Open Educational Resources | Pao Yue-kong Library, The Hong Kong Polytechnic University

Courseware

Introduction to Computational Thinking and Data Science

6.0002 is the continuation of 6.0001 Introduction to Computer Science and Programming in Python and is intended for students with little or no programming experience. It aims to provide students with an understanding of the role computation can play in solving problems and to help students, regardless of their major, feel justifiably confident of their ability to write small programs that allow them to accomplish useful goals. The class uses the Python 3.5 programming language.

Subjects:: Computing
Keywords:: Computer programming Computer science Python (Computer program language)
Resource Type:: Courseware

Courseware

Introduction to Computer Science and Programming in Python

6.0001 Introduction to Computer Science and Programming in Python is intended for students with little or no programming experience. It aims to provide students with an understanding of the role computation can play in solving problems and to help students, regardless of their major, feel justifiably confident of their ability to write small programs that allow them to accomplish useful goals. The class uses the Python 3.5 programming language.

Subjects:: Computing
Keywords:: Computer programming Computer science Python (Computer program language)
Resource Type:: Courseware

Courseware

Mathematical Modeling Basics

How do populations grow? How do viruses spread? What is the trajectory of a glider? Many real-life problems can be described and solved by mathematical models. In this course, you will form a team with another student and work in a project to solve a real-life problem. You will learn to analyze your chosen problem, formulate it as a mathematical model (containing ordinary differential equations), solve the equations in the model, and validate your results. You will learn how to implement Euler’s method in a Python program. If needed, you can refine or improve your model, based on your first results. Finally, you will learn how to report your findings in a scientific way. This course is mainly aimed at Bachelor students from Mathematics, Engineering and Science disciplines. However it will suit anyone who would like to learn how mathematical modeling can solve real-world problems.

Subjects:: Mathematics and Statistics
Keywords:: Mathematical models
Resource Type:: Courseware

Courseware

Introduction to Functional Programming

Broadly speaking, functional programming is a style of programming in which the primary method of computation is the application of functions to arguments. Among other features, functional languages offer a compact notation for writing programs, powerful abstraction methods for structuring programs, and a simple mathematical basis that supports reasoning about programs. Functional languages represent the leading edge of programming language design, and the primary setting in which new programming concepts are introduced and studied. All contemporary programming languages such as Hack/PHP, C#, Visual Basic, F#, C++, JavaScript, Python, Ruby, Java, Scala, Clojure, Groovy, Racket, … support higher-order programming via the concept of closures or lambda expressions. This course will use Haskell as the medium for understanding the basic principles of functional programming. While the specific language isn’t all that important, Haskell is a pure functional language so it is entirely appropriate for learning the essential ingredients of programming using mathematical functions. It is also a relatively small language, and hence it should be easy for you to get up to speed with Haskell. Once you understand the Why, What and How that underlies pure functional programming and learned to “think like a fundamentalist”, we will apply the concepts of functional programming to “code like a hacker” in mainstream programming languages, using Facebook’s novel Hack language as our main example. This course assumes no prior knowledge of functional programming, but assumes you have at least one year of programming experience in a regular programming language such as Java, .NET, Javascript or PHP.

Subjects:: Computing
Keywords:: Haskell (Computer program language) Functional programming (Computer science)
Resource Type:: Courseware

Courseware

Data Analysis: Take it to the MAX

This course is for all of those struggling with data analysis. You will learn: - Overcome data analysis challenges in your work and research - Increase your productivity and make better business decisions - Enhance your data analysis skills using spreadsheets - Learn about advanced spreadsheet possibilities like array formulas and pivottables - Learn about Excel 2013 features like PowerPivot & PowerMap - Learn to organize and test your spreadsheets

Subjects:: Computing
Keywords:: Electronic spreadsheets -- Computer programs Visual analytics Python (Computer program language) Information visualization Microsoft Excel (Computer file)
Resource Type:: Courseware

Courseware

Advanced Python Programming for GIS

In this course, you will learn advanced applications of Python for developing and customizing GIS software, designing user interfaces, solving complex geoprocessing tasks, and leveraging open source. The course consists of readings, walkthroughs, projects, quizzes, and discussions about advanced GIS programming concepts and techniques, and a final term project. Software covered in the course includes: Esri ArcGIS Pro/arcpy, Jupyter Notebook, Esri ArcGIS API for Python, QGIS, GDAL/OGR.

Subjects:: Land Surveying and Geo-Informatics
Keywords:: Python (Computer program language) Geographic information systems
Resource Type:: Courseware

MOOC

Data Science for Construction, Architecture and Engineering

The building industry is exploding with data sources that impact the energy performance of the built environment and health and well-being of occupants. Spreadsheets just don’t cut it anymore as the sole analytics tool for professionals in this field. Participating in mainstream data science courses might provide skills such as programming and statistics, however the applied context to buildings is missing, which is the most important part for beginners. This course focuses on the development of data science skills for professionals specifically in the built environment sector. It targets architects, engineers, construction and facilities managers with little or no previous programming experience. An introduction to data science skills is given in the context of the building life cycle phases. Participants will use large, open data sets from the design, construction, and operations of buildings to learn and practice data science techniques. Essentially this course is designed to add new tools and skills to supplement spreadsheets. Major technical topics include data loading, processing, visualization, and basic machine learning using the Python programming language, the Pandas data analytics and sci-kit learn machine learning libraries, and the web-based Colaboratory environment. In addition, the course will provide numerous learning paths for various built environment-related tasks to facilitate further growth.