Analytics

Program Description

The M.S. in Analytics provides students with the background needed to delve deeper into review and questioning of data, internal and external related to their specific industry and professional needs.  The program mixes statistical analysis with data preparation to provide visual results to questions.  The program may be adapted to the industries needed for the student’s professional growth.

If you have any questions regarding the Analytics program, please contact:

Margaret McCoey, M.S.
Director
(215) 951-1136
mccoey@lasalle.edu
www.lasalle.edu/analytics

If you have any questions regarding the Analytics program, please contact: analytics@lasalle.edu

Mission

The graduate program in M.S. Analytics educates students in theoretical and practical knowledge of data analytics.  The program develops professional competencies in analytics which may be applied to various industries.  The faculty, staff and students engage in relationships with industry practitioners to encourage excellence and provide attention to ethical principles.

Program Goals

The learning goals of this proposed program are the following:

  • Prepare students to participate ethically and professionally in analytics professions.
  • Prepare students to enter the field of analytics.
  • Prepare students and faculty to be leaders in analytics.

Student Learning Outcomes

  1. Define and explain differences between descriptive, predictive and prescriptive analytics.
  2. Construct and transform relevant views of data sources based on independent variables.
  3. Use statistical methods and develop models for data sources.
  4. Construct data simulations based on data models.
  5. Generate visual data solutions.

Program Specific Information

 

Academic Requirements

Students are required to complete 10 courses (30 credits) for this program.  This includes a capstone course (3 credits)

ANA 601 - Overview of Analytics
ANA 613 - Statistics for Data Analytics
ANA 615 - Optimization Methods for Data Analytics
ANA 617 - Modeling and Simulation for Data Analytics
ANA 523 - Database Management Systems
ANA 658 - Data Mining 
ANA 624 - Data Warehouse 
ANA 652 - Leadership Assessment and Evaluation
ANA 665 - Data Visualization
ANA 880 - Analytics Capstone 

Course Sequence

Tentative Schedule

Fall 1

ANA 601

ANA 523

Spring 2

ANA 613

ANA 615

Summer 1

ANA 658

ANA 652

Fall 2

ANA 624

ANA 617

Spring 2

ANA 665

ANA 880

 

Degree or Certificate Earned

M.S.

Number of Courses Required for Program Completion

10

Number of Credits Required for Program Completion

30

GPA Required for Program Completion

3.0

Program Contact Information

Margaret McCoey, M.S.
Director
(215) 951-1136
mccoey@lasalle.edu
www.lasalle.edu/analytics

Staff Contact Information

M.S.  Analytics
Holroyd 123
www.lasalle.edu/analytics

Faculty

Program Director: Margaret McCoey, M.S.
Associate Professors: DiDio, Fierson, Highley. Redmond
Assistant Professors: McCoey, Wang
Lecturers: Crossen, Parker, McGinley, Smith

Course Descriptions

ANA 523 - Database Management Systems

This course entails analysis and evaluation of database designs in relation to the strategic mission of the project. Topics include database systems, database architectures, and data-definition and data-manipulation languages. Also included are logical and physical database design, database models (e.g., entity-relationship, relational), normalization, integrity, query languages including SQL. The course will address the use of Cloud Storage, non-structured data, the use of NOSQL databases. The course discusses social and ethical considerations and privacy of data. This course incorporates case studies for real project implementations.

Number of Credits: 3

ANA 601 - Overview of Analytics

This course introduces the student to the foundational principles, terminology, history and types of analytics used in industry. Students will learn how to define requirements and identify challenges, examine design strategies, explore approaches to analyzing data and identify appropriate data visualization tool(s). Students will explore trends, uncover ethnical challenges presented during data analysis and collection using case studies, problem scenarios and team projects. Topics include understanding your client & their need/use for data, analytic trends, and examples of using data to illustrate a picture for your client.

