Accreditation:
EQF7
MaltaSwitzerlandWisconsinCaliforniaWashington
Workload:
1500 hours | 60 ECTS
Tuition cost:
4,000 USD

Postgraduate Diploma in Data Science

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Kind
Diploma
Area
Computer & Mathematical Science
Mode
Fully Online
Language
English
Student education requirement
Undergraduate (Bachelor’s)
Standard length
12 months
Standard delivery length
8 months
Certificates
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\ Overview

The course teaches students comprehensive and specialised subjects in data science; it develops sophisticated skills in statistics, mathematical modelling, and the ability to code in support of such analyses. It further grounds students in the disciplinary history and methodology of data science, preparing them for either further study or to work as a practitioner in the field. The program prominently features a major capstone project, requiring students to identify a real-world problem that would benefit from a data-driven approach; to collect and prepare the data to address the problem; and to build visualisations in support of their arguments. The combination of rigorous mathematical training with practical approaches gives learners the ability to autonomously further develop their skills after graduation, turning them into lifelong learners of data science methods.

450 hours | 18 ECTS

Tier 1 : Foundational

150 hours | 6 ECTS

Exploratory Data Analysis & Management

150 hours | 6 ECTS

Statistical Inference

150 hours | 6 ECTS

Fundamentals of Predictive Modelling

1050 hours | 42 ECTS

Tier 2

150 hours | 6 ECTS

Data Science In Practice

150 hours | 6 ECTS

Unsupervised Multivariate Methods

150 hours | 6 ECTS

Business Intelligence

150 hours | 6 ECTS

Advanced Predictive Modelling

150 hours | 6 ECTS

Machine Learning I

150 hours | 6 ECTS

Machine Learning II

150 hours | 6 ECTS

Time Series Analysis

150 hours | 6 ECTS

Text Mining and Natural Language Processing

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\ Intended learning outcomes

Knowledge
Knowledge acquired by the learner at the end of the course:
- Students will have a comprehensive knowledge and understanding of data science, mathematical and statistical modelling of data, machine learning, and working with structured and unstructured data. - Students will gain specialised knowledge, including knowledge which is at the forefront of data science, analytics, and modelling, including such topics as computer vision, deep learning, big data, natural language processing, and industry-specific domains. - Students will be able to analyse the societal, regulatory, and ethical contexts for data science and machine learning. - Students will show evidence of a critical understanding of the techniques, methods, and core concepts of data science and analytics - Students will be able to apply data science to solve real-world problems across a variety of use cases and situations. - Students will display original thinking on the basis of the knowledge they gain in the course.
Skills
Skills acquired by the learner at the end of the course:
- Students will develop advanced, innovative, and multi- disciplinary problem-solving skills, - Students will communicate data science clearly and unambiguously to specialised and non-specialised audiences - Students will develop advanced abilities related to data analytics operational procedures and the ability to implement them in response to changing environments. - Students will critically evaluate alternative approaches to data science on the basis of academic scholarship and case studies, demonstrating reflection on social and ethical responsibilities. - Students will formulate data-driven analytical judgments and plans despite incomplete information by integrating knowledge and approaches from diverse domains including statistical inference, machine learning, big data, computer vision, deep earning, and natural language processing. - Students will produce work driven by research at the forefront of the domain of Data Science. - Students will enquire critically into the theoretical strategies for applying data science and analytics within business and organizational contexts. - Students will gain facility with modern tools for data analysis, from data visualisation tools such as Tableau through platforms for analysing massive amounts of unstructured data, such as Hadoop or MongoDB, and cloud architectures for data analysis. - Students will develop new skills in response to emerging knowledge and techniques and demonstrate leadership skills and innovation in complex and unpredictable contexts. - Students will gain experience in working collaboratively on data science teams, including such skills as peer review, understanding contributor roles, and team dynamics.
Competencies
Competencies acquired by the learner at the end of the course:
- Students will formulate research-based solutions to practical problems in environments of incomplete information - Students will manage decisions with autonomy in complex and unpredictable environments - Students will organise projects and people in a way that is responsive to changes in the wider data analytics environment - Students will demonstrate learning skills needed to maintain continued, self-directed study

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