APPLIED ANALYTICS IN ORGANIZATIONAL CONT
APP AN IN ORG CONTEXT
- T 6:10 p.m.-8 p.m.
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Instructor:
Anjana Bhattacharyya
Info - 3 points
DATA MODELING
DATA MODELING ONLINE
- M 6:10 p.m.-8 p.m.
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Instructor:
Steven Hoberman
Info - 3 points
APPLIED ANALYTICS FRAMEWORKS & METHODS I
APP AN FRAMEWORKS & METHODS I
APPLIED ANALYTICS FRAMEWORKS & METHODS I
- R 8:10 p.m.-10 p.m.
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Instructor:
David Shilane
Info - 3 points
APPLIED ANALYTICS FRAMEWORKS & METHDS II
APP AN FRAMEWORKS & METHODS II
- R 8:10 p.m.-10 p.m.
- T 6:10 p.m.-8 p.m.
- T 8:10 p.m.-10 p.m.
- W 6:10 p.m.-8 p.m.
- R 6:10 p.m.-8 p.m.
- F 10:10 a.m.-noon
- W 8:10 p.m.-10 p.m.
- M 8:10 p.m.-10 p.m.
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Instructor:
Joseph Egbulefu
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Instructor:
Vishal Lala
Info - 3 points
APPLIED ANALYTICS FRAMEWORKS & METHDS II
APAN FRMWRKS & MTHDS II ONLINE
- M 6:10 p.m.-8 p.m.
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Instructor:
Gurgen Hayrapetyan
Info - 3 points
PYTHON FOR DATA ANALYSIS
- W 6:10 p.m.-8 p.m.
- S 10:10 a.m.-noon
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Instructor:
Guilherme de Oliveira
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Instructor:
Helga Hartelt Baez
Info - 3 points
PYTHON FOR DATA ANALYSIS
PYTHON FOR DATA ANALYSIS ONLNE
- T 8:10 p.m.-10 p.m.
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Instructor:
Max Vilenchik
Info - 3 points
SQL
SQL & RELATIONAL DB
- W 6:10 p.m.-8 p.m.
- T 8:10 p.m.-10 p.m.
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Instructor:
Michael J Coakley
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Instructor:
Day Yi
Info - 3 points
SQL
SQL & RELATIONAL DB ONLINE
- R 8:10 p.m.-10 p.m.
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Instructor:
Nikolaos Machairas
Info - 3 points
MANAGING DATA
- R 6:10 p.m.-8 p.m.
- W 8:10 p.m.-10 p.m.
- M 6:10 p.m.-8 p.m.
- M 8:10 p.m.-10 p.m.
- T 8:10 p.m.-10 p.m.
- S 12:10 p.m.-2 p.m.
- F 12:10 p.m.-2 p.m.
- W 8:10 a.m.-10 a.m.
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Instructor:
Arash Khatibi
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Instructor:
Javid B Huseynov
Info -
Instructor:
Nitinchandra Nayak
Info - 3 points
MANAGING DATA
MANAGING DATA ONLINE
- R 6:10 p.m.-8 p.m.
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Instructor:
Robert Simione
Info - 3 points
APPLIED TEXT AND NATURAL LANGUAGE ANALYT
APPLIED TXT & NAT LANG AN
- W 8:10 p.m.-10 p.m.
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Instructor:
John Ortega
h-index: None Info - 3 points
CLOUD COMPUTING
Data analytics have become an essential component of business intelligence and informed decision making. Sophisticated statistical and algorithmic methodologies, generally known as data science, are now of predominant interest and focus. Yet, the underlying cloud computing platform is fundamental to the enablement of data management and analytics.
This course introduces students to cloud computing concepts and practices ranging from infrastructure and administration to services and applications. The course is primarily focused on the development of practical skills in utilizing cloud services to build distributed and scalable analytics applications. Students will have hands-on exposure to VMs (Virtual Machines), databases, storage, microservices, and AI/ML (Artificial Intelligence and Machine Learning) services through Google Cloud Platform, et al. Cost and performance characteristics of alternative approaches will also be studied. Topics include: overview of cloud computing, cloud systems, parallel processing in the cloud, distributed storage systems, virtualization, security in the cloud, and multicore operating systems. Throughout, students will study state-of-the-art solutions for cloud computing developed by Google, Amazon, Microsoft, and IBM.
The course modules provide a blend of lecture and reading materials along with class exercises and programming assignments. While extensive programming experience is not required, students taking the course are expected to possess basic Python 3 programming skills.
The desired outcome of the course is the student’s ability to put conceptual knowledge to practical use. Whether you are taking this course for future academic research, for work in industry, or for an innovative startup idea, this course should help you master the fundamentals of cloud computing.
- T 6:10 p.m.-8 p.m.
- M 6:10 p.m.-8 p.m.
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Instructor:
Mohsen Golalikhani
Info -
Instructor:
Mohamed Mneimneh
Info - 3 points
INTRO TO BLOCKCHAIN, CRYPTO$ & ANALYTICS
BLOCKCHAINCRYPTOCURRENCY &ANL
TBD
- T 6:10 p.m.-8 p.m.
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Instructor:
Siddhartha Dalal
Info - 3 points
MARKET RESEARCH
Market research is the way that companies identify, understand and develop the target market for their products. It is an important component of business strategy, and it draws on the research and analytics skills you have learned thus far in the program. Often market research consists of generating your own data, through quantitative and qualitative methodologies, in pursuit of the market research question.
