Curriculum

You must complete this online course before you begin the program.

This course provides general exposure to basic statistical concepts that are necessary for students to understand the content presented in more advanced courses in the program. The course covers theoretical distributions and the way these distributions are used to assign probabilities to events in some depth. The course also introduces students to descriptive statistical methods to explore and summarize data, methodologies for sampling units for measurement or analysis, drawing inferences on the basis of knowledge gained from samples to populations, assessing relationships between variables, and making predictions based upon relationships between variables.

An advanced linear algebra course focused on the theoretical foundations and applications of linear algebra for machine learning. Upon completion of this course, students will be provided a strong foundation of theoretical linear algebra and linear analysis topics essential for the development of core machine learning and data mining concepts. In addition, various real-life applications of linear algebra for data analytics will be demonstrated.
Prerequisites:
Successful completion of Undergraduate level coursework in Linear Algebra.
Successful completion of the Advanced Linear Algebra for Machine Learning pretest exam with a passing grade, or, successful completion of online Coursera course as outlined and recommended by MSCA program.
MSCA & MSAP Students Only.

This one-day workshop is an introduction to the essential concepts and techniques for the statistical computing language R. Topics covered include the R and RStudio environment, arithmetic, basic data structure, importing and exporting data, visualization, and basic statistics. No prior R or programming experience is required.

This first quarter course introduces the essential general programming concepts and techniques to a data analytics audience without prior programming experience. The goal is to equip the students with the necessary programming skill to be successful in the other courses in the MSCA program. Topics covered include: boolean, numbers, loops, function, debugging, R's specifcs (such as list, data frame, factor, apply, RMarkdown), Python's specifics (such as NumPy, Pandas, Jupyter notebook), version control, and docker. Examples are drawn from the problems and programming patterns often encountered in data analysis. It will use the programming language R in the first part of the course and Python in the second part.

Time Series Analysis is a science as well as the art of making rational predictions based on previous records. It is widely used in various fields in today’s business settings. For example, airline companies employ time series to predict traffic volume and schedule flights; financial agencies measure market risk via stock price series; marketing analysts study the impact of a newly proposed advertisement by the sales series. A comprehensive knowledge of time series analysis is essential to the modern data scientist/analyst. This course covers important issues in applied time series analysis: a solid knowledge of time series models and their theoretical properties; how to analyze time series data by using mainstream statistical software; practical experience in real data analysis and presentation of their findings in a logical and clear way to various audiences.

This course provides a comprehensive and practical introduction to statistical data analysis. The statistical techniques taught in this course will enable students to analyze complex datasets and formulate and solve real- world problems to facilitate data-driven decisions. Throughout the course, students will learn concepts and fundamentals of statistical inference and regression analysis by studying theory, developing intuition, and working through several practical examples. Students will become proficient in interpreting standard regression output and conducting model selection and validation. Students will also learn the statistical programming language used to construct examples and homework exercises. Examples will be constructed using SAS or R. Students will have many opportunities to apply the new concepts to real data and develop their own statistical routines. The course also addresses the importance of quality control and reproducibility when conducting research and developing work product.
Prerequisites:
MSCA 31000: Introduction to Statistical Concepts
MSCA 37016: Advanced Linear Algebra for Machine Learning

Drawing on statistics of collecting and analyzing data, and machine learning algorithms that learn from experiences, data mining is a process of applying statistics and machine learning algorithms to discover patterns and rules that can generate business values. This course will introduce students to the common algorithms: association and sequence rules discovery, memory-based reasoning, clustering, classification and regression decision trees, logistic models, and neural network models. In addition, students will learn how to compare analytical results and give recommendations during the data mining process. In addition to assignments and tests, students are required to propose and complete a data mining research project of their own design.

This course in advanced data mining will provide a practical, hands-on set of lectures surrounding modern predictive analytics and machine learning algorithms and techniques. It will emphasize practice over mathematical theory, and students will spend a considerable amount of class time gaining experience with each algorithm using existing packages in R, Python, and Linux libraries. The course will cover the following topics: regression and logistic regression, regularized regression including the lasso and elastic net techniques, support vector machines, neural networks, decision trees, boosted decision trees and random forests, online learning, k-means and special clustering, and survival analysis.

