Lawrence Technological University

What you'll study

The program is built around a set of core courses (each listed with course code and credit value in the source):

• Statistics for Data Analytics and Visualization (MBA6103, 3 credits) — introduces core data-analytics concepts, clarifies distinctions among statistical analysis, data mining, business intelligence and data science, and surveys relevant tools and languages.
• Management Information Systems (INT6043, 3 credits) — examines strategic and operational uses of information systems, managerial issues in development and implementation, and socio-technical approaches via case studies.
• Python for Data Analysis and Visualization (INT6103, 3 credits) — hands-on introduction to Python for data tasks (programming fundamentals, data manipulation, visualization) culminating in a team project solving business problems.
• Database Management Systems (INT6113, 3 credits) — covers relational DBMS principles, logical and physical modeling, normalization, query languages, transaction processing and hands-on database design and implementation.
• Introduction to Machine Learning (INT6203, 3 credits) — a programming-focused introduction to supervised and unsupervised methods, neural networks, deep learning and reinforcement learning, with a practical project.
• Business Analytics and Intelligence (INT7213, 3 credits) — focuses on the BI/BA lifecycle: opportunity definition, dimensional modeling, ETL, data warehousing and deploying BI solutions to support decision making.
• Introduction to Social Media Data Analytics (INT6303, 3 credits) — uses R and social media APIs for analyses such as sentiment analysis while critically assessing limits and benefits of such approaches.
• Visual Analytics (INT7253, 3 credits) — teaches design, cognition and HCI principles for interactive visualization techniques aimed at business users, with group work applying visualization methods to real problems.
• Data Science for Business — emphasizes theoretical foundations and practical techniques for working with massive business datasets, covering statistical modeling, predictive modeling, text and web mining, and social network analytics in real-world case studies.

Entry requirements

The program page references admissions requirements but does not list specific criteria in the source text. Prospective applicants should consult the university admissions office or program contact for exact application prerequisites and documentation.

Career prospects

The curriculum targets the growing demand for professionals who can turn large-scale data into actionable business insight. Graduates are prepared to work across industries where critical thinking and analytical skills are needed to define problems, build analytic solutions and support data-driven decision making.

Scholarships & funding angle

The source does not specify scholarships for this program. Applicants should explore university-wide financial aid, departmental awards, employer tuition benefits and external scholarships for graduate study in analytics or data science.