APPLIED ANALYTICS IN ORGANIZATIONAL CONT
APP AN IN ORG CONTEXT
- M 8:10 p.m.-10 p.m.
- W 6:10 p.m.-8 p.m.
- T 8:10 a.m.-10 a.m.
- S 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 8:10 p.m.-10 p.m.
- W 6:10 p.m.-8 p.m.
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Instructor:
Jennifer Tsao
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Instructor:
Lori Cenci
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Instructor:
Robert Simione
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Instructor:
Anjana Bhattacharyya
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Instructor:
Huan Liu
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Instructor:
Panagiotis Tarhanidis
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Instructor:
Mani Vannan
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Instructor:
Jisang Pack
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Instructor:
David Kreutter
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
- T 8:10 a.m.-10 a.m.
- W 8:10 a.m.-10 a.m.
- W 6:10 p.m.-8 p.m.
- F 10:10 a.m.-noon
- M 6:10 p.m.-8 p.m.
- M 8:10 p.m.-10 p.m.
- T 8:10 p.m.-10 p.m.
- R 8:10 p.m.-10 p.m.
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Instructor:
Kitty K Chan
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Instructor:
Vishal Lala
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Instructor:
Birol Emir
CULPA: 14 Info -
Instructor:
Daniel Fernandez
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Instructor:
Evgeniya Reshetnyak
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Instructor:
Vinay P Mahadeo
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Instructor:
Lei Yu
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Instructor:
Andrew Assing
Info - 3 points
APPLIED ANALYTICS FRAMEWORKS & METHODS I
APAN FRMWRKS & MTHDS I ONLINE
APPLIED ANALYTICS FRAMEWORKS & METHODS I
- R 8:10 p.m.-10 p.m.
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Instructor:
Zachary Kurtz
Info - 3 points
APPLIED ANALYTICS FRAMEWORKS & METHDS II
APAN FRMWRKS & MTHDS II ONLINE
- R 6:10 p.m.-8 p.m.
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Instructor:
David Shilane
Info - 3 points
PYTHON FOR DATA ANALYSIS
- W 6:10 p.m.-8 p.m.
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Instructor:
Guilherme de Oliveira
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
RESEARCH DESIGN
- F 2:10 p.m.-4 p.m.
- T 8:10 p.m.-10 p.m.
- R 6:10 p.m.-8 p.m.
- W 8:10 p.m.-10 p.m.
- F 12:10 p.m.-2 p.m.
- M 6:10 p.m.-8 p.m.
- T 6:10 p.m.-8 p.m.
- M 8:10 p.m.-10 p.m.
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Instructor:
David Shilane
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Instructor:
Laura A Sywulak
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Instructor:
Jalisha Jennifer
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Instructor:
Scott A Spencer
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Instructor:
Robert E Harlow
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Instructor:
Nicole C Dicrecchio
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Instructor:
Sandy Balkin
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Instructor:
Scott A Spencer
Info - 3 points
RESEARCH DESIGN
RESEARCH DESIGN ONLINE
- M 8:10 p.m.-10 p.m.
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Instructor:
Andy W Chen
Info - 3 points
SQL
SQL & RELATIONAL DB ONLINE
- M 8:10 p.m.-10 p.m.
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Instructor:
Nikolaos Machairas
Info - 3 points
MACHINE LEARNING: CONCEPTS & APPLICATION
- T 8:10 a.m.-10 a.m.
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Instructor:
Adrian Heilbut
h-index: 13 Info - 3 points
MANAGING DATA
MANAGING DATA ONLINE
- W 8:10 p.m.-10 p.m.
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Instructor:
Mohamed Mneimneh
Info - 3 points
ANOMALY DETECTION
- M 6:10 p.m.-8 p.m.
- T 8:10 p.m.-10 p.m.
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Instructor:
Christopher Kuo
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Instructor:
Andrew Assing
Info - 3 points
APPLIED TEXT AND NATURAL LANGUAGE ANALYT
APPLIED TXT & NAT LANG AN
- M 8:10 p.m.-10 p.m.
