18 courses with the subject DATA, each shown exactly as we captured it from the college's catalog, with every element we hold. Where the wording looks broken, that is our reading of the catalog, not the college's text.
DATA 200Introduction to Data Analytics,4
This is an introductory course in using modern data analysis concepts and tools to gain insight and make decisions in a business or organizational setting. Topics include data storage, business intelligence, basic data mining and modeling, visualization, prediction/forecasting, and clustering/segmentation. Students will complete at least one data analytics project, starting from an original research question and concluding with actionable recommendations.
An introduction to the construction and analysis of least-squares models, including multiple regression, ANOVA, ANCOVA, and mixed models. Generalized linear models will also be presented, with special attention paid to logistic regression and log-linear models. Examples and applications will be drawn from various disciplines, including biology, medicine, economics, engineering, and the social sciences.
Forecasting is the science of predicting future events and outcomes. In this course students will learn how to effectively use both data and theory to create forecasts and how to quantify and communicate uncertainty in forecasts. Topics include random walks, Markov models, time series analysis, Bayesian methods and qualitative forecasting.
Data mining is the study of discovering and assessing patterns, relationships and information within large datasets. This course provides an introduction to data mining with an emphasis on predictive modeling techniques and machine learning algorithms. Examples and applications will be drawn from various disciplines.
Courses covering various topics of interest in this particular discipline are offered regularly. Contact department or program chair for more information.
This course will allow students to apply their knowledge of data wrangling, analysis, and visualization to implement a data project. Students will learn concepts of data and project management, applying their knowledge to the solving of a significant data-rich challenge. PEAK DESCRIPTION: Students will seek out an organization of their choosing (non-profit, commercial business, political campaign, internal to Concordia College group, etc.) and complete a data analytic consulting project. Students will work with 2-4 members to meet with stakeholders of the organization to discuss their data needs, perform data analysis, and propose solutions based on the analysis. Students will also complete a postmortem report documenting their skills gained and how their perspective was changed by the project. Examples of past projects include quantifying the local impact of domestic violence for YWCA, performing an equity analysis for Homeless2Housed, and designing a food preference survey for Emergency Food Pantry.
This course provides an opportunity for individual students to conduct in-depth study of a particular topic under the supervision of a faculty member. Contact the department or program chair for more information.
This course provides an opportunity for individual students to conduct research in a specific area of study, completed under the direction of a faculty mentor. Specific expectations of the research experience to be determined by the faculty. Repeatable for credit. Prerequisite: consent of instructor.
This course focuses on technical and visual aspects of inspecting and presenting data. Technical topics include importing data from various sources, establishing relationships between data tables, transforming data, filtering, sorting, and aggregation. Visuals will be designed to focus attention on what the data is saying, with a special focus on visuals that respond dynamically to user manipulations. Emphasis will be placed on the design/refinement cycle for visualizations.
This course allows the student to understand and demonstrate knowledge of descriptive and inferential statistics used in research, and apply their knowledge to real-world situations and research questions. Emphasis is placed on distinguishing similarities and differences among statistical tests, and recognizing the essentiality of statistics for producing and comprehending scientific research
Forecasting is the science of predicting future events and outcomes. In this course students will learn how to effectively use both data and theory to create forecasts and how to quantify and communicate uncertainty in forecasts. Topics include random walks, Markov models, time series analysis, Bayesian methods and qualitative forecasting.
Data mining is the study of discovering and assessing patterns, relationships and information within large data sets. This course provides an introduction to data mining with an emphasis on predictive modeling techniques and machine learning algorithms. Examples and applications will be drawn from various disciplines.
The students will study different patterns of agricultural organizations' decision-making and ways that data analysis can be effectively used for each type. The course provides an understanding of the basics of several important analytic methods for agriculture business. Students will learn various machine learning models using Python in this course which will help them in making better decisions.
Students will learn specialized applications of operations research to problems arising from business. These will include data envelope analysis, transportation/transshipment problems, goal programming, network models (including PERT-CPM), and capital budgeting. Other topics such as inventory models, facility location problems, etc. will be covered as time and student interest permit. Special attention will be paid to the development and analysis of models for realistic medium- to large-scale problems.
The main purpose of the capstone course is to provide the culminating, integrative curricular experience for students. The course consolidates students' learning to develop a project with knowledge gained from many areas in the MSQM. The focuses of the course are case analyses and professional development.
Integrative Capstone Experience II's main purpose is to provide a structured means for students to get hands-on experience in real-life business analytics practices. Students will apply skills and knowledge gained throughout the MSQM program, such as statistical techniques, models, and analytical decision-making that support the business-defined problems scoped collaboratively between companies and Concordia.
