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Master's in Data Analytics: Course Schedule & Descriptions

FALL 2020 START  August 22-23, first in-person residency weekend

Fall 1 Foundations of Data Analytics 3
Fall 2 Data Mining:  Algorithms and Applications 3
Spring 1 Statistical Methods for Data Science and Analytics 3
Spring 2 Fundamentals of Data Storage, Cleaning, and Retrieval 3
Summer 1 Fundamentals of Programming in Python 3
Fall 1 Qualitative Methods 3
Fall 2 Quantitative Reporting and Modeling 3
Spring 1 Predictive and Proscriptive Modeling 3
Spring 2 Advanced Data Analytics 3
Summer 1 Practicum in Applied Analytics, Data Communication, & Ethics 3
Total program credits   30

SPRING 2021 START  January 9-10, first in-person residency weekend

Spring 1  Foundations of Data Analytics 3
Spring 2 Data Mining:  Algorithms and Applications 3
Summer 1 Statistical Methods for Data Science and Analytics 3
Summer 2 Fundamentals of Data Storage, Cleaning, and Retrieval 3
Fall 1 Fundamentals of Programming in Python 3
Fall 2 Qualitative Methods 3
Spring 1 Quantitative Reporting and Modeling 3
Spring 2 Predictive and Proscriptive Modeling 3
Summer 1 Advanced Data Analytics 3
Fall 1 Practicum in Applied Analytics, Data Communication, & Ethics 3
Total program credits   30

SUMMER 2021 START  May 15-16, first in-person residency weekend

Summer 1  Foundations of Data Analytics 3
 Summer 2 Data Mining:  Algorithms and Applications 3
Fall 1 Statistical Methods for Data Science and Analytics 3
Fall 2 Fundamentals of Data Storage, Cleaning, and Retrieval 3
Spring 1 Fundamentals of Programming in Python 3
Spring 2 Qualitative Methods 3
Summer 1 Quantitative Reporting and Modeling 3
Fall 1 Predictive and Proscriptive Modeling 3
Fall 2 Advanced Data Analytics 3
Spring 1 Practicum in Applied Analytics, Data Communication, & Ethics 3
Total program credits   30
     

Course Descriptions

Foundations of Data Analytics (3 Credits): This course introduces fundamental techniques of data analysis, emphasizing complex and/or large data sets. Students will be introduced to common software and will begin to connect analysis technique with decision-making processes.

Upon completion of this course students will have the ability to apply data and analytic principles to real-world problems, understand the data analytics lifecycle from problem definition to solution development, and will be able to articulate how to stitch together analytics, visualization and methodologies to unlock value.

Data Mining (3 Credits): This course is an extension of Foundations of Data Analytics. Focusing on techniques of data mining, students will continue to develop their skills using SAS to learn SQL coding and use SQL Server and/or Oracle to implement algorithms for basic data mining techniques.  Students will learn to prepare data, address classification, performance evaluation and clustering among other practices. (This should be the fourth, not third course in the program.

Upon completion of this course they will be able to find patterns in databases, perform prediction and forecasting with the data, and know how data algorithms work together, as well as experience in the extraction of data insights.

Statistical Methods for Data Science and Analytics (3 Credits): This course introduces fundamental techniques of data analysis, emphasizing complex and/or large data sets. Students will be introduced to common software including SAS and will begin to connect analysis technique with decision-making process, visualization, and advanced data mining.

Upon completion of this course students will understand how to apply their understanding of computational statistics to real-world problems.

Fundamentals of Data Storage, Cleaning, and Retrieval (3 Credits): This course focuses on the process of collecting data and preparing it for analysis.  Students will learn computational processes to automatically correct errors in large data sets when possible, identifying shortcomings in these approaches and manual approaches when necessary.  Students will also learn techniques for error detection using readily available data sets.

This course prepares students to identify data patterns, cluster data, text retrieval, and text mining of both structured and unstructured data.

Fundamentals of Programming in Python (3 Credits): Using the Python language, students will learn concepts around problem solving and algorithm creation, data types and expressions.  Python will be presented as both an interpreted and compiled language to work with a variety of data types and to manage data.

With the completion of this course students will understand the fundamental programming concepts that include data structures, networked application program interfaces and databases using the Python programming language

Qualitative Methods (3 Credits):  This course is an introduction to qualitative research.   Students will learn the basic foundations of qualitative research methodology and be introduced to key research strategies in qualitative research and research design with the goal of equipping participants with the skills to be able to sensitively and critically design, carry out, report, read, and evaluate the quality of qualitative research projects. Specifically, students will learn the basics of questionnaire design, data collection methods, sampling design, dealing with missing values, making estimates, combining data from different sources, and the analysis of survey data.

With the completion of this course students can formulate qualitative research questions and collect and analyze qualitative data. Students will be exposed to different styles of presenting qualitative research results and will consider different ways in which qualitative data is used in practice.

Quantitative Reporting and Modeling (3 Credits): In this course, students will learn to engage with large data sets to gain insights into business operations.  The course covers managerial, strategic and technical issues. Students will learn to deploy and capitalize on business intelligence and analytics solutions.  Students will learn to focus on KPIs and other models of metrics using dashboards and report cards to communicate insights and progress.

In this course students will learn how to translate data into actionable insights by providing the tools and techniques that can be used for making decisions.  The emphasis will be on application and interpretation of results.

Predictive and Prescriptive Modeling (3 Credits): This advanced statistical modeling course is designed to help students uncover patterns in data, determine variables that have predictive capacity, and develop robust models for predicting business and operational trends. Topics include regression analysis and best practices for selecting and building predictive models.

In this course student will how data analytics informs strategy.  In doing so the students will gain hands-on experience with data and economic models to optimally utilize information in decision-making.

Advanced Data Analytics. (3 Credits):  This course addresses the theory and application of advanced econometric methods, neural networks, deep learning, and machine learning.  Students will analyze their own data and interpret reports from other settings.

In this course students will understand the basics of deep learning and their applications in various AI tasks.  Students will have experience using computational intelligence and machine learning methods to solve complex analytic problems and develop decision support systems.

Practicum in Data Analytics (1-3 Credits): The practicum course is designed to help students transition their knowledge and skills from the classroom to their professional setting.  Assignments parallel and support core classes in the data analytics program.  Students are required to complete 3 credit hours for the MS in Data Analytics program. This variable credit course can be assigned 1, 2, or 3 credits and can be repeated to complete the 3-credit requirement.

In this course students learn to effectively work in small analytics teams, accelerate the journey from analytics to actions, and deliver an end to end an industry specific project