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

  • Define and explain differences between descriptive, predictive and prescriptive analytics.
  • Formulate business practices related to specific industries and ethical behavior.
  • Describe statistical methods related to data collection.
  • Construct relevant views of data sources based on independent variables.
  • Develop models for data sources.
  • Construct data simulations based on data models.
  • Transform data sources for data analysis.
  • Generate data trends based on data sources.
  • Integrate data sources into historical repositories.
  • Generate visual data solutions.
  • Formulate problem and solutions to applied analytics problems.

Admission Requirements

To be accepted for admission into the program, a candidate must:

  1. Complete the application for Admission which may be obtained at https://www.lasalle.edu/grad/apply/
  2. Provide evidence of successful academic achievement in the completion of a bachelor’s degree from an accredited institution of higher education. Candidates must have an undergraduate GPA of at least 3.0.
  3. Request official transcripts from the insitutions of higher education showing all undergraduate and previous graduate study (if applicable). For work completed outside of the US, the transcripts need to be evaluated by World Education Services (www.wes.org).
  4. Provide a professional resume.
  5. Request two letters of recommendation from professors or current or past supervisors at his/her place of professional employment.
  6. Attend an interview with a faculty member to assess the candidate’s requirements.

This program is offered online. International students may take the program at their home, but are not permitted to receive US Student VISAs because of the delivery format.

Students should be comfortable with the use of basic spreadsheet tools. Students need to be inquisitive. Students with professional experience in a corporate setting would be the best suited to enter this program.

Please refer to the University’s Nondiscrimination Policy in the General Reference section of this catalog. Admission is based soley upon applicant’s qualifications.

All documents should be sent to the following:

Office of Graduate Enrollment
La Salle University – Box 826
1900 W. Olney Avenue
Philadelphia, PA 19141

215.951.1100 / Fax 215.951.1462
grad@lasalle.edu

Curriculum

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 

Tuition and Fees

Tuition and fees for the current year are provided in the General Reference section of this catalog.

Tuition Assistance

Part-time students can apply for need-based financial aid. For more information on financial aid or to apply for a Federal Stafford Loan and the Additional Unsubsidized Loan Program, please contact the Student Financial Services office at 215.951.1070.

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. Prerequisite: ANA 523

Number of Credits: 3

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