Minor in Data Analytics

1503222

Introduction to Data Analytics

Credit hour 3

Prerequisite:

1501100 Introduction to IT

Description: The course provides an introduction to data analytics, focusing on the fundamental concepts, principles, techniques, and technologies available for generating actionable information and business value from massive quantities of data. Topics covered include modeling under uncertainty, regression models, time series analysis and forecasting, optimization and simulation models, data mining clustering and classification, and predictive modeling with data visualization. Students will be introduced to various software tools and technologies used to analyze data and generate critical insights to support decision making. Students will learn how to apply these tools, such as Stata, RapidMiner, Power BI, Tableau, and Weka, on a hands-on, laboratory environment. The course requires a semester project.

1503230

Database Management

Credit hour 3

Prerequisite:

1503130 Introduction to BIS OR Introduction to Data Analytics

Description: This course provides students with the theoretical foundation and technical skills required to implement a business database solution on a relational database management system using Microsoft Access (latest version). The course teaches students how to create tables, perform queries, create forms and reports according to storage needs and constraints and how to retrieve and modify specific data by using the Access (latest version) & SQL components. Finally, students will learn how to develop and test a database application.

1503232

Programming for Data Analytics

Credit hour 3

Prerequisite:

1501100 Introduction to IT

Description: This course equips students with essential programming skills for effective data manipulation, analysis, and visualization. Through a blend of theoretical concepts and hands-on exercises, students will study basic programming constructs, delve into data manipulation, and create insightful visualizations with the used programming language. Key topics include data wrangling and manipulation where students learn techniques for cleaning and preparing data for analysis using libraries like Pandas and NumPy, data visualization to allow students to create insightful visual representations using tools such as Matplotlib and Seaborn, statistical programming to interpret data, applying statistical. In addition, students will explore basic machine learning models, such as regression and classification, as well as big data processing. Additionally, the course covers the Automation of Data Tasks, where students learn to streamline repetitive processes, such as data updates or report generation, using scripting and scheduling tools The final weeks emphasize real-world applications, challenging students to apply their programming skills to solve practical data analytics problems. By the end of the course, students will have a solid foundation in programming for data analytics, enabling them to handle and analyze data efficiently.

1503331

Applied Data Mining

Credit hour 3

Prerequisite:

1503222 Introduction to Data Analytics AND 1503232 Programming for Data Analytics

Description: This course explores data mining techniques tailored for business applications, focusing on critical areas such as customer segmentation, predictive analytics, and recommendation systems. Designed for students with a foundation in business analytics, it aims to deepen their understanding by applying these techniques to real-world business challenges. Through hands-on projects and analysis of industry case studies, students will gain practical experience and learn to extract meaningful insights that can drive strategic decision-making within organisations. The course emphasises practical skills in utilising data mining tools, allowing students to bridge the gap between theoretical knowledge and applied business analytics.

1503430

Big Data and Business Intelligence

Credit hour 3

Prerequisite:

1503230 Database Management

Description: This course combines the Business Intelligence (BI) and big data. Big data is used to store, process, and analyze huge volumes of data. The course will introduce students to BI applications. Students will learn how to apply BI to turn data into insights that deliver value - through methodologies, algorithms, and approaches for big data analytics. This course will also cover how some of the world’s most successful companies use big data and business analytics to acquire BI.

1503443

Advanced Data Analytics

Credit hour 3

Prerequisite:

1503222 Introduction to Data Analytics AND 1503232 Programming for Data Analytics

Description: This course prepares students to the concepts of advanced techniques and methodologies in data analytics. It focuses on predictive modeling, machine learning algorithms, big data analytics, and applications in various domains. Students also will learn the essentials of big data analytics. State-of-the-art computational frameworks (e.g., Hadoop, Spark) for big data will be introduced. Students will gain hands-on experience with advanced analytics tools and apply them to large datasets.

1503444

Special Topics in Data Analytics

Credit hour 3

Prerequisite:

1503331 Data Mining AND 1503222 Introduction to Data Analytics

Description: Data Analytics is an emerging area, the primary aim of this course is to introduce students to new developments in this area with a focus on emerging technologies and trends in Data Analytics. This course also emphasizes security, ethics, and privacy aspects related to Big Data in industry, business, academia, and research settings.

1503445

Data Visualization

Credit hour 3

Prerequisite:

1503230 Database Management

Description: This course provides students with an introduction to the fundamentals and practice of data visualization. It explains how to build high-quality figures from data using powerful and reproducible methods, and how to think about data visualization in an effective way. The course demonstrates how to create plots, starting with summaries of single variables and moving on to more complex graphics, in addition to some advanced visualization techniques, including dashboards, maps, networks, and graphs.