DipHE Data Science

Program Overview

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The Diploma of Higher Education (DipHE) in Data Science is a two-year program that equips students with the fundamentals of data science and data analysis. It combines statistics, machine learning, Artificial Intelligence, and data analysis to empower students to extract insights from data. The program emphasizes hands-on learning and practical application of skills and knowledge, preparing students for entry-level positions in the growing field of data science.

Program Outline

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Degree Overview:

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Overview:

The Diploma of Higher Education (DipHE) in Data Science is a two-year, fast-paced program designed to equip students with the fundamentals of data science and data analysis. It combines statistics, machine learning, Artificial Intelligence, and data analysis to empower students to extract insights from data.

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Objectives:

• Introduce students to the fundamentals of data science and data analysis.
• Provide practical experience working with industry-standard tools, systems, and programming languages.
• Develop the skills and knowledge needed to succeed in industry within data science and data analysis.
• Equip students with problem-solving skills and proficiency in Python.
• Foster practical and analytical approaches to problem-solving.
• Encourage students to enjoy working with data to spot patterns and trends or to solve problems.

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Outline:

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Program Content and Structure:

• The program is designed in collaboration with industry partners to ensure its relevance and meet industry needs.
• The modules are developed as new learning experiences specifically for this program.
• It offers a concentrated two-year experience to prepare students for employment or top-up study.
It emphasizes problem-solving skills and proficiency in Python.
• Databases: This module introduces relational databases and the fundamentals of Structured Query Language (SQL).
Additionally, it explores database design, data security, recovery, and integrity.
• Probability and Statistics: This module delves into the programming language R, widely used in statistics, applying it to assess the performance of companies and performance indicators.
Students undertake practical assessments from companies, evaluating their statistical performance. It covers the principles and theory behind data visualization, best practices, and avoiding misleading visualizations. Practical workshops introduce industry-standard tools for building data dashboards and reports.
• Machine Learning: This module introduces the core concepts of supervised and unsupervised machine learning, enabling students to discover patterns in data and make predictions.
The emphasis is on practical application using Python and libraries like Scikit Learn to implement machine learning algorithms and build predictive models.
• Introduction to Business Intelligence: This module sheds light on business intelligence (BI) systems in organizational scenarios.
It provides a broad set of skills applicable to the origins and evolution of BI systems, as well as distinctions between characters, data, information, and knowledge.

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Year Two:

• Artificial Intelligence & Deep Learning: This module delves into the theory behind neural networks and how deep learning is used within fields like computer vision.
Students build and train their own neural networks, working with complex datasets.
• Professional Practices: This module equips students with the research and professional skills required within the industry.
It involves team working on real-world mathematics problems, reflecting workplace scenarios. Students also learn about the professional body, reflect on their skills and future direction with continuing professional development, and attend library-led courses on CV writing and soft skills development.
• Big Data Analytics: This module explores the challenges and opportunities of Big Data and provides practical skills working with tools and techniques for processing and analyzing it.
It covers a wide range of applications, including text classification, document clustering, sentiment analysis, and chatbots.
• Data Science Project: This module challenges students to apply their data science and analysis skills to a complex dataset and solve a real-world business problem.
They synthesize the knowledge gained throughout the course to develop and justify their own solution, and also develop their written and oral communication skills to communicate results to technical and non-technical stakeholders.
• Digital Leadership & Management: This module explores the role of business leadership and management in digital business scenarios.

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Assessment:

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Assessment Methods:

Students are expected to demonstrate their understanding of the material covered in the modules through their performance in the assessed work.

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Teaching:

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Teaching Methods:

• The program emphasizes practical, problem-based activities and applying learning through hands-on exercises in computer-based laboratories.
• It combines workshops and lectures to introduce the theory and practice its application through individual and group activities.
• Computer-based laboratories provide practical, hands-on experience using a range of industry-standard tools, systems, and programming languages.

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Faculty:

• It emphasizes hands-on learning and practical application of skills and knowledge.
• The modules are developed as new learning experiences specifically for this program, offering a concentrated two-year experience.

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Careers:

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Potential Career Paths:

• Junior Data Analyst
• Data Scientist
• AI Engineer
• Business Intelligence Analyst
• Data Visualization Specialist
• The program prepares students for entry-level positions in these fields.

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Career Outcomes:

• Graduates will be equipped with the skills and knowledge to succeed in the data science field and contribute to the growing demand for data professionals.

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Other:

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Greater Manchester Institute of Technology (GMIoT):

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Digital Leadership & Management:

• This module equips students with leadership skills for the digital business environment.