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Data Analytics Courses
DAN601 Decision Making with Data
[3–0, 3 cr.]
This course delves into the integral role of data analytics in decision-making processes across diverse sectors. Students will develop a foundational understanding of the significance of data, its evolution within the digital age, and the transformative impact of “Big Data” on modern analytics practices.
Throughout this course, you will:
• Explore the essentials of data analytics, emphasizing its critical role in supporting informed and strategic decision-making across various industries.
• Investigate the historical development and contemporary significance of data analytics, with a focus on the advent and integration of “Big Data” technologies.
• Be introduced to a systematic framework for data analysis, encompassing the predominant tools and techniques that enhance analytical accuracy and efficacy.
• Apply your knowledge through practical, simulated business scenarios, employing analytical skills to navigate and resolve complex challenges.
DAN604 Statistics for Data Analytics
[3–0, 3 cr.]
This course provides a comprehensive understanding of fundamental statistical concepts that are essential for data analysis. The course explores basic statistical concepts including data modeling, random variables and hypothesis testing, clustering, principal component analysis, linear models, logistic regression, and analysis of variance.
DAN611 Applied Machine Learning
[3–0, 3 cr.]
Students in this course will learn about supervised and unsupervised training methods. The focus is on identifying relationships that cannot be found by basic statistics and used for example in customer satisfaction, branding, machine failure, resource allocation, fraud detection, and fraudulent activities. Techniques include Nearest Neighbors, Naive Bayes, deep learning, text mining, clustering, association rules, regularization and dimensionality reduction. The bias/variance trade-off and model selection is a focal point of the course and will be illustrated from multiple angles. Students will acquire hands-on experience on all techniques taught.
DAN612 Data Ethics
[3–0, 3 cr.]
This course introduces key ethical principles for designing intelligent systems, focusing on their impact on the economy, society, and government. Topics include ethical data sourcing, ethical modelling, ethical deployment, bias mitigation, economic equity, and data governance.
DAN613 Data Engineering
[3–0, 3 cr.]
This course introduces students to the fundamentals of data engineering in business analytics. It covers the principles and techniques of data sourcing, extraction, transformation, and loading (ETL) necessary for preparing data for analysis. Students will learn the basics of data pipelines, data quality, and management using structured and semi-structured data. The course also introduces SQL and NoSQL databases and examines the principles behind scalable data preparation. Practical exposure to data engineering tools and simple pipeline design is emphasized.
DAN614 Data Visualization
[3–0, 3 cr.]
This course introduces students to the latest data visualization techniques and tools to visualize data using dashboards, scorecards, and other formats. Students will learn presentation techniques with emphasis placed on the data story, the visual display of data and smart reporting of results. Students will acquire hands-on skills to create effective presentations leveraging latest technology and software such as Tableau, QlikView, or IBM Insights. Other covered topics include web analytics and communication.
DAN615 Cognitive Analytics
[3–0, 3 cr.]
This course offers students an understanding of the “data” or the conscious/non-conscious information processing in our brains explaining the consumer’s decision-making on a non-conscious level. Neuro-metrical and biometrical measures, models and technologies help students understand and predict consumer behavior using non-traditional research methodologies.
Cognitive analytics is about understanding brain science (neuro-science, behavioral economics & social psychology) and exposing as well as measuring the consumer’s hidden data from the mind and body. Applying data mining techniques to Neuro Marketing data (mind-mining) will provide students with a much deeper and richer understanding of consumer preferences, choices and behavior.
DAN617 Information Security User Behavior Analytics
[3–0, 3 cr.]
This course covers key risks to information systems and business data. Students will apply data analytics techniques across different dimensions to provide effective information security analytics. Threats to normal user behavior are compared and contrasted by utilizing the user behavior analytics approach Normal behavior.
DAN618 Healthcare Analytics
[3–0, 3 cr.]
