IS611 Advanced Data Science
This course focuses on the theoretical foundations of Data Science. In this course, the steps essential in Data Science (DS) projects are explained. Guidelines on how to plan, manage and implement a successful DS project are discussed. Topics in this course additionally include the business values of DS to organizations, the implication and concerns of attempting to utilize DS in organizations, and the skills needed to be a successful data scientist.
IS612 Advanced Topics in Database Systems
Active assembly, exploration, and preservation of data is strategic to accomplish success in Data Science projects. In this course, students will acquire knowledge to develop, secure, optimize, and administer database systems. The topics include query processing, implementation, and optimization, data relations, storage and file systems, database backup and recovery, self-tuning database systems, data stream systems, concurrency control protocols, transactions creation and maintenance, relational and non-relational databases, database security, data management problems, and distributed database.
IS613 Programming for Data Science
Programming is a critical part of data science. While working with datasets, it is principally significant to be capable to write operational, and well-organized code to help process, clean, organize, consolidate, comprehend, and leverage the data. In this course, students learn the essentials of the programming language Python and study how it can be used to accomplish tasks common in data science projects (e.g. data processing and cleansing). Programming topics covered include the collection of data from various sources, the preprocessing and cleaning of data, and the performing of exploratory analysis on the data. Vital data science libraries in Python such as NumPy and Pandas are learned.
IS621 Advanced Data Mining
Data mining emphasizes leveraging computational methods to recognize patterns, perform prediction and forecasting, and discover knowledge from datasets. In this course, students will study crucial algorithms for selecting and categorizing data, data visualization techniques. Additionally, students learn how to apply machine learning solutions. Data mining algorithms essential for knowledge discovery such as association & sequence rules discovery, memory-based reasoning, clustering, classification and regression decision trees are covered. The technical contents of this course also include providing an overview of data warehousing and on-line analytical processing (OLAP).
IS622 Data Analysis using Applied Statistics
Data analysis is an important step in any data science project. This course offers a firm basis in statistical data analysis techniques and the philosophies of statistical methods. This include learning how hypotheses can be formulated and tested using several statistical tests such as chi-square test, paired T-test, the analysis of variance test (ANOVA), linear and logistic regression, and Wilcoxon rank-sum test; how research questions can be generated and adequately answered to form a research finding. Topics also include data assessment methods, basic data visualization techniques, and probability concepts. Common issues related to data analysis such Type I and Type II errors, dirty data, determination of statistical significance of results, data sampling issues are also covered.
IS631 Data Visualization
Properly visualization relevant aspects and findings is a crucial phase in any Data Science project. In this course, principles of data visualization, techniques and methods needed to provide clear illustrations of data, and data visualization guidelines are covered. Specific techniques to display certain types of data such as text or time series data are also covered. Students get practical experience on how to employ and evaluate data visualization software tools and programming libraries, and learn the skills needed to convert raw datasets into meaningful, interactive, dynamic, and insightful graphical dashboards.
IS641 Capstone Research Project
In this course, students work on a Data Science project under the supervision of a faculty member. The primary objective of this course is to allow students to apply the knowledge learned in the program to tackle a real-world problem in a full Data Science project.
IS651 Advanced Topics in Information Retrieval
Learn Information Retrieval (IR) theories and systems, and selected topics in IR such as search engines design and architecture, evaluation criteria for information retrieval systems, language models, relevance feedback, and the processing, indexing, querying, management, sorting of bibliographic collections, and textual documents including hypertext documents available on the internet.
CS652 Artificial Intelligence and Deep Learning
The course covers the foundations, theories, approaches, and applications of Artificial Intelligence (AI) and focuses on deep learning, a branch of AI concerned with the creation and deployment of advanced neural networks. Deep learning algorithms, tools, methods, and techniques are studied and applied.
IS653 Data Science Ethics
This course explores ethical issues related to the management of data in organizations and considers the necessity for legal, security and privacy protection properties when dealing with data.
