Descriptive statistical measures and probability theory are combined to provide the basis for statistical and analytic based decision-making techniques. Software is introduced for data visualization techniques and for analytics on spreadsheets. Topics covered: data analytics using spreadsheets; data presentation and visualization; measures of central tendency and variability; basic probability laws; binomial; 't,' and normal distributions; confidence intervals.
The Statistical Data Analytics course uses statistical methods to analyze data in the areas of estimation, inference and prediction. This includes applications of confidence intervals and hypothesis testing, simple and multiple linear regression analysis to estimate relationships between variables and make predictions, models for time series forecasting and variance and chi-square tests. Excel and more advanced statistical software will be utilized to analyze data.
Utilizing MS Excel and Database Access, as well as IBM Analytics (SPSS) software, students will: become acquainted with essential data mining and machine learning concepts, practice regression, cluster, classification and decision tree analysis methods; learn data unsupervised and supervised learning models, and apply them through the course project.
This course introduces the student to various Operations and Supply Chain Management tools and quantitative decision making models. Quantitative decision making adds value to data by building models that aid the prescriptive decision-making process. This course focuses on model formulation and the rationale behind the quantitative tools and techniques without delving deep into the mathematical theory. Topics in this course include Forecasting, Statistical Process Control, Scheduling, Decision Analysis, and various Optimization models such as Linear and Integer Programming.