"Information is the oil of the 21st century, and analytics is the combustion engine" - P. Sondergaard. Are you a student in Business, Engineering, or Science? Do you want to add a new dimension to your areas of expertise to boost your career? You can do more by enrolling in the new DATA ANALYTICS (DA) minor. The demand for data analytics expertise has grown rapidly over the past few years and is expected to grow even faster in the coming years. Students graduating with a data analytics minor are set to position themselves to bridge the gap in their chosen field.
Graduates of this minor will be able to use their acquired skills across various industries and in the non-profit and government sectors. Telecommunication, banking, financial services, environmental sector, energy, biomedical, police, biology, bioinformatics, and physics are but a few areas in which you can operate. The minor shall also serve as good preparation for further and more advanced graduate and professional studies in Data Analytics and related areas.
At the end of this minor, the student is expected to demonstrate ability to:
This minor is offered to all RHU students except CCE students. Early in their major, interested RHU students must fill in the appropriate form declaring they will minor in Data Analytics while completing their regular major.
This minor is structured to accommodate undergraduate RHU students in the BE or BS program from different disciplines (engineering, computer science, business). It may also Page 153 be offered to students with a BS/BE from other universities subject to a case-by-case evaluation of their transcripts and other specific RHU requirements. Early in their studies, interested students must declare their intention to seek a minor in DA by filling out the pertinent minor declaration form and informing their advisor.
To complete the DA minor, a student must
It should be noted that common courses between student major requirements and data analytics minor requirements are counted to fulfill the minor requirements. The student must complete at least six credit hours of coursework that are not counted toward the requirement for their major or any other minor.
In today’s world, most sectors and industries involve some form or another of data analysis. Therefore, minor holders would be well suited to work in various sectors, including but not limited to telecommunication, banking, financial services, environmental, energy, biomedical, police, biology, and physics.
Moreover, minor holders may also assume specialized roles such as data scientist, data analyst, data engineer, etc.
Enterprise Resource Planning (ERP) systems are a major investment for businesses. These systems are essential for maintaining competitiveness, meeting customer demands, and improving efficiency and flexibility in a global marketplace. By adopting ERP, companies can streamline their operations, align with industry best practices, and fully utilize integrated data resources. This course will provide students with the skills and knowledge needed to effectively plan, design, and implement ERP systems.
Prerequisite: COSC231.
This course introduces business intelligence as computerized support for managerial decision-making. It concentrates on the theoretical and conceptual foundations of business intelligence and commercial tools and techniques available for effective decision support. It focuses on extracting business intelligence from data sets for various applications, including reporting and visual analytics in multiple domains, including web and business analytics, to aid decision-making processes. Provides hands-on experience with various business intelligence software for reporting and building visualizations and dashboards.
Prerequisite: Senior Standing & BADM 350
Digital marketing has evolved from a peripheral element of organizational marketing to one that is the hub of customer-centric communications in an increasingly multi-channel environment. This course covers digital marketing topics like social media, email and mobile marketing, search engine optimization, paid search, and content marketing. It explains the principles of digital marketing and the major factors involved with implementing, measuring, and evaluating successful campaigns that utilize digital marketing channels.
Prerequisite: Senior Standing
This course will introduce students to managing their databases, querying them, and managing data warehousing. Students will also learn advanced programming tools, including bigtable, NoSQL, R, Python, SCALA, MapReduce, and ElasticSearch, and apply these tools to address big data issues.
Prerequisite: CCEE 315 or equivalent.
This course introduces the principles, models, and applications of computer vision. The course will cover image structure, projection, stereo vision, and the interpretation of visual motion. Case studies of industrial (robotic) computer vision applications, including visual navigation for autonomous robots, robot hand-eye coordination, and novel man-machine interfaces.
Prerequisite: CCEE 214 or COSC 214.
This course introduces the student to natural language processing (NLP). The student is first introduced to word and sentence tokenization. The student then uses the learned skills to implement systems for text classification and sentiment analysis, spelling correction, information extraction, parsing, meaning extraction, and question answering, Machine learning algorithms, as well as algorithms like n-gram language modeling, naive Bayes and maxent classifiers, sequence models like Hidden Markov Models, probabilistic dependency and constituent parsing, and vector-space models of meaning will be introduced as needed for the above NLP applications.
Prerequisite: CCEE 214.
In this course, advanced data mining topics will be covered, namely, classification (linear discriminant analysis, quadratic discriminant analysis, neural networks), combining multiple learners (bagging, boosting, cascading, stacking), dimensionality reduction (principal component analysis, linear discriminant analysis, subset selection), deep learning, anomaly detection, and reinforcement learning.
Prerequisite: CCEE 564 or equivalent.
This course consolidates algorithm design and programming techniques. It provides an extended study of object-oriented programming properties, data structures, and data abstraction and an introduction to complexity consideration.
Prerequisite: COSC 215.
This course introduces students to the basic knowledge representation and learning methods of artificial intelligence. The emphasis will be on understanding the fundamental artificial intelligence concepts, as well as being able to practically apply the corresponding approaches in solving practical problems and developing useful software applications. Covered topics include Intelligent agents, informed and uninformed search strategies, and adversarial search. The Python language libraries will also be introduced.
Prerequisite: COSC 214. Equivalent to CCEE 562.
Data is becoming the fuel of the 21st century, and acquiring data processing and analysis skills is becoming necessary. This course introduces data science processes focusing on web scraping as an application. The course will combine different domains, i.e., web programming, system programming, and machine learning. In particular, the course focuses on analyzing the HTML code of webpages using Python, analyzing the available information, and generating dashboards.
Co-requisite: COSC 333 (or CCEE 411) and MATH 351 (or BADM 250).
This course enables students to understand why the Big Data Era has come to be. Students will become conversant with the terminology and the core concepts behind big data problems, applications, and systems. Students will learn how to make Big Data useful in their business or career. Students will be introduced to one of the most common frameworks, Hadoop, which has made big data analysis easier and more accessible -- increasing the potential for data to transform our world.
This course introduces students to managerial decision analysis using quantitative tools. The course will introduce students to using and building mathematical models to help managers make informed decisions. The focus is on the applied aspects of statistics and math. As such, the course will cover the basics of probabilistic and statistical techniques, decision analysis, linear programming, optimization, forecasting, and waiting-line theory.
Prerequisite: BADM 250 or MATH 351.
This course introduces students to the basic knowledge representation and learning techniques. The emphasis is understanding the data mining process, applying the corresponding approaches to solving practical problems, and developing intelligent software applications. The course covers several topics about classification, prediction, and clustering.
Prerequisites: COSC 214; MATH 351 or BADM 250. Equivalent to CCEE 564.
Probability and conditional probability, Discrete and continuous random variables, marginal distributions, expectation, variance-mean-median-covariance and correlation, conditional expectation, Normal distribution, Sampling distribution, Prediction and confidence intervals, Hypothesis testing, and regression line and correlation coefficients.
Prerequisite: MATH 211.
If you have a query about a specific major or application, please contact the relevant Administrative Assistant.
Administrative Assistant Tel: +961 5 60 30 90 Ext. 701
E-mail: da_cas@rhu.edu.lb