Computer Science Coursework
Computational Thinking using Python
Course Link: Computational Thinking using Python
Summary
The aim of the X-Series program is to give students an overview of various topics, offering insights into using computation for future career goals. Introduction to Computer Science and Programming Using Python covers computation, Python, basic algorithms, testing, debugging, and introductory algorithmic complexity. Introduction to Computational Thinking and Data Science imparts skills to achieve diverse goals through computation, introducing computational problem-solving topics.
Topic include:
- A Notion of computation
- Some simple algorithms
- Testing and debugging
- An informal introduction to algorithmic complexity
- Data structures
Documentation
Computing in Python 1: Fundamentals and Procedural Programming
Course Link: Computing in Python
Summary
This course covers the fundamentals of computer programming, including topics such as interpreting code, writing programs, evaluating output, working with variables, and using mathematical and relational operators.
Topic include:
- The write-run-debug cycle of writing code, running it, and revising it based on its output
- Procedural programming, or how to write sequential lines of code.
- Variables, their types, and their role in complex programs.
- Mathematical operators for arithmetic operations, exponents, and more.
- Relational operators for evaluating relative values or set membership.
- Boolean operators for resolving complex logical statements
Documentation
Programming Coursework
Introduction to MATLAB
Course Link: Introduction to MATLAB
Summary:
This course introduces computer programming using MATLAB, catering to beginners. MATLAB’s simplicity, versatility, and applicability in engineering and other fields make it an ideal choice. This specialized language enables concise yet potent program development for numerical tasks. The course emphasizes general programming concepts, equipping students with a solid MATLAB foundation that’s valuable across scientific, engineering, financial and industrial domains.
Documentation
Crash Course on Python
Course Link: Crash Course on Python
Summary
This brief course teaches the foundations of Python programming to write simple programs. No prior programming experience is required. The course covers the benefits of programming in IT, writing simple programs, understanding programming’s building blocks, and using this knowledge to solve complex programming problems. The course covers computer program basics with hands-on experience, interactive exercises, and real-world examples.
Topics include:
- Basic Python Data Structures
- Fundamental Programming Concepts
- Basic Python Syntax
- Python Programming
- Object-Oriented Programming (OOP)
Documentation
Programming in Python
Course Link: Programming in Python
Summary
The specialization is aimed towards individuals with no programming background who wish to acquire Python programming skills and gain knowledge about fundamental computer science concepts, enabling them to quickly learn other programming languages. The four courses cover topics ranging from basic concepts to object-oriented design. Upon completion, learners will have the ability to write programs, from small scripts that automate repetitive tasks to larger applications. The specialization provides a solid foundation for learners to advance towards more specialized fields such as Data Science and Artificial Intelligence.
Topics included
- Automating repetitive tasks using python scripts
- Create engaging, graphical based programs using PyGame
- Recursion
- Object-Oriented Programming (OOP)
Documentation
Introduction to Scripting in Python
Course Link: Introduction to Scripting in Python
Summary
In this four course specialization, the goal is to learn key programming concepts in Python 3, which will prepare you to use Python for common scripting tasks. This knowledge forms a strong foundation for a career in data science, software engineering, or other programming-related fields.
Topics included
- Data Analysis
- Python Programming
- Data Visualization (DataViz)
- Python Syntax And Semantics
- Debugging
- Tuple, List and Label
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Understanding and Visualizing Data with Python
Course Link: Statistics with Python
Summary
This course offers an introduction to statistics for learners who want to understand where data comes from, how to manage and explore data, and perform data analysis using the Python programming language. The course covers various topics including data visualization, analysis, and interpretation for univariate and multivariate data. Learners will learn the differences between probability and non-probability sampling, as well as how sample estimates vary and how inferences can be made about larger populations based on probability sampling.
Topics include
- Properly identify various data types and understand the different uses for each
- Communicate statistical ideas clearly and concisely to a broad audience
- Create data visualizations and numerical summaries with Python
- Identify appropriate analytic techniques for probability and non-probability samples
Documentation
Using Python for Research
Course Link: Using Python for Research
Summary
This course acts as an intermediary between basic and advanced Python courses. While introductory courses provide a foundation, they often lack the depth needed for research projects. After refreshing Python basics, the course introduces essential tools for research. With guided introductions and independent exploration, the course helps in applyting Python skills through diverse case studies.
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