Introduction to Computer Science I

Description

Introduction: The Introduction to Computer Science courses are designed for students in non-technical programs seeking foundational knowledge in computer science and programming. They offer an accessible introduction to key computing concepts, focusing on practical programming skills, data analysis (in Introduction to Computer Science I ), relational databases, and web technologies and development (in Introduction to Computer Science II ), enabling students to apply computational tools to real-world business challenges and decision-making. Course content: Introduction to Computer Science - Understanding the foundational concepts of computer science, including computational thinking and problem-solving. - Exploration of computer architecture, focusing on the central processing unit (CPU) and memory. - Introduction to algorithms and computational complexity, including space and time complexity. - Data representation and data structures, with an emphasis on how computers process and store information. - Introduction to Programming - A beginner-friendly introduction to programming through Scratch, a visual programming language developed by MIT for educational purposes. - Basic programming concepts, such as functions, conditionals, loops, and event handling, will be covered using Scratch. - Python Programming Fundamentals - Introduction to the Python programming language, covering its syntax, variables, and basic data types (integers, floats, strings). - Control flow mechanisms, including if statements, for and while loops, and functions enabling students to write more complex and dynamic programs. - Understanding and using Python data structures, such as lists, dictionaries, tuples, and sets, to efficiently manage and manipulate data. - File handling in Python, teaching students how to read from and write to files. - Programming Tools - Introduction to essential programming tools that are commonly used in software development. - Terminal and shell commands will be taught to enhance students’ ability to interact with the operating system. - Students will work with Integrated Development Environments (IDEs) for coding, debugging, and testing. - Introduction to Git and GitHub for version control, enabling students to collaborate on projects and track changes efficiently. Learning outcomes - Knowledge: Upon successful completion, students will be able to: - Understand and explain key principles of computer science, such as computational thinking, algorithms, and data structures. - Explain and apply foundational programming concepts, with a focus on Python. Learning outcomes - Skills: Upon successful completion, students will be able to: - Develop, test, and debug Python programs using best practices in coding. - Perform data analysis, visualization, and interpretation using tools such as Pandas, Matplotlib, and Seaborn. - Use integrated development environments (IDEs). - Use programming tools such as GitHub and terminal-based environments for efficient version control and collaboration. General Competence: Students will also develop: - Critical problem-solving skills by applying computational thinking to real-world business problems. - Competence in analyzing, processing, and interpreting complex datasets. - The ability to read and comprehend technical documentation. - Team collaboration skills, especially in technical projects, with a focus on clear communication. Teaching and learning activities: . Software tools: Python Qualifications: Higher Education Entrance Qualification Disclaimer Deviations in teaching and exams may occur if external conditions or unforeseen events call for this. Required prerequisite knowledge: . Assessments: Assessments Exam category: Submission Form of assessment: Submission other than PDF Exam/hand-in semester: First Semester Weight: 100 Grouping: Individual Duration: 30 Hour(s) Exam code: ELE 38081 Grading scale: ECTS Resit: Examination when next scheduled course Type of assessment: Ordinary examination Total weight: 100 Student workload: Activity Duration Comment Teaching 45 Hour(s) Group work / Assignments 124 Hour(s) Prepare for teaching 15 Hour(s) Examination 16 Hour(s) Sum workload: 200 Incoming exchange students at BI Norwegian Business School select courses from BI's official semester-specific exchange course lists. BI defines a full semester as 30 ECTS; bachelor courses are typically 7.5 ECTS each (4 per semester) and master courses are typically 6 ECTS each (5 per semester). Course availability, prerequisites, and timetabling are confirmed via BI's International Office and the Learning Agreement process.