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Comprehensive Course Structure for Business Analytics Program

The following table outlines the complete course structure across all eight semesters of the Business Analytics program at Alpine College Of Management And Technology. It includes core courses, departmental electives, science electives, and laboratory components with their respective credit structures and prerequisites.

Detailed Departmental Elective Courses

The department offers a range of advanced elective courses that allow students to specialize in specific areas of business analytics. These courses are designed to provide in-depth knowledge and practical skills relevant to current industry trends and challenges.

Machine Learning Fundamentals

This course provides a comprehensive introduction to machine learning algorithms, including supervised and unsupervised learning techniques. Students learn to implement algorithms using Python and R, evaluate model performance, and apply them to real-world datasets. The curriculum covers linear regression, decision trees, clustering, classification, and ensemble methods. Practical applications include predictive modeling for business scenarios, recommendation systems, and anomaly detection.

Predictive Modeling

Students explore advanced statistical modeling techniques for forecasting future trends and behaviors based on historical data. The course covers time series analysis, regression models, and probabilistic graphical models. Emphasis is placed on building robust predictive models using tools like SAS, SPSS, and Python libraries. Case studies involve financial forecasting, demand prediction, and customer lifetime value estimation.

Data Mining

This course delves into techniques for extracting meaningful patterns and knowledge from large datasets. Topics include association rule mining, frequent pattern discovery, classification, clustering, and anomaly detection. Students gain hands-on experience with tools like Weka, KNIME, and Apache Spark. Applications include market basket analysis, fraud detection, and customer segmentation.

Optimization Techniques

The course introduces mathematical optimization methods used in business analytics, including linear programming, integer programming, and nonlinear optimization. Students learn to formulate real-world problems as optimization models and solve them using software tools like Gurobi, CPLEX, and MATLAB. Applications include resource allocation, scheduling, and logistics optimization.

Advanced Statistical Methods

This course covers advanced statistical techniques used in business analytics, including multivariate analysis, survival analysis, Bayesian inference, and stochastic processes. Students learn to apply these methods to analyze complex datasets and make informed decisions under uncertainty. The curriculum emphasizes practical implementation using R and Python.

Financial Risk Management

Focused on quantitative methods for assessing and managing financial risks, this course covers value at risk (VaR), credit risk modeling, operational risk assessment, and derivatives pricing. Students gain expertise in risk metrics, portfolio optimization, and regulatory compliance frameworks. Practical exercises involve stress testing, scenario analysis, and risk reporting.

Customer Analytics

This course explores data-driven approaches to understanding customer behavior and optimizing marketing strategies. Topics include customer segmentation, churn prediction, loyalty analysis, and recommendation systems. Students learn to build predictive models for customer lifetime value and implement targeted marketing campaigns using analytics tools.

Supply Chain Optimization

The course addresses logistics optimization, inventory management, demand forecasting, and supply chain resilience. Students learn to model supply chain networks, optimize distribution strategies, and manage disruptions. Tools like simulation software and optimization solvers are used to solve complex supply chain problems.

Healthcare Data Analytics

This specialized course applies analytics techniques to healthcare data for improving patient outcomes and operational efficiency. Topics include electronic health records analysis, disease prediction models, public health surveillance, and healthcare policy evaluation. Students work with real-world datasets from hospitals and health organizations.

Operations Research Applications

Students learn to apply mathematical modeling and optimization techniques to solve complex business problems in operations research. The course covers queuing theory, network flows, integer programming, and simulation methods. Real-world applications include manufacturing planning, service system design, and resource allocation.

Big Data Analytics

This course introduces students to processing and analyzing large-scale datasets using distributed computing frameworks like Hadoop and Spark. Topics include data warehousing, streaming analytics, real-time processing, and scalable machine learning algorithms. Students gain hands-on experience with cloud-based platforms and big data tools.

Deep Learning and Neural Networks

The course provides a deep dive into neural network architectures, including convolutional networks, recurrent networks, and transformers. Students learn to implement deep learning models using TensorFlow and PyTorch frameworks. Applications include image recognition, natural language processing, and generative modeling.

Geospatial Analytics

This course integrates spatial data analysis with business applications, covering geographic information systems (GIS), spatial statistics, location-based services, and environmental monitoring. Students learn to analyze geospatial data using tools like QGIS, ArcGIS, and Python libraries for spatial analysis.

Privacy and Data Governance

The course addresses legal and ethical aspects of data usage, including privacy regulations (GDPR, CCPA), compliance requirements, and data governance frameworks. Students learn to design secure and compliant analytics solutions while respecting individual privacy rights and organizational policies.

Project-Based Learning Philosophy

The department's approach to project-based learning is rooted in the belief that real-world application of theoretical knowledge enhances understanding and prepares students for professional success. Projects are carefully designed to mirror industry challenges, allowing students to apply their skills in meaningful contexts.

Mini-projects are introduced from the second year onwards, providing students with early exposure to practical problem-solving. These projects typically last 2-3 weeks and involve small teams working on specific tasks under faculty supervision. They focus on developing technical skills, teamwork, and communication abilities.

The final-year thesis/capstone project represents the culmination of the student's learning journey. It is a substantial, original research or application-based project that demonstrates mastery in their chosen specialization area. Students select projects in consultation with faculty mentors, ensuring alignment with academic rigor and industry relevance.

Evaluation criteria for projects include technical depth, innovation, presentation quality, teamwork, and impact on business outcomes. Regular progress reviews ensure students stay on track and receive timely feedback. Faculty mentors provide guidance throughout the project lifecycle, from initial concept development to final implementation and documentation.

The department also encourages collaborative projects with industry partners, giving students opportunities to work on real-world challenges and gain exposure to professional environments. These partnerships enhance the relevance of educational experiences and strengthen connections with potential employers.