BIG DATA AND DATA ANALYTICS
BOS DATA, MODELS AND DECISIONS
Data, Models and Decisions (QTM7200) Boston Location
Data, Models and Decisions (DMD) - This course is concerned with identifying variation, measuring it, and managing it to make informed decisions. Topics include: numerical and graphical description of data, confidence intervals, hypothesis testing, regression, decision analysis, and simulation. Applications to Economics, Finance, Marketing, and Operations illustrate the use of these quantitative tools in applied contexts. The course utilizes spreadsheet, statistical, and simulation software.
QTM7200 will meet for 7 face-to-face sessions, plus a 3 hour online session, and an integrated session with ECN7200 and MKT7200 held on a Saturday April 23rd 9:00 AM - Noon - Boston location.
QTM7200 is part of Cluster B, but can be taken prior to ECN7200 and MKT7200 as a prerequisite or students can take QTM7200 as a co-requisite while enrolled in ECN7200 and MKT7200. All of the courses in Cluster B must be taken at the same location.
QTM7200 is a prerequisite for Cluster F MOB7202 and MBA7201.
Business Intelligence, Analytics & Visualization
Formerly Business Intelligence & Data Mining
This course will examine the methods and challenges faced in turning data into insightful analytics in business. With data sizes significantly increasing in the last decade, extracting meaningful information to compete successfully is essential. You will accomplish this by learning techniques for data gathering, data analysis, and visualization as well as in discussion on companies currently trying to turn the information they gather into business opportunities. We will learn a variety of methods and software for finding patterns(such as regression, neural networks, association rules, CART, forecasting etc.), building models, and ultimately making decisions using large data sets. We will address questions such as:
Guest speakers who are executives and consultants in the field of analytics and visualization will discuss how they address these challenges in their companies. This is a hands-on course with in-class exercises and group projects to help students learn and apply data analysis techniques preparing them for the practical challenges analysts face in the real world.
- How does Amazon recommend products based on your past purchases ?
- How to forecast energy consumption based on historical weather and consumption data?
- How do credit-card companies detect fraud?
- What challenges does Big Data pose to companies and how to handle these challenges?
Prerequisite: Evening: QTM7010 or QTM8400 or QTM7200
Fast Track: MBA7335 or (ECN7201 and MIS7200)
One Year: MBA7210 or QTM7200
Two Year: MBA7320 or QTM7301 or QTM7200
INTRO DATA SCIENCE BUS ANALYTICS
Friday, January 22nd 6:30 PM - 9:30 PM
Saturday, January 30th 8:30 AM - 4:30 PM
Saturday, February 6th 8:30 AM - 4:30 PM
DROP DEADLINE: January 22nd by 11:59 PM
QTM9515 Introduction to Data Science and Business Analytics
1.5 credit Intensive Elective
This course is an introduction to data science – the science of iterative exploration of data that can be used to gain insights and optimize business processes. The course discusses the data analytics lifecycle, and introduces predictive analytics techniques in the context of real-world applications from diverse business areas such as database marketing, financial forecasting, and operations. The focus is on framing business problems as analytics problems. A brief map of applications and an overview is provided for advanced methods for data visualization, logistic regression, decision tree learning methods, clustering, and association rules. Students will gain exposure to different software packages for data visualization as well as R, the most popular open-source package used by data scientists around the world. Since R is freely available, students will be able to apply the skills acquired in this course regardless of where they work after graduation. Students can pursue these topics in more depth in QTM7571 Business Intelligence, Analytics, and Visualization.