This course concentrates on the following topics: Review of statistical inference based on linear model, extension to the linear model by removing the assumption of Gaussian distribution for the output (Generalized Linear Model), extension to the linear model by allowing a correlation structure for the model residuals (mixed effect models), and extension of the linear model by relaxing the requirement that inputs are combined linearly (nonparametric regression, regime switches). Course emphasizes applications of these models to various fields and covers main steps of building analytics from visualizing data and building intuition about their structure and patterns to selecting appropriate statistical method to interpretation of the results and building analytical model. Topics are illustrated by data analysis projects using R. Familiarity with R at some basic level is not a requirement but recommendation. Students can pick up the programming language by following the descriptions of the examples.

Effective data engineering is an essential first step in building an analytics-driven competitive advantage in the market. Modern data engineering platforms reduce manual data preparation by automating processes, which in turn, enable companies to focus on deriving efficiencies in data processing to develop impactful business insights. This course provides students with a thorough understanding of the fundamentals of data engineering platforms, for both operational and analytical use cases, while gaining hands-on expertise in building these platforms in a way to develop analytical solutions effectively. Students will have the opportunity to construct both relational and analytical databases on the cloud or on premise from real-life datasets while using programmatic or configuration driven data pipelines. By the end of the course, students will be able to design and implement an end-to-end data engineering platform capable of supporting sustainable analytics solutions.

This course teaches students how to approach Big Data and large-scale machine learning applications. While there is no single definition of Big Data and multiple emerging software packages exist to work with Big Data, we will cover the most popular approaches. Students will learn the Big Data infrastructure, including Linux, Massive parallelization and Distributed Computing, and how to apply both Hadoop and Spark map-reduce concepts for clustering, similarity search, web analytics and classification. During the course, we will cover the applications of NoSQL systems, such as JSON stores, object storage and Elasticsearch. The cloud computing section of the course will focus on virtualization and container orchestration, including virtual machines, dockers and Kubernetes. During the course students will gain hands-on expertise leveraging Hive, Pig, Python and PySpark for Big Data applications in client-server environment.

In Leadership Skills: Teams, Strategies, and Communications, students learn how to work effectively in teams to identify, structure, and communicate the business value of data analytics to an organization. The goals of the course are (1) to identify points in an organization that can benefit from analytics; (2) to structure analytic problems from a strategic perspective, thereby identifying business impact; (3) to develop the ability to communicate the power of analytics to others, especially senior leaders; and (4) to work in a team to accomplish these and related goals successfully. At the end of the course, students should have the ability to describe business problems that lend themselves to a data analytics approach, position these problems from the perspective of a coherent business strategy, and represent the power of analytics to a business audience. Students should also understand how to harness the powerful dynamics of a team to achieve excellence in the world of data analytics.

The demand for analytics and data-driven decision making creates a market demand for expertise driven leadership - evidenced in knowledgeable consultants that bring data science and results-driven impact to clients. The successful data science leader / consultant brings an uncommon combination of deep business acumen, data literacy, leading edge methodology experience, inspirational team leadership, client communication management and organizational change skills.

Successful consultants rely on a variety of consulting tools to diagnose organizational problems, identify solutions and deliver those solutions.

The Data Science for Consulting course will enable students:

Understanding the structure of consulting organizations and engagements.
Developing data science solutions to enterprise problems through employing traditional consulting frameworks and best practice tools.
Practicing successful project delivery through effective data discovery, communication, influential team leadership and client relationship management.

In addition to theory and experimentation, big data analytics has now emerged as an alternative way to discover new knowledge. This course covers the analytics research process from the translation of business problems into researchable questions that can be addressed by using analytics, development of data sources to address each key researchable issue, to the translation of research results back to business implications. By completing the course, students will be able to: frame a business problem; map alternative solutions to develop a plan; identify potential sources or relevant data; understand analytical principles that can be applied to design data-gathering experiments; explain the pros and cons of the selected methodology to the analytical team as well as non-analysts. Students will develop a research proposal to produce knowledge from data to address a real business problem in small steps throughout the course.

