CCNY

TAG = "decision making"

Fall 2010

Course ID Course Name Code# Prerequisite Day & Time Room Instructor
First Year
MIS G1010 Statistics and Decision Making 2733 N/A Monday 18:50 NAC8/207 Prof. Mowshowitz
MIS G2010 OOD and Software Engineering 2975 N/A Tuesday 18:50 NAC6/115 Prof. Kawaguchi
Second Year
MIS G3030 Organization and Management 3520 N/A Monday 18:50 HR13 Dr. Jonatan Jelen
MIS G4010 System Analysis and Design 3521 N/A Thursday 18:50 NAC5/148 Mr. Hernandez & Prof. Gertner

† Each class lasts two and a half hours. ‡ Please see the last minute class room change posted in front of the computer science.

Fall 2009

Course ID Course Name Code# Prerequisite Day & Time Room Instructor
MIS G1010 Statistics and Decision Making N/A Monday 18:50 NAC 4/148 Prof. Mowshowitz
MIS G2010 OOD and Software Engineering N/A Tuesday 18:50 NAC 4/205 Prof. Kawaguchi

† Each class lasts two and a half hours.

MIS G3010: General Economics and Finance

Course ID: MIS G3010
Course Name: General Economics and Finance
Description:
Provides an advanced introduction to economics and finance. The major focus is on forces determining product and factor prices and quantities under alternative market structures. Particular attention given to financial market prices, portfolio analysis, measuring and pricing of risk. The influence of macroeconomic determinants on financial markets is also covered.

Term: Spring
Credits: 3.0
Pre/Co-requisite: none

MIS G1010: Statistics and Decision Making

Course ID: MIS G1010
Course Name: Statistics and Decision Making
Description:
The objective of this course is to help you learn to analyze data and use methods of statistical inference in making business decisions. This course will focus on the application of fundamental concepts covered in Probability and Decision Making to the problem of drawing inferences from data on observed outcomes. Topics covered during the first part of the course will include statistical sampling and sampling distributions, point estimation and confidence intervals, hypothesis testing, and correlations among variables. The second part of the course will focus on multivariate analysis, with special attention paid to the inferences that may drawn with respect to prediction and causality. Microcomputer statistical packages support the course content.

Term: Fall
Credits: 3.0
Pre/Co-requisite: none

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