APPLIED ANALYTICS IN ORGANIZATIONAL CONT
APP AN IN ORG CONTEXT
- R 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.
- F 12:10 p.m.-2 p.m.
- F 10:10 a.m.-noon
- M 8:10 p.m.-10 p.m.
- W 6:10 p.m.-8 p.m.
- T 6:10 p.m.-8 p.m.
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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:
Lisa Godek
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Instructor:
Jennifer Tsao
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Instructor:
David Kreutter
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Instructor:
Daniel Omura
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.
- F 10:10 a.m.-noon
- W 6:10 p.m.-8 p.m.
- T 6:10 p.m.-8 p.m.
- F 10:10 a.m.-noon
- T 8:10 p.m.-10 p.m.
- M 6:10 p.m.-8 p.m.
- M 8:10 p.m.-10 p.m.
- R 6:10 p.m.-8 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:
Lei Yu
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Instructor:
Daniel Fernandez
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Instructor:
Adrian Heilbut
h-index: 13 Info -
Instructor:
Evgeniya Reshetnyak
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Instructor:
Vinay P Mahadeo
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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 6:10 p.m.-8 p.m.
- R 8:10 p.m.-10 p.m.
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Instructor:
Joseph Egbulefu
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Instructor:
Zachary Kurtz
Info - 3 points
APPLIED ANALYTICS FRAMEWORKS & METHDS II
APP AN FRAMEWORKS & METHODS II
- M 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
- T 6:10 p.m.-8 p.m.
- M 6:10 p.m.-8 p.m.
- W 6:10 p.m.-8 p.m.
- F 12:10 p.m.-2 p.m.
- F 2:10 p.m.-4 p.m.
- F 10:10 a.m.-noon
- R 6:10 p.m.-8 p.m.
- W 8:10 p.m.-10 p.m.
- R 8:10 p.m.-10 p.m.
- W 6:10 p.m.-8 p.m.
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Instructor:
Laura A Sywulak
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Instructor:
Bisakha Peskin
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Instructor:
Scott A Spencer
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Instructor:
Robert E Harlow
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Instructor:
David Shilane
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Instructor:
Robert E Harlow
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Instructor:
Sheela Kolluri
CULPA: 18 Info -
Instructor:
Nicole C Dicrecchio
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Instructor:
Sandy Balkin
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Instructor:
Laura A Sywulak
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:
Siddhartha Dalal
Info - 3 points
MANAGING DATA
MANAGING DATA ONLINE
- M 6:10 p.m.-8 p.m.
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Instructor:
Mohamed Mneimneh
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:
Yufeng Huang
Info - 3 points
APPLIED TEXT AND NATURAL LANGUAGE ANALYT
APPLD TXT & NAT LANG AN ONLINE
- T 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.
- T 8:10 p.m.-10 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:
Javid B Huseynov
Info - 3 points
Healthcare Analytics
Data and analytics have always been central to understanding diseases, delivering healthcare and improving patient outcomes. As far back as the 1800’s, Florence Nightingale used data and analytics to reduce the number of deaths of British soldiers in the Crimean War by two-thirds, and John Snow used data and analytics to contain the outbreak of cholera in London. Both used data visualization to communicate their findings and drive change. Today, we have a much deeper understanding of diseases and many more treatment options available. However, the adoption of advanced analytics in healthcare has not kept pace with adoption in other industry sectors, such as financial services and retail. One thing that has not changed since the days of Florence Nightingale and John Snow is the importance of clearly communicating data-driven insights for maximum impact. These lessons are evident in our current challenges with COVID, especially relating to testing, vaccination and individual behaviors. The barriers in the contemporary healthcare environment are high because the outcomes are critical, there are multiple stakeholders, and the system is siloed with discrete, and sometimes competing, needs and expectations. Healthcare is inherently human-centered. Therapeutic interventions cannot improve lives unless healthcare providers and patients adopt them. As with all applications of analytics, providing insights that are understandable and actionable is critical.
In Healthcare Analytics, students will gain a strategic understanding of the healthcare industry, knowledge of how different stakeholder groups use data and analytics to inform scientific, clinical and operational decisions, and how state of the art analytics are transforming every aspect of healthcare from how drugs are discovered and developed to how population and individual health outcomes are optimized. Students will learn how to communicate healthcare data and analyses to drive the adoption of insights by healthcare providers and patients.
Healthcare Analytics is an elective that is intended for students who are interested in learning about healthcare and analytics and students who are interested in pursuing a career using analytics in the healthcare industry sector or in healthcare consulting. This full semester course will be offered online, and is open to APAN students who have successfully completed Applied Analytics in an Organizational Context (APAN PS5100), Storytelling with Data (APAN PS5800), Strategy and Analytics (APAN
- M 6:10 p.m.-8 p.m.
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Instructor:
David Kreutter
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
STRATEGY AND ANALYTICS
STRATEGY AND ANALYTICS ONLINE
- W 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
- M 6:10 p.m.-8 p.m.
- F 2:10 p.m.-4 p.m.
- T 6:10 p.m.-8 p.m.
- F 10:10 a.m.-noon
- W 6:10 p.m.-8 p.m.
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Instructor:
Paul J Bailo
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Instructor:
Ashoke S Bhattacharjya
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Instructor:
Joseph R Schlosser
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Instructor:
Aaron Foster
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Instructor:
Joseph R Schlosser
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Instructor:
Orin Davis
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
- F 10:10 a.m.-noon
- T 6:10 p.m.-8 p.m.
- S 10:10 a.m.-noon
- M 6:10 p.m.-8 p.m.
- T 8:10 p.m.-10 p.m.
- R 8:10 a.m.-10 a.m.
- W 6:10 p.m.-8 p.m.
- R 6:10 p.m.-8 p.m.
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Instructor:
Javid B Huseynov
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Instructor:
Balmatee Bidassie
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Instructor:
Benjamin Kinsella
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Instructor:
Christian Klose
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Instructor:
Colleen Davy
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Instructor:
Paula Darling
Info - 3 points
STORYTELLING WITH DATA
STORYTELLING WITH DATA ONLINE
- W 8:10 p.m.-10 p.m.
- M 8:10 p.m.-10 p.m.
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Instructor:
Bum Chul Kwon
h-index: None Info -
Instructor:
Michael Mangarella
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
- F 2:10 p.m.-4 p.m.
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Instructor:
Paul Maniaci
Info - 3 points