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Instructor:
John Ortega
h-index: None Info - 3 points
FINANCIAL DATA SCIENCE AND MACHINE LEARN
FIN DATA SCI & MACHINE LE
This course teaches cutting-edge tools and methods that drive investment decisions at quantitative trading firms, and, more generally, firms applying machine learning to big data. The course will combine presentations of theory, immediately followed by in-class Python programming examples using real financial data. The course will develop a general approach to building models of economic and financial processes, with a focus on statistical learning techniques that scale to large data sets. Among the topics covered are lasso, elastic net, cross validation, Bayesian models, the EM algorithm, Support Vector Machines, kernel methods, Gaussian processes, Hidden Markov Models, and neural networks. The final project will lead the students to build a trading strategy based on the techniques learned throughout the course.
- F 2:10 p.m.-4 p.m.
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Instructor:
William G Ritter
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.
- W 8:10 p.m.-10 p.m.
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Instructor:
Mohsen Golalikhani
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.
- W 6:10 p.m.-8 p.m.
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Instructor:
Roya Heydari
Info - 3 points
STRATEGY AND ANALYTICS
STRATEGY AND ANALYTICS ONLINE
n/a
- T 8:10 p.m.-10 p.m.
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Instructor:
Diahanna L Post
Info - 3 points
ANALYTICS & LEADING CHANGE
ANALYTICS & LEADING CHNG
- S 10:10 a.m.-noon
- F 2:10 p.m.-4 p.m.
- F 12:10 p.m.-2 p.m.
- T 6:10 p.m.-8 p.m.
- R 8:10 a.m.-10 a.m.
- R 12:10 p.m.-2 p.m.
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Instructor:
Paul J Bailo
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Instructor:
Peter Went
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Instructor:
Ashoke S Bhattacharjya
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Instructor:
Aaron Foster
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Instructor:
Joseph R Schlosser
Info - 3 points
ANALYTICS & LEADING CHANGE
ANALYTICS & LEADNG CHNG ONLINE
- M 6:10 p.m.-8 p.m.
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Instructor:
David Kreutter
Info - 3 points
STORYTELLING WITH DATA AND AI
Data does not have meaning without context and interpretation. Being able to effectively present data analytics in a compelling narrative to a particular audience will differentiate you from others in your field. This course takes students through the lifecycle of an analytical project from a communication perspective. Students develop written, verbal, and visual deliverables for three major audiences: data experts (e.g., head of analytics); consumer and presentation experts (e.g., chief marketing officer); and executive leadership (e.g., chief executive officer).
Students get ample practice in strategic interactions in relevant social and professional contexts (e.g., business meetings, team projects, and one-on-one interactions); active listening; strategic storytelling; and creating persuasive professional spoken and written messages, reports, and presentations. Throughout the course, students create and receive feedback on data storytelling while sharpening their ability to communicate complex analytics to technical and nontechnical audiences with clarity, precision, and influence.
- R 8:10 p.m.-10 p.m.
- S 10:10 a.m.-noon
- M 6:10 p.m.-8 p.m.
- T 6:10 p.m.-8 p.m.
- W 8:10 a.m.-10 a.m.
- R 8:10 p.m.-10 p.m.
- T 8:10 p.m.-10 p.m.
- F 2:10 p.m.-4 p.m.
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Instructor:
Javid B Huseynov
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Instructor:
Benjamin Kinsella
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Instructor:
Christian Klose
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Instructor:
Paula Darling
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Instructor:
Samar Deen
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Instructor:
Juan Melo
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Instructor:
Benjamin Kinsella
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Instructor:
Gert de Geyter
Info - 3 points
STORYTELLING WITH DATA AND AI
STORYTELLING WITH DATA ONLINE
Data does not have meaning without context and interpretation. Being able to effectively present data analytics in a compelling narrative to a particular audience will differentiate you from others in your field. This course takes students through the lifecycle of an analytical project from a communication perspective. Students develop written, verbal, and visual deliverables for three major audiences: data experts (e.g., head of analytics); consumer and presentation experts (e.g., chief marketing officer); and executive leadership (e.g., chief executive officer).
Students get ample practice in strategic interactions in relevant social and professional contexts (e.g., business meetings, team projects, and one-on-one interactions); active listening; strategic storytelling; and creating persuasive professional spoken and written messages, reports, and presentations. Throughout the course, students create and receive feedback on data storytelling while sharpening their ability to communicate complex analytics to technical and nontechnical audiences with clarity, precision, and influence.
- W 8:10 p.m.-10 p.m.
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Instructor:
Bum Chul Kwon
h-index: None 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:
Gonzalo Miranda Madrigal
Info - 3 points