This course teaches students how to approach Big Data and large-scale machine learning applications. While there is no single definition of Big Data and multiple emerging software packages exist to work with Big Data, we will cover the most popular approaches. Students will learn the Big Data infrastructure, including Linux, Massive parallelization and Distributed Computing, and how to apply both Hadoop and Spark map-reduce concepts for clustering, similarity search, web analytics and classification. During the course, we will cover the applications of NoSQL systems, such as JSON stores, object storage and Elasticsearch. The cloud computing section of the course will focus on virtualization and container orchestration, including virtual machines, dockers and Kubernetes. During the course students will gain hands-on expertise leveraging Hive, Pig, Python and PySpark for Big Data applications in client-server environment.

This course teaches students how to approach Big Data and large-scale machine learning applications. While there is no single definition of Big Data and multiple emerging software packages exist to work with Big Data, we will cover the most popular approaches. Students will learn the Big Data infrastructure, including Linux, Massive parallelization and Distributed Computing, and how to apply both Hadoop and Spark map-reduce concepts for clustering, similarity search, web analytics and classification. During the course, we will cover the applications of NoSQL systems, such as JSON stores, object storage and Elasticsearch. The cloud computing section of the course will focus on virtualization and container orchestration, including virtual machines, dockers and Kubernetes. During the course students will gain hands-on expertise leveraging Hive, Pig, Python and PySpark for Big Data applications in client-server environment.

This course concentrates on the following topics: review of financial markets and assets traded on them; main characteristics of financial analytics: returns, yields, volatility; review of stochastic models of market price and their statistical representations; concept of arbitrage, elements of arbitrage pricing approach; principles of volatility analyses, implied vs. realized volatility; correlation, cointegration and other relationships between various financial assets; market risk analytics and management of portfolios of financial assets. The course puts special emphasis on covering main steps of building analytics from visualizing data and building intuition about their structure and patterns to selecting appropriate statistical method to interpretation of the results and building analytical models. Topics are illustrated by data analysis projects using R. Basic familiarity with R is a requirement.

(Data Science for Algorithmic Marketing) This course focuses on data science methods and algorithms for that are used to develop marketing strategies, and create a link between marketing, customer behavior and business outcome. The course will focus on analytical techniques organized according to the Strategic Marketing Process. The course would cover algorithms for competitive analysis and market sizing, market segmentation, targeted marketing via database marketing, design of new products, market sizing & forecasting via diffusion models, real time product positioning, algorithmic marketing in the digital world, pricing and promotions, marketing effectiveness and ROI. The course will use a combination of lecture, in-class discussions, and group work.
Prerequisites:
MSCA 31007: Statistical Analysis

This course teaches analytical tools commonly employed in the areas of credit and insurance risk. In the area of credit risk, students at the end of the course should be able to: Understand the business problems and their challenges in the consumer credit risk analytics, design and apply analytical approaches tailored to each problem, and identify and address the underlying assumptions in the designed approaches. In the area of insurance risk, students should be able to: Understand various risks related to the insurance business, in particular the underwriting or pricing risks, quantify and price an individual insurance risk exposure and construct customer segmentation by using statistical and actuarial approaches, and assess company’s overall risk management performance at the portfolio level.

One of the most actively developing areas of analytics is the real time analytics because of the growing number of data sources capable of collecting data round the clock in ever-larger amounts and with more complex structure; penetration of smart sensors everywhere where data collection used to be not possible, from micro to macro world and into hostile environments unsuitable for human observers; increasing demand for decisions made at latencies below human reaction time. Conducting real time analysis is different from the traditional data analysis in batch mode. Streaming data makes the very concept of sample nonexistent. Usual static sample characteristics, like p-value turn into dynamically changing processes. The old statistical concept of sufficient statistics may be getting a whole new meaning in the context of streaming data. The focus of the course is on stochastic methods suitable for real time analysis and their statistical implementations. Students will work with real data streaming live from the course server. We will learn about stochastic processes observed at random times and apply them to problems of monitoring, early event detection, prediction and control. Students are expected to be comfortable enough with R to write software for processing and responding to streaming data.