CS654 Big Data Analytics
Due to recent computational advances, datasets of large volume now exist. Examples of such sources of datasets are social media entries, Internet of Things devices and sensors, and online videos. Data Science projects often need to be applied on such large datasets to produce knowledge and insights. To manage the size, rapidity, and diversity of data, it is required to depend on several computational methods that emphasis on scaling-out data. This course covers how to process, analyze and manage large datasets in a manner that empowers real-time decision making and logical discovery at large scale. Vital concepts related to big data such the Hadoop ecosystem, distributed file systems, and parallel and distributed computing are covered.
IS655 Web and Cloud Computing
The exponential evolution of data magnitude in academia, enterprises and social media has prompted the broader use of cloud computing services. This course offers a graduate-level wide-ranging outline to cloud computing with a prominence on cutting-edge data science topics. In this course, students test the most significant APIs provided and used by the major public cloud providers. Students learn to handle significant topics like load assessment, caching, instruction level parallelism, vector instructions, parallel computing and disseminated transactions. The academic knowledge, applied sessions and projects aim to construct their abilities to develop enterprise applications by means of cloud platforms and tools.
IS656 Data Warehousing
Data warehouses are information systems used to leverage data available in organizations. This course covers the foundations and principles of these systems and provides details on how to design, integrate, and operationalize data warehouses to support organizations.
IS657 Business Intelligence
This course explores Business Intelligence (BI) as a wide-ranging category of concepts, tools and technologies for aggregating, evaluating, leveraging, and sharing of data to benefit enterprise workers make informed decisions that are based on accurate and actionable intelligence. The course also covers strategies and methods that empower data-driven decision making, encourage data utilization for competitive advantage, and accept analytics as an ongoing process that contributes to the success of organizations.
IS658 Project Management
This course empowers students to effectively and productively accomplish and manage a data science project. It is methodologically focused and describes cross-industry processes and best-practices for successful management of projects.
IS659 Time Series Analysis and Forecasting
Time Series Analysis has widespread applicability in economic and financial fields. This course enables students to learn how to perceive patterns in time series data, model this data, and make predictions based on those models.
IS661 Text Mining
This course provides an overview of the methods and applications of text mining and highlights the unique challenges of mining unstructured text data. Topics covered include text pre-processing and cleaning, vector space representations, part-of-speech tagging, document classification and clustering, sentiment analysis, text summarization and topic models.
IS662 Sports Analytics
Sports analytics focuses on how data can be used to improve the performances of athletes and sports teams. This class explores the foundations of sports analytics and demonstrates the effectiveness of analytics in the improvement of training and performance of athletes.
IS663 Real-Time Analytics
This course covers architectures and technologies at the basis of real-time analytics. These technologies enable scalable administration and real-time handling of gigantic and continuous extents of data from sources such as sensors and social media streams.
IS664 FinTech Analytics
This course emphasizes on the opportunities and methods that relies on data to improve systems and applications in the financial sector. Learn how to discover insights, and develop data-driven solutions specific for Financial Information Systems.
IS665 Health Informatics
In healthcare, enormous volumes of diverse health data have become accessible in numerous healthcare establishments (providers, financiers, suppliers, pharmaceuticals). Health informatics is the study of how computational methods can be used to improve solutions and outcomes in the healthcare industry. This course covers recent development, analytical approaches, and potential opportunities in the healthcare industry.
IS666 Data Science for Startups
This course explains successful strategies and practices for founding and running startups, and then discusses the opportunities for using Data Science as the foundation for new ventures. The course enables students to plan and start new projects that have the potential of being extended as funded startups.
IS667 Advanced Decision Support Systems
This course delivers an outline on Decision Support Systems (DSS). Topics include: the policy principles behind DSS, scientific fundamentals of DSS, and applications of DSS. Students learn how to classify and chose applicable DSS that is suitable for the development of state-of-the-art enterprise solutions useful for the enhancement of data-driven enterprise decision making.
IS668 Data Governance
Data governance is the study of rules, standards, methods, people, and technology vital to the maintenance of high-quality data in organizations. This course focuses on how a discerning data governance can benefit the regular superiority, accessibility, reliability, and usability of organizational data and how to secure, maintain, and manage data in way that ensure integrity and trust in data.