HIGHER DIPLOMA IN DATA ANALYTICS

نظرة عامة على البرنامج

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The Higher Diploma in Data Analytics at Maynooth University equips students with the skills to collect, analyze, interpret, and visualize complex data sets. Through a unique interdisciplinary approach, graduates gain a comprehensive understanding of data analytics principles, techniques, and applications, preparing them for careers in various data-related fields. The program emphasizes hands-on practice, real-world case studies, and industry-standard software, ensuring graduates are well-positioned to translate data-driven insights into actionable results.

مخطط البرنامج

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Degree Overview:

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Objective:

The Higher Diploma in Data Analytics (HDipDataAn) aims to equip students with the knowledge and skills to collect, manage, analyze, interpret and visualize large and complex data sets. Graduates will be able to apply their skills to real-world problems in a multitude of disciplines and sectors.

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Overview:

The HDipDataAn is delivered in an inter-disciplinary fashion by faculty members from the Departments of Mathematics & Statistics, Computer Science, and the National Centre for Geocomputation.

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Structure:

• The HDipDataAn is a full-time, 12-month intensive program consisting of 3 semesters, each with 20 credits.
• Semester 1 focuses on foundational topics such as programming, data management and manipulation, introductory statistics and mathematics.
• Semester 2 builds on these foundations by delving deeper into statistical methods, machine learning algorithms and their applications in various domains.
• Semester 3 offers students the opportunity to apply their learned skills to a real-world project in a chosen area of specialization.

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Learning Outcomes:

• Graduates of this program will be adept at:
• Utilizing programming languages (e.g.
Python, R) to extract, wrangle and manipulate data from diverse sources.
• Implementing machine-learning algorithms and interpreting the generated models and their predictions.
• Analyzing data to draw insights, test hypotheses, and inform decision making.
• Communicating effectively, both orally and in writing, on complex data-driven topics to a broad audience.
• Collaborating effectively in a team setting.
• Recognizing the ethical and legal considerations surrounding data collection, management and analysis.

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Other:

• The HDipDataAn program prepares graduates for further study by providing a solid grounding in theory and practice.
• Graduates are well-positioned for careers in various data-related fields or in roles requiring the ability to translate complex information into actionable insights across industry and government organizations.

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Outline:

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Semester 1:

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Modules:

• Programming for Data Science (5 credits): Introduces foundational programming concepts, including data types, control flow, functions and data structures in Python or R.
• Statistical Methods for Data Analysis (5 credits): Covers basic descriptive statistics, probability theory and basic statistical inference techniques like hypothesis testing and confidence intervals.
• Introduction to Data Analysis with R (5 credits): Introduces the R programming environment and its application for statistical analysis and data visualization.

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Semester 2:

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Modules:

• Data Management and Processing (5 credits): Explores data acquisition techniques, cleaning and preprocessing, and data wrangling using advanced techniques for handling complex datasets.
• Statistical Machine Learning (5 credits): Introduces supervised and unsupervised machine learning algorithms and methods, their applications, and model evaluation techniques.
• Introduction to Big Data (5 credits): Explores distributed computing architectures such as MapReduce, Spark, Hadoop and their application for big data processing and analysis.
• Case Studies in Data Analysis (5 credits): This module delves into real-world case examples from diverse disciplines (e.g., finance, healthcare, social media), applying the theoretical concepts and skills covered in previous modules to practical problems and data sets.

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Assessment:

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Assessment methods vary depending on modules and include:

• Continuous assessments like assignments, quizzes and presentations, contributing to a significant portion of the overall grade.
• End-semester examinations contributing to the remaining portion of the overall module grade.
• In some modules, practical projects or in-class presentations may be incorporated.

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Careers:

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Graduates find employment opportunities across diverse sectors including

• Data analytics
• Financial services
• Healthcare
• Technology
• Research
• Government agencies

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Career titles may include:

• Data analyst
• Data scientist
• Statistician
• Data engineer
• Business intelligence analyst

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Teaching:

• The HDipDataAn employs a multifaceted approach to teaching:
• Lectures by subject experts in computer science, statistics, and data analysis provide the fundamental theoretical framework.
• Laboratory sessions and practical exercises allow students to apply the learned concepts to real-world datasets, using industry-standard software and tools.
• The faculty comprises experienced academic staff in their respective areas, actively engaged with research in academia or industry, ensuring students receive current and relevant knowledge.
• Development of strong analytical and problem-solving skills through active engagement with complex datasets and challenges.
• Enhances communication skills by enabling graduates to convey complex data analysis results and findings effectively to various audiences both orally and in written form.