Degree Structure
College
Computing and Informatics
Department
Computer Science
Level
Undergraduate
Study System
Courses
Total Credit Hours
18 Cr. Hrs.
Duration
Sophomore 3rd and 4th Years
Intake
Fall and Spring
Language
English
Study Mode
Full Time
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Minor Overview
The proposed Minor in Data Analytics at the University of Sharjah is an interdisciplinary program designed to provide students with a comprehensive understanding of data analysis techniques. It is intended to complement various undergraduate majors, enhancing students' academic journey and career prospects by equipping them with valuable analytical skills that are highly sought after in today's data-driven job market. The minor curriculum includes foundational courses in data analytics, practical applications, and hands-on projects, fostering proficiency in data analysis tools and techniques.
Study Plan
What You Will Learn
Exploring various software, tools, and methods used in data analytics, describing the role of data analytics in the decision-making process, applying data analytics techniques to extract insights from data, and analyzing intricate data sets for trends, patterns, and abnormalities.
University Requirements
College Requirements
Minor Requirements
• A minimum CGPA grade of 2.5.
• Sophomore standing.
• Programming Prerequisite: A basic programming course such as: Business Programming, Programming 1 or Prog for Engineers or equivalent.
Course Description
Minor in Data Analytics – Mandatory Courses
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. |
Minor in Data Analytics – Elective Courses
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. |
Career Path
By taking the minor in Data Analytics, students will be able to explore more job opportunities in various fields. Data analytics skills are in high demand across numerous industries, including finance, healthcare, marketing, technology, and government. Graduates with expertise in data analytics can pursue roles such as data analyst, business analyst, data scientist, financial analyst, marketing analyst, and more. These roles involve tasks like interpreting data trends, making data-driven decisions, optimizing business processes, and providing action able insights, which are crucial for organizations looking to improve their performance and competitiveness.

How will you make an impact?
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