| CODE | CIS3190 | ||||||||||||
| TITLE | Data Analytics | ||||||||||||
| UM LEVEL | 03 - Years 2, 3, 4 in Modular Undergraduate Course | ||||||||||||
| MQF LEVEL | 6 | ||||||||||||
| ECTS CREDITS | 5 | ||||||||||||
| DEPARTMENT | Computer Information Systems | ||||||||||||
| DESCRIPTION | This study-unit is a lecture-based study unit with practicals held in class during the lectures. The topics covered include: - Introduction to Data Analytics: An overview of the field, its importance, and its applications; - Data Collection and Preparation: Techniques for gathering and cleaning data from various sources; - Descriptive Statistics: Methods for summarizing and visualizing data to gain initial insights; - Inferential Statistics: Hypothesis testing, confidence intervals, and regression analysis; - Data Visualization: Creating effective charts, graphs, and dashboards to communicate findings; - Exploratory Data Analysis (EDA): Techniques for exploring datasets to uncover patterns and anomalies; - Data Mining and Pattern Recognition: Methods for discovering hidden patterns in data; - AI and Machine Learning Fundamentals: Introduction to supervised and unsupervised machine learning algorithms; - Predictive Analytics: Building and evaluating predictive models for real-world applications; - Ethical and Legal Issues: Discussion on data privacy, security, and the responsible use of data in analytics; - Data Warehousing. Study-unit Aims: - Develop Data Proficiency: The primary aim is to equip students with the skills and knowledge necessary to proficiently work with data. This includes the ability to collect, clean, and pre-process data for analysis effectively. The includes ETL (extract, transform, and load process in data warehouses); - Foster Analytical Thinking: Promote critical thinking and problem-solving skills by teaching students how to interpret data, identify trends, and draw meaningful insights to inform decision-making; - Predictive Analytics: Students become familiar with the main machine learning concepts and models; - Promote Ethical and Responsible Analytics: Instill a strong understanding of data ethics and privacy, emphasizing the importance of ethical data handling and decision-making throughout the data analytics process. Learning Outcomes: 1. Knowledge & Understanding By the end of the study-unit the student will be able to: - Recall and recognize fundamental data analytics concepts, such as descriptive statistics, data types, and data cleaning techniques; - Explain the underlying principles of statistical analysis, including concepts like probability, hypothesis testing, and statistical inference, and demonstrate an understanding of their relevance in data analytics; - Interpret and translate data visualization techniques, recognizing patterns, trends, and anomalies within datasets to extract meaningful insights; - Comparing and Contrasting: Analyze and differentiate between various machine learning algorithms, understanding their strengths, weaknesses, and typical use cases in data analytics; - Elaborate on ethical and legal considerations in data analytics, articulating the importance of responsible data handling, privacy, and security practices, and how they impact the decision-making process. 2. Skills By the end of the study-unit the student will be able to: - Apply data collection and cleaning techniques to real-world datasets, demonstrating the ability to gather (extract), pre-process, and transform data for analysis; - Analyze data using statistical tools and techniques, and generate meaningful insights and conclusions from the data, showing proficiency in statistical analysis; - Create informative and visually appealing data visualizations and reports using data visualization tools and software, effectively communicating findings to diverse audiences; - Evaluate the performance of machine learning models by measuring accuracy, precision, recall, and other relevant metrics, and make data-driven recommendations based on the results; - Identify and solve complex data analytics challenges by selecting and applying appropriate machine learning algorithms and data analysis methodologies to address real-world problems effectively. Main Text/s and any supplementary readings: - Data Analytics Made Accessible: 2023 Kindle Edition Anil Maheshwar, ASIN: B00K2I2JL8 May 2023 - Course notes available on VLE. |
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| STUDY-UNIT TYPE | Lecture, Independent Study & Tutorial | ||||||||||||
| METHOD OF ASSESSMENT |
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| LECTURER/S | John M. Abela Joseph Bonello |
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The University makes every effort to ensure that the published Courses Plans, Programmes of Study and Study-Unit information are complete and up-to-date at the time of publication. The University reserves the right to make changes in case errors are detected after publication.
The availability of optional units may be subject to timetabling constraints. Units not attracting a sufficient number of registrations may be withdrawn without notice. It should be noted that all the information in the description above applies to study-units available during the academic year 2024/5. It may be subject to change in subsequent years. |
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