This course is an elective that will expand on quantitative and qualitative methodologies that have been introduced previously, provide an introduction to other methodologies that are more specific to market research, and provide hands-on practice in defining a market research plan from start to finish. Students will also learn about particular types of market research studies and when and how they should be deployed. Students will generate and test their own research instruments. Through the use of case studies and simulations, students will learn how market research fits into an overarching marketing plan for a company.
This course is designed for students who have completed the Research Design and Strategy and Analytics core courses, and who are exploring how research fits into product marketing. You will leave this class understanding the essential aspects of market research, when and how they should be deployed, and the role you could play in small and large companies directing and executing on market research opportunities.
- M 8:10 p.m.-10 p.m.
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Instructor:
Diahanna L Post
Info - 3 points
AI USER INTERFACES FOR ANALYTICS
AI UI FOR ANALYTICS
In recent years, data analytics and artificial intelligence (AI) have become essential to business intelligence and informed decision making. But to realize the impact of analytics and AI, effective visual communication of data insights via user interfaces (UI), such as web pages and app dashboards, is equally critical. Building effective UIs requires mastering the user experience (UX) design principles and certain front-end development technologies. Furthermore, the recent rise of multimodal Generative AI offers unprecedented opportunities for simplifying, automating, and scaling UX/UI development.
This course provides a comprehensive understanding of UX design principles and best practices for developing UIs while emphasizing ethical considerations and inclusivity. Students will learn to create intuitive and visually engaging websites and dashboards that leverage AI-generated insights, also considering data privacy, diversity, and accessibility. Key topics include the design, implementation, and evaluation of UIs, with hands-on experience in web development technologies like HTML, CSS, and JavaScript, as well as related cloud services. Students will apply state-of-the-art AI technologies to create intelligent and interactive UIs, all while critically assessing data sources and AI models for potential biases.
The course content comprises a blend of conceptual learning and practice assignments. Weekly lectures and reading materials will cover the fundamentals of data visualization and user experience designs. Students will put the gained knowledge into practice through individual design and coding assignments and a group term project.
- W 8:10 p.m.-10 p.m.
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Instructor:
Javid B Huseynov
Info - 3 points
STRATEGY AND ANALYTICS
- S 10:10 a.m.-noon
- T 8:10 p.m.-10 p.m.
- F 2:10 p.m.-4 p.m.
- F 10:10 a.m.-noon
- R 8:10 p.m.-10 p.m.
- M 6:10 p.m.-8 p.m.
- T 6:10 p.m.-8 p.m.
- W 6:10 p.m.-8 p.m.
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Instructor:
Arunava Bhattacharyya
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Instructor:
Ranjit Kumble
Info -
Instructor:
Peter Went
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Instructor:
Lisa Godek
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Instructor:
Jisang Pack
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Instructor:
Panagiotis Tarhanidis
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Instructor:
Sandy Balkin
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Instructor:
Ashoke S Bhattacharjya
Info - 3 points
STRATEGY AND ANALYTICS
STRATEGY AND ANALYTICS ONLINE
- W 6:10 p.m.-8 p.m.
- T 6:10 p.m.-8 p.m.
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Instructor:
David Kreutter
Info - 3 points
ANALYTICS & LEADING CHANGE
ANALYTICS & LEADNG CHNG ONLINE
- M 8:10 p.m.-10 p.m.
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Instructor:
Zachary Kornhauser
Info - 3 points
STORYTELLING WITH DATA
STORYTELLING WITH DATA ONLINE
- W 8:10 p.m.-10 p.m.
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Instructor:
Bum Chul Kwon
h-index: None Info - 3 points
SPECIAL TOPICS: APPLIED DEEP LEARNING AN
TOPIC: APPLD DEEP LEARN A
Deep Learning has become a cornerstone of Artificial Intelligence (AI), with applications in finance, healthcare, sports, autonomous vehicles, chatbots, national security, and more. It is revolutionizing fields like Natural Language Processing, Computer Vision, and Speech Recognition. This advanced course delves into deep learning, blending key elements from Statistical Machine Learning. Students will gain a solid foundation in supervised learning and other related algorithms and methods. Topics covered include Support Vector Machines, Neural Networks, Convolutional Neural Networks (CNN), word embeddings, attention mechanisms, transformers, encoder-decoder architectures, Generative Adversial Networks (GAN), and Reinforcement Learning. Practical applications will demonstrate how to prepare, train, test, and validate models.
- T 8:10 a.m.-10 a.m.
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Instructor:
Siddhartha Dalal
Info - 3 points
Internship In APAN
This course requires you to experience firsthand a program-related job in a real working environment. You will engage in personal, environmental and organizational reflection. The ideal Internship will provide you an opportunity to gain tangible and practical knowledge in your chosen field by taking on a position that is closely aligned with your coursework and professional interests. Before registering for this course, you must have completed the Internship Application Form in which you will describe your internship sponsor and provide details about the work that you will be doing. This form must be signed by your internship supervisor and approved by your program director BEFORE you register for this course.
To receive instructor approval, the internship:
● Must provide an opportunity for the student to apply course concepts, either at the organizational or team level
● Must fit into the planned future program-related career path of the student
You must identify your own internship opportunities. The internship must involve a commitment to completing a minimum of 210 hours over the semester.
At the end of your course, you will submit an evaluation form to your internship supervisor. The evaluation form should be returned directly to the instructor
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Instructor:
Kat Castro
Info - 3 points