This course teaches students how to work with real-world data and leverage analytics to help solve business problems. We will examine data requirements and sources of data; utilize statistical techniques and visualization methods to evaluate data completeness and quality; assess and compare model performance; learn how to effectively communicate analytical insights to non-technical audience. Students will learn through a combination of in-class discussions, case studies, and team projects. Team exercises will teach students effectively communicate between business process owners and analytical experts to overcome typical barriers in Business Analytics, such as data availability, resource constraints, and resistance to change.

Given the breadth of the field of health analytics, this course will provide an overview of the development and rapid expansion of analytics in healthcare, major and emerging topical areas, and current issues related to research methods to improve human health. We will cover such topics as security concerns unique to the field, research design strategies, and the integration of epidemiologic and quality improvement methodologies to operationalize data for continuous improvement. Students will be introduced to the application of predictive analytics to healthcare. Students will understand factors impacting the delivery of quality and safe patient care and the application of data-driven methods to improve care at the healthcare system level, design approaches to answering a research question at the population level, become familiar with the application of data analytics to impacting care at the provider level through Clinical Decision Systems, and understand the process of a Clinical Trial.

This course introduces students to how optimization and simulation techniques can be used to solve many real-life problems. It will cover two classes of optimization methods. First class has been developed to optimize real, non- simulated systems or to find the optimal solution of a mathematical model. The methods that belong to this class include liner programming, quadratic programming and mixed-integer programming. Second class of methods has been developed to optimize a simulation model. The difference with the classical mathematical programming methods is that the objective function (which is the function to be minimized or maximized) is not known explicitly and is defined by the simulation model (computer code). The course will demonstrate multiple approaches to build simulation models, such as discrete event simulations and agent-based simulations. Then, it will show how stochastic optimization and heuristic approaches can be used to analyze the simulated system and design a sequence of computational experiments that allow to develop a basic understanding of a particular simulation model or system through exploration of the parameter space, to find robust plausible behaviors and conditions and robust near-optimal solutions that are not prone to being unstable under small perturbations.

Bayesian inference is a method of statistical learning in which Bayes' theorem is used to understand probability distributions of unobserved variables, like model parameters or predictions for future observations. Bayesian analysis is especially important because it naturally allows updating the probability for a model or hypothesis as more evidence or information becomes available. This property of Bayesian approach plays significant role in dynamic analysis of a sequences of data. Applications of Bayesian analysis have exploded in recent period thanks to advances in computing techniques that made Bayesian approaches like Gibbs sampling, Markov Chain Monte Carlo, Dirichlet processes the main tools for advanced machine learning. The focus of this course is on foundations of Bayesian approach, its applications via hierarchical models, linear and generalized linear models, mixed models and various types of Bayesian decision making. Students will learn necessary facts of probability theory, fundamentals of Bayesian method as well as most modern applications of the approach by accessing through R important software products for efficient sampling: JAGS and STAN. Students, are expected to be comfortable with coding in R and ready to learn new concepts of theory and practice of Bayesian approach.

Successfully marketing brands today requires a well-balanced blend of art and science. This course introduces students to the science of web analytics while casting a keen eye toward the artful use of numbers found in the digital space. The goal is to provide marketers with the foundation needed to apply data analytics to real-world challenges they confront daily in their professional lives. Students will learn to identify the web analytic tool right for their specific needs; understand valid and reliable ways to collect, analyze, and visualize data from the web; and utilize data in decision making for their agencies, organizations or clients. By completing this course, students will gain an understanding of the motivations behind data collection and analysis methods used by marketing professionals; learn to evaluate and choose appropriate web analytics tools and techniques; understand frameworks and approaches to measuring consumers’ digital actions; earn familiarity with the unique measurement opportunities and challenges presented by New Media; gain hands-on, working knowledge of a step-by-step approach to planning, collecting, analyzing, and reporting data; utilize tools to collect data using today’s most important online techniques: performing bulk downloads, tapping APIs, and scraping webpages; and understand approaches to visualizing data effectively.