The rise of preventive care, health technology and telemedicine has generated massive amounts of multidimensional health data. The magnitude and complexity of these data are overwhelming for healthcare providers and stakeholders to analyze and extract meaningful knowledge to make informed decisions. Moreover, the COVID 19 pandemic has unveiled profound weaknesses in the healthcare systems of most countries. Global investments in private health systems and private healthcare solutions have witnessed a 6% increase in Q2 2020 and are predicted to increase significantly in the future.
The expected digital transformation will not be possible without data and analytics.
In this course, you will be equipped with the knowledge to work in the healthcare field or with a healthcare client as analyst or consultant. You will be introduced to the pillars of healthcare systems and the main health concepts and measures. You will learn about healthcare data types and sources, how to formulate data queries, how to use geospatial information systems to map health data and how analytics is applied in the healthcare field. Finally, you will dive into the economic evaluation and financial impact of health-related interventions and programs.
DAN619 Big Data Processing & Blockchain Technology
[3–0, 3 cr.]
This course has two pillars. The course first focuses on blockchain technology and its applications in business. It explores how blockchain brings profound changes to businesses and explains how it transforms businesses structures, functions and roles of the organization. The course then dives into the various methods of blockchain governance that exist in the market place and examines specific features of blockchain to overcome problems that have been difficult to solve in the past using the existing centralized architecture. Topics include: key concepts like hashing, public key cryptography, digital signing, mining, proof-of-work, proof of stake, public vs private vs permissioned blockchain, peer-to-peer transactions, blocks, consensus mechanisms, smart contracts, crypto-asset, distributed resources, decentralized protocol, and the double spending problem. These concepts will be illustrated using the Bitcoin application and implemented mainly using Ethereum. The course then tackles how to process large data volumes on large computational clusters by introducing advanced features for Spark 2.0. Students will learn how to set up clusters in both batch and real time modes, retrieve big volumes of textual data, analyze streaming data and use the ML API.
DAN623 NLP-Text Analytics
[3–0, 3 cr.]
This course focuses on the computational aspect of Natural Language Processing (NLP) technologies and aims at finding a balance between traditional and modern NLP techniques. It covers major concepts and techniques for processing, cleaning, visualizing, and analyzing textual data to extract interesting information, discover knowledge, and support decision-making in business applications. Students will learn fundamental pre-processing techniques (i.e., tokenization, stemming, lemmatization, part-of-speech tagging, and named entity recognition), text representation (i.e., vector-space and language models, and modern distributed representation of words), and various text analytics tasks (i.e., text categorization and classification, document summarization, and sentiment analysis). Hands-on labs and projects in parallel to course lectures and readings will allow students to develop practical skills in building foundational NLP tools that can be applied to address real-world business analytics problems.
DAN624 Reinforcement Learning
[3–0, 3 cr.]
This course provides students with a solid foundation in reinforcement learning (RL), a key branch of machine learning that enables systems to learn optimal behaviors through trial-and-error interactions with dynamic environments. The course covers core concepts such as Markov Decision Processes (MDPs), dynamic programming, Monte Carlo methods, temporal-difference learning, Q-learning, and policy gradient methods. Through real-world examples and practical applications, students will learn how to model decision-making problems and implement RL algorithms to solve complex challenges in fields like business optimization, operations, recommendation systems, and resource allocation. Emphasis will be placed on applying RL in data-driven business environments using Python-based libraries.
DAN630 Special Topics in Analytics
[3–0, 3 cr.]
The course material is updated frequently to reflect the changing analytics landscape, making it the perfect choice for students who want to stay on the leading edge of this quickly developing profession.
DAN634 Analytical Data Mining
[3–0, 3 cr.]
This course will provide students with an understanding of fundamental data mining concepts and tools. The topics covered include data sources, data cleaning techniques and tools, common data mining algorithms, statistical modeling, and widely used tools for both structured and unstructured data mining.
