STEVENS ONLINE MASTER’S IN DATA SCIENCE (MSDS)

This course will provide an introduction to exploratory data analysis and the use of basic statistical tools. Topics will include: data collection; descriptive statistics, and graphical and tabular treatment of quantitative, qualitative, and count data; detecting relations between variables; confidence intervals and hypothesis testing for one and two samples; simple and multiple linear regression; analysis of variance; design of experiments; and nonparametric methods. Selected topics, such as quality control and time series analysis, may also be included. Statistical software will be used throughout the course and statistical inference will be based on examples using real data. Students will participate in group projects of data analysis. They will be trained in the different phases of the professional statistician’s work, namely: data collection, description, analysis, testing, and presentation of the conclusions. Prerequisite: MA 540.

This course will provide foundational ideas as well as advanced techniques in linear algebra that are employed in computational science of big data. Students will work with vector-matrix representation of various types of structured and unstructured data and how different models and processes could be understood in terms of linear algebra operations and algorithms. Efficient implementation of algorithms for high dimensional data by using Randomized Numerical Linear Algebra will be one of the focal points. Students will develop and improve their coding skills in Python and MATLAB for implementation of several algorithms. In addition, students will read past and current literature in machine learning and data science to familiarize themselves with current trends and challenges in linear algebra for solving real life problems. Prerequisites: MA 123, MA 124 or equivalent, MA 232 or equivalent, MA 222 or equivalent, and have basic knowledge of MATLAB (FE 516) or Python (FE 520).

The objective of this course is to introduce the students to the theory and methods of optimization used in data science. The first portion of the class focuses on elements of convex analysis and subgradient calculus for non-smooth functions, optimality conditions for differentiable and for non-smooth optimization problems, and Lagrangian duality. The main part of the class discusses numerical methods for optimization with a focus and application to problems arising in data science. Approaches to large-scale/big-data optimization include decomposition methods, design of distributed and parallel methods of optimization, as well as stochastic approximation methods. Examples of optimization models in classification, clustering, statistical learning, compressed sensing will be discussed in order to illustrate the theoretical and numerical challenges and to demonstrate the scope of applications.

This course will provide an introduction to machine learning theory, algorithms, and applications. Content aims to provide students with the knowledge to understand key elements of how to design algorithms/systems that automatically learn, improve, and accumulate knowledge with experience. Topics covered in this course include decision tree learning, neural networks, Bayesian learning, reinforcement learning, ensembling multiple learning algorithms, and various application problems. Students will be provided opportunities to simulate their algorithms in a programming language and apply them to solve real-world problems. Cross-listed with: EE 695.

Deep learning (DL) is a family of the most powerful and popular machine learning (ML) methods and has wide real-world applications including face recognition, machine translation, self-driving car, recommender system, and playing the Go game. This course is designed for students either with or without ML background. The course will cover fundamental ML, computer vision, and natural language problems and DL tools for solving the problems. The students will be able to use DL methods for solving real-world ML problems. The homework is mostly implementation and programming using the Python language and popular DL frameworks such as TensorFlow and Keras. Knowledge and skills in Python programming and linear algebra are strictly required. Probability theory, statistics, and numerical analysis are recommended by not required. Knowledge of machine learning and artificial intelligence is helpful but unnecessary.

This course provides a broad and systematic introduction to time series models and their applications to modeling and prediction. It utilizes real-world examples to apply a variety of time series models and methods. After successful completion of this course, students will be able to work with stationarity and measures of dependency, time series regression, graphical analysis, trend and seasonality detection and removal, and moving-average filtering. Students will also be able to apply linear time series analysis, spectral analysis, and multivariate time series methods. Additional topics that will be covered include long-memory processes, unit root testing, volatility modeling, state space models and Kalman filtering.

This course uses advanced technologies, such as IBM’s Blue Mix and Google’s TensorFlow, as building blocks, allowing student teams to exercise their ingenuity to develop applications that use AI and machine learning in entirely new business application areas. The products of cognitive computing are beginning to appear in the marketplace, while so-called “deep-learning” AI applications are finding their way into healthcare, energy management, security, marketing and financial services.

The field of Big Data is emerging as one of the transformative business processes of recent times. It utilizes classic techniques from Business Intelligence & Analysis, along with new tools and processes to deal with the volume, velocity, and variety associated with big data. As they enter the workforce, a significant percentage of BIA students will be directly involved with big data either as technologists, managers, or users. This course will build on their understanding of the basic concepts of BI&A to provide them with the background to succeed in the evolving data centric world, not only from the point of view of the technologies required, but in terms of management, governance, and organization. Tools will include Hadoop, Hbase, and related software.