About
Data science is applicable to a myriad of professions, and analyzing large amounts of data is a common application of computer science. This course empowers students to analyze data, and produce data-driven insights. It covers all areas needed to solve problems involving data, including preparation (collection and integration), presentation (information visualization), analysis (machine learning), and products (applications).
This course is a hybrid of a computing course focused on Python programming and algorithms, and a statistics course focusing on estimation and inference. It begins with acquiring and cleaning data from various sources including the web, APIs, and databases. Students then learn techniques for summarizing and exploring data with spreadsheets, SQL, R, and Python. They also learn to create data visualizations, and practice communication and storytelling with data. Finally, students are introduced to machine learning techniques of prediction and classification, which will prepare them for advanced study of data science.
Throughout the course, students will work with real datasets (e.g., economic data) and attempt to answer questions relevant to their lives. They will also probe the ethical questions surrounding privacy, data sharing, and algorithmic decision making. The course culminates in a project where students build and share a data application to answer a real-world question.
Global Recognition
Our accreditation through the Malta Further and Higher Education Authority (MFHEA) provides a solid foundation for credential recognition worldwide.
How students have found success through Woolf
Course Structure
About
Data science is applicable to a myriad of professions, and analyzing large amounts of data is a common application of computer science. This course empowers students to analyze data, and produce data-driven insights. It covers all areas needed to solve problems involving data, including preparation (collection and integration), presentation (information visualization), analysis (machine learning), and products (applications). This course is a hybrid of a computing course focused on Python programming and algorithms, and a statistics course focusing on estimation and inference. It begins with acquiring and cleaning data from various sources including the web, APIs, and databases. Students then learn techniques for summarizing and exploring data with spreadsheets, SQL, R, and Python. They also learn to create data visualizations, and practice communication and storytelling with data. Finally, students are introduced to machine learning techniques of prediction and classification, which will prepare them for advanced study of data science. Throughout the course, students will work with real datasets (e.g., economic data) and attempt to answer questions relevant to their lives. They will also probe the ethical questions surrounding privacy, data sharing, and algorithmic decision making. The course culminates in a project where students build and share a data application to answer a real-world question.
Teachers
Intended learning outcomes
- Have a knowledge of key strategies for interpreting data to make informed predictions about possible outcomes.
- Theoretical and practical techniques for data collection and management, including acquiring and cleaning data from the web, APIs, and databases.
- Ability to work with real datasets to answer questions set in the module.
- Techniques for summarizing and exploring data with spreadsheets, SQL, R, and Python.
- Create data visualizations, and practice communication and storytelling with data.
- Communicate insights on the basis of data sets in a well-structured, coherent format.
- Communicate effectively about ethical issues surrounding data privacy, data sharing, and algorithmic decision making.
- Make judgments based on knowledge of the rules and conventions for the proper use of advanced data sets and demonstrate knowledge of the social and ethical issues relevant to technology.
- Consistently evaluates own learning and identifies learning needs.
- Show creativity and initiative while working with real datasets (e.g., economic data) and providing valuable answers.
- Possess the academic competences to undertake further studies in data science with a high degree of autonomy.
- Solve problems involving data, including preparation, presentation, analysis, and products.
Entry Requirements
Application Process
Submit initial Application
Complete the online application form with your personal information
Documentation Review
Submit required transcripts, certificates, and supporting documents
Assessment
Note: Not required by all colleges.
For colleges that include this step, your application will be evaluated against specific program requirements.
Interview
Note: Not all colleges require an interview.
Some colleges may invite selected candidates for an interview as part of their admissions process.
Decision
Receive an admission decision
Enrollment
Complete registration and prepare to begin your studies
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