Since the era of big data started, challenges associated with data analysis have grown significantly in different directions: First, the technological infrastructure had to be developed that can hold and process large amounts of data from different sources and of multiple not always well formalized formats. Second, data analysis methods had to be reviewed, selected and modified to work in distributed computational environments like combinations of in-house clusters of servers and cloud. But the biggest challenge of all is learning to think differently in order to ask new types of questions that could not be answered by analyses of less complex data streams with less complex technological infrastructure. In recent years significant progress has been achieved in creating technological ecosystems for big data analysis. Innovative technologies such as open source projects MapReduce, Hadoop, Spark, Storm, Kafka, TensorFlow, H2O, etc. allowed us to look at depths of data unseen before. We have now growing number of sources and educational courses introducing these new tools. It appeared little more difficult to develop new data analysis methods appropriate for the new data ecosystems. There are some new interesting ideas, there is significant amount of empirical studies. But the methods of late 19-th and first half of 20-th centuries, albeit transformed to run with technology of 21-st century, are still dominating our research. Traditional and new concepts and methods of big data analysis are typically, with few exceptions, covered in books and taught in courses requiring a laptop environment, often testing limits of computing power of personal hardware, but not giving enough flavor of the 21-st century combination of high-end technology and discussion of methods in depth. The goal of this course is to fill this gap and teach students to think about real problems by analyzing big data in new data analysis ecosystems. The course is project-based: we will take up several real life projects and discuss different approaches to digging for insights, possible pitfalls and applications. In our work on projects we will use Python and modern cloud computing environments: Spark, TensorFlow.

Extracting actionable insights from unstructured text and designing cognitive applications have become significant areas of application for analytics. Students in this course will learn foundations of natural language processing, including: concept extraction; text summarization and topic modeling; part of speech tagging; named entity recognition; semantic roles and sentiment analysis. For advanced NLP applications, we will focus on feature extraction from unstructured text, including word and paragraph embedding and representing words and paragraphs as vectors. For cognitive analytics section of the course, students will practice designing question answering systems with intent classification, semantic knowledge extraction and reasoning under uncertainty. Students will gain hands-on expertise applying Python for text analysis tasks, as well as practice with multiple IBM Watson services, including: Watson Discovery, Watson Conversation, Watson Natural Language Classification and Watson Natural Language Understanding.

Developing end-to-end automation and intelligent systems is now the most advanced area of application for analytics. Building such systems requires proficiency in programming, understanding of computer systems, as well as knowledge of related analytical methodologies, which are the skills that this course aims to teach to students. The course focuses on python and is tailored for students with basic programming knowledge in Python. The course is partially project based. During the first three sessions, we will review basic python concepts and then learn more advanced python and the ways to use Python to handle large data flows. The later sessions are project based and will focus on developing end-to-end analytical solutions in the following areas: Finance and trading, blockchains and crypto-currencies, image recognition, and video surveillance systems.

This course is an introduction to reinforcement learning, also known as neuro-dynamic programming. It discusses basic and advanced concepts in reinforcement learning and provides several practical applications. Reinforcement learning refers to a system or agent interacting with an environment and learning how to behave optimally in such environment. An environment typically includes time, actions, states, uncertainty and rewards. Reinforcement learning combines neuro networks and dynamic programming to find an optimal behavior or policy of the system or agent in complex environment setting. Neuro networks approximations are used to circumvent the well-known 'curse of dimensionality' which have been a barrier to solving many practical applications. Dynamic programming is the key learning mechanism that the system or the agent uses to interact with the environment and improve its performance. Students will master key learning techniques and will become proficient in applying these techniques to complex stochastic decision processes and intelligent control.

Prerequisites: MSCA 31009: Machine Learning & Predictive Analytics

The objective of this course is two-folds - first, to understand what Machine Learning Operations (MLOps) is and why it is a key component in enterprise production deployment of machine learning projects. Second, to expose students to software engineering, model engineering and state-of-the-art deployment engineering with hands-on platform and tools experience.

