Post graduate program in Data Sciences, Design & Visualization

Pedagogy:

In order to cater to the needs of working professionals, the program is delivered in the blended mode, which does not require the participant to take any time off work. The content is covered through:

1. 6-hour face-to-face classes at the Bridge centre on either Saturday or Sunday (every weekend)

2. Online self-learning through the week; 7 to 10 hours a week

There are four sequential courses, of 8 to 10 weeks each. The course details are as below.

1) Database Systems and Technologies

Database systems are at the core of modern information systems. In this course, students will explore the principles of data management and data extraction. Database design, modeling, and implementation concepts will be reviewed and discussed. Students will learn how the different forms of data such as structured data in SQL databases, and unstructured data in NoSQL database systems are stored and accessed. Students will also learn how to query a database and extract useful information to support the decision making process for information design and strategy. The course has hands-on modules that provide the students with SQL and NoSQL programming skills in order to extract and process data from a database engine and present the information in forms suitable for end-users.

2) Introduction to Data Analytics and R

This course will introduce to their primary tool R and to the appropriate uses of analytics and its limitations and define how to approach the various stakeholders within an organization with analytic information. Included will be a review of the ethical, regulatory, and compliance issues related to a given business problem and/or solution. Time will be spent interpreting performance-based organizational issues while concurrently identifying solutions for these same performance-based organizational issues. In addition, time will be spent identifying best practices to plan for engaging, implementing, and sustaining organizational change.

3) Applied Analytics for Business

Students learn to apply statistical techniques to the processing and interpretation of data from various industries and disciplines. This course introduces statistical models as they are used in predictive analytics. It addresses issues of statistical model specification and model selection, as well as best practices in developing models for management.

4) Data Visualization and Communication

This course entails learning by doing—working with data and text, utilizing models of text and data, working within an open-source programming environment, and building interactive visualizations for the web. It reviews the psychology of human perception and cognition and best practices in visualization and web design. Assignments involve reviewing and developing interactive visualizations of text, time series, networks, and maps. This is a project-based course with individual and team assignments.

In addition to the core Northwestern Program, Bridge School of Management will provide three add-on modules for free to enable students of the program to perform better in the job market.These three modules have been successfully used extensively in the Predictive Business Analytics Program.

1) Introduction to Advanced MS Excel

One of the key tools for the program will be the all-powerful and popular MS Excel. While R will be covered in the core program, a certain level of prerequisite in Excel is required. The module on Excel will cover all the key elements of applying MS Excel for both analytics and otherwise. This is a skill that, in itself, is useful for anyone in their current roles as well.

2) Business Communication

To help students train for a long and successful career, we realize, soft skills are often as important even in strongly quantitative fields like data science and analytics. The module on covers a variety of topics related to communication, professionalism, getting your message across to groups, interviewing, presenting to groups etc. and is taught by leaders in the area.

3) Capstone Project

The project is a key part of the course and students get to use a variety of models and tools that they have learnt during the modules on a single dataset. The project is performed as a mentored class under the guidance of a faculty member.