LESS THEORY. MORE APPLICATION.

Foundations of Data Management course provides students exposure to fundamental data management skills used in modern information systems that support various operational and functional areas within a business organization. Topics covered in the course has an emphasis on how data is fundamentally identified, organized, described and managed as the most valued asset within an organization. Course emphasis is also on applied learning of concepts and skills for relational data modeling and querying. This course will help prospective Data Science and Analytic business professionals to develop and apply data management skills that will be essential to the success in subsequent coursework.

This course provides students with a foundation in statistical basic statistical analysis, focusing primarily on descriptive univariate statistics. Topics addressed in this course include variables and their properties, measurement scales,  descriptive analyses of continuous and categorical variables, Central Limit Theorem, univariate and bivariate estimation, and hypothesis testing logic and procedures. Students will be exposed to hands-on computational examples using R and SPSS as they learn how to apply the various statistical concepts covered in this course to real-life business situations.

This course introduces students to the fundamentals of exploratory data analyses, broadly defined here as review of the available/focal data, and extraction of descriptive characteristics with the goal of generating valid and reliable insights. The course covers the basic data due diligence and curation considerations, key data preparatory steps, and offers an overview of a general descriptive data analytical framework. Analytic approach-wise, the course addresses analyst-led exploration utilizing classical statistical techniques, as well as automated data mining applications, while addressing topics of statistical inference, statistical significance, and outcome validity and reliability. Lastly, the course combines conceptual overview of the focal concepts and statistical reasoning, while also providing hands-on introduction to the practical side of data exploration. The general instructional approach used in this course is one that casts exploratory data analyses in the context of the data –> information –> knowledge continuum that underpins extraction of decision-guiding insights out of the available data as a way of answering business questions.

This course offers students a hands-on introduction to the basic concepts, practices and applications of developing forward-looking – i.e., predictive – statistical models. Content-wise, the class begins with a conceptual overview of multivariate statistics, followed by a discussion of commonly used types of multivariate predictive models. The second part of the course focuses on hands-on applications of model fitting and evaluation using scripring (R and/or SPSS Statistics) and GUI/object-based (SPSS Modeler) applications.

This course introduces data science students to the foundations of statistical machine learning, which are automated methods of broadly scoped – i.e., descriptive, predictive, explanatory – data analyses. The first part of the course offers a conceptual overview of established and emerging machine learning algorithms, followed, in the second part, by a discussion and hands-on introduction to some of the more widely used machine learning platforms (e.g. R, SAS Enterprise Miner, SPSS Modeler). Some of the specific topics include unsupervised and supervised learning, text mining, data preparation and result interpretation.

This course focuses on the effective communication of data analysis and its insights and implications. Students will learn the principles and techniques for information visualization and representation as well as verbal and written communication. Students will develop proficiency in several of the latest tools for visualization. Students will use real-world business scenarios to gain experience designing and building data visualization and communication.  Best practices will be highlighted and students will receive tailored individual coaching and feedback sessions to accelerate skill improvement.

This course introduces students to the current and emerging topics and considerations addressing the issue of data governance, usage and security; furthermore, this course also investigates the ethical and legal right and responsibilities of data analysts, and delves into questions that emerge from developing, storing, analyzing and using data.  Issues including intellectual property, data ownership, storage security and safeguards as well as the human impact of using data are investigated; case studies and scenario explorations in a range of industries from consumer products and retail to government and social justice are used to illustrate and apply the concepts discussed in class, with the goal of preparing students to manage these often grey issues during their career.

The final course in the Data Science curriculum, the Capstone is a practicum focused on an analytic skill set that is an essential part of a data scientist skillset: multi-source analytics. Unlike the earlier courses which combined theory and practice, this course is heavily skewed toward the latter, giving students the opportunity to focus the bulk of the 8 week course period on fine-tuning their hands-on computational programming and related skills.