APPLIED ANALYTICS IN ORGANIZATIONAL CONT
APP AN IN ORG CONTEXT
- M 8:10 p.m.-10 p.m.
- T 8:10 p.m.-10 p.m.
- S 10:10 a.m.-noon
- R 8:10 p.m.-10 p.m.
- M 6:10 p.m.-8 p.m.
- R 6:10 p.m.-8 p.m.
- F 10:10 a.m.-noon
- R 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:
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:
Joseph R Schlosser
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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 6:10 p.m.-8 p.m.
- R 6:10 p.m.-8 p.m.
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Instructor:
Kitty K Chan
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Instructor:
Birol Emir
CULPA: 14 Info -
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
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
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 ONLINE
- W 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
- S 12:10 p.m.-2 p.m.
- F 10:10 a.m.-noon
- T 6:10 p.m.-8 p.m.
- R 6:10 p.m.-8 p.m.
- W 8:10 a.m.-10 a.m.
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Instructor:
Darshan Desai
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Instructor:
Amreeta Choudhury
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Instructor:
Mohsen Golalikhani
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Instructor:
Arash Khatibi
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Instructor:
Samar Deen
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 6:10 p.m.-8 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.
- M 8:10 p.m.-10 p.m.
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Instructor:
Mohamed Mneimneh
Info - 3 points
MATHEMATICS FOR AI
This course equips students with essential mathematical foundations for understanding and working with artificial intelligence (AI) algorithms. After a brief introduction to the historical and social context that numbers arise in, students will learn about:
- Linear Algebra: Matrices, matrix-vector multiplication, linear models, change of basis, dimensionality, spectral decomposition, and principal component analysis (PCA).
- Calculus: Rates of change, derivatives, optimization techniques like gradient descent, with a brief touch upon linear approximation.
- Probability and Statistics: Mathematically deriving complex probability distributions out of simpler ones, mathematically deriving statistical testing methods
- Graph Theory: How graphs are used to find relationships between data as well as being a setting for AI-driven problem solving.
- Problem Solving and Algorithms: Applying mathematical concepts to find problem solutions.
Students will learn about search methods like uninformed search, informed search with the A* algorithm, and greedy algorithms.
- Computational Theory and Automata: Answering questions about what is computable, what is needed in order to compute something, and using this framework to state how much “information” is contained in a mathematical object.
By the end of this course, students will possess a strong mathematical toolkit to confidently tackle the complexities of modern AI algorithms.
- M 6:10 p.m.-8 p.m.
- 3 points
ANALYTICS FOR BUSINESS OPERATIONS MANAGE
Operations Management (OM) is responsible for the efficient production and delivery of goods and services, serving as a cornerstone of successful organizations. This course emphasizes how analytical techniques, such as forecasting, queuing theory, and linear programming, provide critical tools for optimizing operational decision-making, improving efficiency, and addressing real-world challenges in operations management. In this course, you will gain essential skills to optimize processes, manage resources, and enhance productivity across various industries. The course will be delivered through a combination of interactive lectures, case studies, and hands-on coding exercises to ensure a balance between conceptual learning and practical application.
Through lectures, you will gain a solid foundation in OM principles and analytical techniques. Case studies will help illustrate real-world applications of OM in industries such as manufacturing, healthcare, retail, and logistics, allowing you to see how the concepts are applied in diverse contexts. This course will integrate the principles of OM with hands-on analytical techniques using Python, allowing you to model and solve real-world OM problems. You will learn to run simulations, perform optimizations, and analyze data to make data-driven decisions that enhance efficiency and overall performance.
OM practices are tailored to meet the specific needs of various sectors. In manufacturing, OM helps streamline production lines and minimize waste; in healthcare, it enhances patient flow and optimizes resource allocation; in retail, it improves inventory management and supply chain operations; and in logistics, it ensures timely deliveries while reducing transportation costs. This course will equip you with the skills to apply OM practices effectively in different industries.
Analytics for Business Operations Management is an elective that is intended for students who are interested in pursuing a career using analytics and operational insights to drive organizational success in a competitive global marketplace across various industries.
- M 8:10 p.m.-10 p.m.
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Instructor:
Andy W Chen
Info - 3 points
APPLIED GENERATIVE AI
This course offers a comprehensive introduction to a branch of machine learning called generative modeling, focusing on the underlying concepts, theoretical techniques, and practical applications. The defining property of Generative AI models is their ability to generate new data similar to a given dataset. In recent years, Generative AI has seen rapid advancement, revolutionizing various industries by enabling machines to create realistic and novel content, ranging from images, videos, and music to text and complex simulations.
Students will learn to use, fine-tune, and programmatically interface with high-level APIs and open-source foundational models, allowing them to leverage state-of-the-art tools in Generative AI. Additionally, the course delves into the theory and practice of low-level implementations, empowering students to train their own models on their own data and understand these models from first principles. The course covers various types of generative models, including Variational Autoencoders (VAEs), Generative Adversarial Networks (GANs), and Transformers with their applications to text, image, audio, and video generation.
By combining these approaches, this course provides a robust foundation in both the practical application and deep theoretical knowledge required to develop innovative AI solutions.
- W 8:10 p.m.-10 p.m.
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Instructor:
Gurgen Hayrapetyan
Info - 3 points
STRATEGY AND ANALYTICS
STRATEGY AND ANALYTICS ONLINE
n/a
- M 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
- T 6:10 p.m.-8 p.m.
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Instructor:
Paul J Bailo
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Instructor:
Ashoke S Bhattacharjya
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.
- T 8:10 p.m.-10 p.m.
- W 8:10 p.m.-10 p.m.
- F 2:10 p.m.-4 p.m.
- W 8:10 p.m.-10 p.m.
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Instructor:
Benjamin Kinsella
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
APPLIED DEEP LEARNING AND AI
Deep Learning has become a cornerstone of Artificial Intelligence (AI), with applications in finance, healthcare, sports, autonomous vehicles, chatbots, national security, and artistic creations using elements of Natural Language Processing, Computer Vision, and Speech Recognition. Students will gain a solid foundation in Deep learning and its applications, starting with a compressed review of some Statistical Learning models followed by much deeper dive into Deep Neural Networks. Topics covered include Neural Networks, Convolutional Neural Networks (CNN), word embeddings, attention mechanisms, transformers, encoder-decoder architectures and Generative Adversarial Networks (GAN). Students will also learn training of agents to make optimal decisions in complex environments using Reinforcement Learning. Practical applications will demonstrate how to prepare, train, test, and validate these models.
- T 6:10 p.m.-8 p.m.
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Instructor:
Siddhartha Dalal
Info - 3 points