This course crosses the chasm that separates machine learning projects/experiments and enterprise production deployment. It covers 3 pillars in MLOps: software engineering such as software architecture, Continuous Integration/Continuous Delivery and data versioning; model engineering such as AutoML and A/B experimentation; and deployment engineering such as docker containers and model monitoring. The course focuses on best practices in the industry that are critical to enterprise production deployment of machine learning projects.

Having completed this course, a student understands the machine learning lifecycle and what it takes to go from ideation to operationalization in an enterprise environment. Furthermore, students get exposure to state-of-the-art MLOps platforms such as Allegro AI, xpresso, Dataiku, LityxIQ, DataRobot, AWS Sagemaker, and technologies such as gitHub, Jenkins, slack, docker, and kubernetes.

This short course is designed to provide a brief, practical introduction to working with data on a Hadoop cluster. The course is aimed at students with no prior knowledge of Hadoop. Topics covered include loading data into Hadoop cluster, using Hive HQL and using Pig script language. Course includes live demos and tutorials so students should complete exercises in class. Students who complete the course will acquire skills to be able to take further studies in Big Data and Text Analytics course

This short, practical course is designed to provide a brief introduction to Linux operating system. It is aimed at students with no prior knowledge of Linux. Topics covered include uploading files to Linux, working with files in Linux, and managing processes in Linux shell. The course includes live demos and tutorials. Students who complete this tutorial course will acquire skills to be able to take further studies in Big Data and Text Analytics course.

This one-day workshop is an introduction to the essential concepts and techniques for the statistical computing language R. Topics covered include the R and RStudio environment, arithmetic, basic data structure, importing and exporting data, visualization, and basic statistics. No prior R or programming experience is required.

This course in Deep Learning and Image Recognition will provide a practical, hands-on set of lectures on Deep Learning and Image Processing tools and techniques. It will emphasize practice over advanced mathematical theory, and students will spend a considerable amount of class time gaining experience on Neural Networks and their applications in Python and other open source libraries.

Big Data analytics has generated significant change in industries such as manufacturing, health care, transportation, and retail, as well as in government and social services. For data analysts to manage these changes constructively, technical skills alone are not sufficient. Understanding the challenging ethical issues Big Data presents needs to supplement technical competence.

This four-session course will focus on two of the more critical Big Data ethical issues at hand: bias and privacy. Its goal is to equip students with the ability to identify, understand, and discuss these the ethics of bias and privacy in the context of their work.

The course is offered on a Pass/Fail basis and employs lecture, discussion, guest speakers, case studies, mock debates, and brief videos as pedagogical techniques.

There is no required reading for the course, although a list of sources and relevant readings will be provided. Students will be expected to participate actively in classroom discussion and activities. One of those activities will be a series of four classroom debates. Students may need to meet outside of class to prepare for these debates. There are no quizzes or essays. Students can only miss one of the four sessions to pass this pass/fail course.Big Data analytics has generated significant change in industries such as manufacturing, health care, transportation, and retail, as well as in government and social services. For data analysts to manage these changes constructively, technical skills alone are not sufficient. Understanding the challenging ethical issues Big Data presents needs to supplement technical competence.

Big data has made a big difference, reshaping business, health care, and our understanding of the physical world around us. At the same time, Big Data and data analytics have raised numerous ethical questions about how data is collected, how data analytics methods transform that data into predictive information, and how that information is then used. This 3-hour seminar will introduce you to the benefits of Big Data and data analytics as well as the ethical challenges they raise. We will examine the rise of Big Data and data analytics as a fundamental paradigm shift in human inquiry; review ethical frameworks that help contextualize conversations about the benefits and challenges of Big Data and data analytics; study challenges related to privacy, anonymity, bias, and disparate impact; compare regulatory environments in the U.S., European Union, India, and China; and study how one large corporation has wrestled with its own Big Data and data analytics ethical challenges.

This course will help you navigate your career in data science and land a job that fits your needs and desires. It starts with taking a deeper discovery into who you are, clarifying what you want to do with your career, and navigating the market to find the right company and job match.
Prerequisites:
Restricted to MSCA & MSAP students, and MScA Alumni Scholars only.