DAN635 Data Management for Analytics
[3–0, 3 cr.]
The goal of this course is to give students the fundamental knowledge and abilities needed to comprehend and apply data modification and management. They will get practical knowledge and experience in data management for business intelligence, analytics, and data science endeavors. Professionals working in data and analytics will also be able to comprehend the various newly developed data management solutions.
DAN636 Customer Behavior Analytics
[3–0, 3 cr.]
Customer Behavior Analytics is a dynamic course designed to equip students with the advanced analytical skills necessary to transform customer data into actionable insights, driving strategic decisions in marketing and customer service. This course delves into the core principles of CRM, exploring how cutting-edge data analytics can optimize customer interactions and enhance organizational growth. Students will learn to harness the power of both descriptive and predictive analytics to understand customer behaviors, preferences, and trends.
DAN637 Web and Social Media Analytics
[3–0, 3 cr.]
This course addresses the move towards social media to build intellectual capital, communicate with society, exchange knowledge among a global workforce, and provide the public face of business for marketing and corporate communications. The course explores the role of social media technologies (e.g., Twitter) in shaping societal and business trends, and emphasizes analyzing social media data in terms of reach, engagement, influencers, etc. using Python and open source tools. The course also explores social networks in the important of information propagation in social media.
DAN638 Supply Chain Analytics
[3–0, 3 cr.]
This course presents practical applications of data analytics (descriptive, predictive, and prescriptive) in the manufacturing, trade, and service industries, in a range of supply chain management domains, including forecasting and inventory management, sales and operations planning, transportation, logistics, and fulfillment, purchasing and supply management, supply chain risk management, etc. In order to improve supply chain efficiency and business value, students learn how to identify the correct data set, ask the relevant questions, and utilize the right models and tools to create decisions that are based on facts. Product development analytics, inventory and resource management, spend analytics and supplier selection, transportation analytics, fulfillment diagnostics in logistics systems, sales and operations analytics in production, demand forecasting for new products, and supplier and product line selection are among the topics covered.
DAN642 Analytics Applications
[3–0, 3 cr.]
The applications studied in this course rely heavily on predictive and prescriptive analytics tools. Students will learn how to define business problems requiring prediction and then select the most appropriate forecasting strategy to meet the application. Similarly, students will learn how to frame a decision problem and then select and apply the appropriate data driven decision making strategy.
DAN696 Research Methods in Data Analytics
[3–0, 3 cr.]
This course offers an in-depth exploration of statistical methods critical for data analytics within business contexts. Emphasizing practical application, the course covers essential statistical techniques and their role in making informed business decisions. Students will learn to apply methods such as hypothesis testing, regression analysis (both linear and logistic), ANOVA, clustering, and principal component analysis. The course also addresses the use of statistical software for data analysis, interpretation of analytical results, and common pitfalls in data analysis. Through real-world business cases, students will develop the skills to effectively model and analyze business data, enhancing their ability to contribute strategic insights based on robust statistical reasoning.
DAN697 Capstone Project
[3–0, 3 cr.]
The Capstone Project course is a pivotal component of the Master of Science in Business Data Analytics program, designed to synthesize and integrate knowledge acquired throughout the curriculum. This course challenges students to apply their comprehensive data analytics skills to analyze and address real-world business issues. By engaging in a final project, students will demonstrate their capability to develop corporate and business strategies using advanced business analytics techniques.
DAN698 Research Project in Business Analytics
[3–0, 3 cr.]
This course entails the application of research methods to a current topic relevant to Business Data Analytics. The project must incorporate the student’s hypothesis, test methods, test results, and conclusions.
DAN699 Thesis in Business Analytics
[3–0, 3 cr.]
Students pre-approved for a thesis may enroll in this class. Students will write a thesis on a topic related to business data analytics approved by the Thesis Supervisor. Students will conduct their research and write their thesis under the supervision of a full-time faculty member and assisted by two other faculty members.
