and programming
• Basic data structures and problem solving approaches
• Systems analysis and design
• Data mining and machine learning overview
• Python for big data analysis, data analytics, machine learning, and data engineering

Assessment

• Attendance of at least 75% of lectures/tutorials is required
• Deliver solutions to exercises, questions, and problems within due dates
• Join working groups to improve programming and computational skills
• Prepare and present a PowerPoint presentation on new trends in data science
• Pass an online final written assignment