Number of Credits: 3

ANA 613 - Statistics for Data Analytics

An introduction to the essential principles of descriptive and inferential statistics needed for effective data analysis and decision making. Applications and case studies using realistic data will be used to demonstrate how statistical methodology is used to generate predictions necessary for decisions via data collection, statistical analysis and interpretation. Topics include applied probability, probability distributions, sampling, estimation, confidence intervals, hypothesis testing, linear and multiple regression, analysis of variance, and model building. Technology, including spreadsheets and dedicated statistical software, will be employed where appropriate.

Number of Credits: 3

ANA 615 - Optimization Methods for Data Analytics

This course introduces students to mathematical models that can be employed to make informed decisions in a wide variety of data-driven fields, including (but not limited to) finance, banking, marketing, health care, retail, manufacturing, and transportation. Goals such as increasing revenue, decreasing costs, and improving overall efficiency of operations in the face of various constraints are considered. Students learn to recognize when a problem lends itself to a particular type of model, formulate the model, and use appropriate methods to solve or extract information from the model. Particular emphasis is placed on linear programming (with exposure to network models and integer programs) and the simplex method. Forecasting, inventory management, and queueing models, as well as Markov chains, are also studied. Additional topics covered include sensitivity analysis, duality, decision analysis, and dynamic programming. Software (both spreadsheets and a computer algebra system) is employed consistently throughout the course to expedite the solution and analysis process; emphasis will be placed on the practical application of models rather than on the models' mathematical properties.

Number of Credits: 3

Prerequisites: ANA 613

ANA 617 - Modeling and Simulation for Data Analytics

This course introduces students to modeling and simulation. Topics include basic queueing theory, the role of random numbers in simulations, and the identification of input probability distributions. Students will also learn to identify limitations of simulations and draw correct conclusions from a simulation study. Students will work with specialized simulation packages.

Number of Credits: 3

Prerequisites: ANA 615

ANA 624 - Data Warehousing

This course covers the use of large-scale data stores to support decision making; critical success factors in designing and implementing a data warehouse and management of a data warehouse project; requirements analysis; design using the star schema; entire data warehouse integration; infrastructure needs; data staging process, including data cleansing and transformation; and data access, including On-line Analytic Processing (OLAP) tools and Big Data. Also considered are introduction to data mining and analysis, evaluation, and selection of data warehousing tools, techniques, and methodologies.

Number of Credits: 3

Prerequisites: ANA 523

ANA 652 - Leadership Assessment and Evaluation

This experiential course emphasizes the importance of feedback and self-assessment for leadership development. It includes extensive assessment of each participant’s management style and skills based on self-evaluations (using structured questionnaires) and feedback from coworkers, faculty, and other participants. Leadership development experiences emphasize time and stress management, individual and group problem solving, communication, power and influence, motivation, conflict management, empowerment, and team leadership. Each participant identifies skills he or she needs to develop and reports on efforts to develop those skills.

Number of Credits: 3

ANA 658 - Data Mining

This course introduces the field of data mining, with specific emphasis on its use for Machine Learning algorithms. Techniques covered may include conceptual clustering, learning decision rules and decision trees, case-based reasoning, Bayesian analysis, neural networks and text mining. The course covers data preparation and analysis of results. Skills in Microsoft Excel are useful.

Number of Credits: 3

Prerequisites: ANA 523

ANA 665 - Data Visualization

This course develops data visualization techniques to provide effective display and presentation of analytical solutions in organizational contexts. The course topics include analytical reasoning, human perception of visual information, visual representation and interaction technologies, data representation and dissemination using texts, graphics, images, sounds. Students will learn research trends in space, time, multivariate analytics and extreme scale visual analytics.

Number of Credits: 3

Prerequisites: ANA 617

ANA 880 - Analytics Capstone

The capstone is an opportunity to pursue an independent learning experience focused on a specific aspect of Analytics. Students choose from an advanced research topic focused on analytics, a professional application of analytics to a specific case or an experiential learning opportunity focusing on the application of analytics. The capstone extends students beyond the course work and cases to apply knowledge to situations relevant to their professional goals. Each student will be required to present his/her capstone as both an oral presentation and a summary written document.

Number of Credits: 3