| Academic Discipline: |
Consulting |
Operations & |
Data & Business Analytics | Digital Transformation |
| Operations Management |
DSO 547 |
GSBA 504B DSO 505 DSO 506 DSO 520 DSO 547 DSO 549 DSO 565 DSO 570 DSO 580 DSO 581 DSO 582 DSO 583 DSO 586 DSO 588 |
DSO 547 DSO 570 |
DSO 547 DSO 570 DSO 580 |
| Statistics/ Data Science |
DSO 516 DSO 528 DSO 545 |
DSO 522 DSO 536 |
DSO 516, DSO 522 DSO 528, DSO 529 DSO 530, DSO 536 DSO 545, DSO 559 DSO 560, DSO 562 DSO 566, DSO 569 DSO 573, DSO 578 DSO 579 |
DSO 528 DSO 573 |
| Information Systems/ Digital Innovation |
DSO 510 DSO 554 DSO 574 |
DSO 574 | DSO 510 DSO 552 DSO 553 DSO 574 |
DSO 510 DSO 531 DSO 551 DSO 554 DSO 556 DSO 574 |
Software taught and/or used:
Python: DSO 522 (Spring), DSO 545, DSO 553, DSO 559, DSO 560, DSO 562, DSO 569, DSO 574
R: DSO 545, DSO 530, DSO 522, DSO 569, DSO 578, DSO 579
Tableau: DSO 510, DSO 549, DSO 565, DSO 578, DSO 579, DSO 583,
SAS/JMP: DSO 510, DSO 528, DSO 529
Consulting Projects included in DSO 549. DSO 574, DSO 583, DSO 586, GSBA 554
Tools taught and/or used:
Excel-Based Software (@Risk, Crystal Ball): DSO 516, DSO 529, DSO 536, DSO 547, DSO 549, DSO 570, DSO 578, DSO 579, DSO 580, DSO 581, DSO 588
Excel Solver: DSO 520, DSO 547, DSO 565, DSO 570, DSO 580, DSO 581, DSO 582
MS Project: DSO 580
Data Sciences and Operations (DSO) Course Descriptions
The number in parentheses ( ) following each course title refers to the number of units the course is or may be worth.
| 505 | Sustainable Supply Chains (1.5) Sustainability concepts and frameworks, design for environment, closed-loop supply chains, sustainability in sourcing, green facilities, renewable energy, facility location and transportation decisions, strategic sustainability implementation. | |
| 506 | Sourcing and Supplier Management (1.5) Factors to consider when making sourcing decisions (costs, prices, ethics, globalization). Impact of sourcing on other activities such as product design or inventory management. | |
| 510 | Business Analytics (1.5 or 3) Foundational knowledge for business analytics, including strategies, methods, and tools integrated with hands-on skills for defining business analytics for data-driven decision making and innovation. | |
| 516 | Probability and Data Modeling (1.5) Principles of probability methodology. Application for providing structure to uncertainty. Develop, implement, and use probability models. | |
| 520 | Logistics Management (3) Gives students a managerial knowledge of basic logistics concepts and principles. Some topics include management of logistics cost integration, transportation, distribution, and customer service. | |
| 522 | Applied Time Series Analysis for Forecasting (1.5 or 3) Survey of forecasting and time series methods. Models for stationary and nonstationary time series; ARIMA model identification, estimation, and forecast development. Seasonal and dynamic models. Recommended preparation: GSBA 506ab or GSBA 524 or (GSBA 516 and GSBA 545) or other MBA-level statistics or analytics course. | |
| 528 | Blended Data Business Analytics for Efficient Decisions (3) Build Analytical Models for Classification, Clustering and Association Problems. Leverage third party "Big Data" for enriching and monetizing data. Develop data mining and business analysis. | |
| 529 | Advanced Regression Analysis (3) Computer-assisted analysis of business data; advanced multiple regression analysis, survey analysis, ANOVA testing for Marketing-type applications and Times Series Analysis methods will be covered. Prerequisites: GSBA 506b or GSBA 524 or GSBA 545 or other MBA-level statistics or analytics course. | |
| 530 | Applied Modern Statistical Learning Methods (3) Overview of highly computational modern statistical learning methods; applications of logistic regression, neural networks, LASSO, trees, boosting and GAM, etc., to finance and marketing data. Prerequisite: DSO 545. | |
| 531 | Digital Foundations for Business Innovation (1.5) Developing a strategic perspective on emerging digital innovations shaping consumer-oriented businesses. Topics include artificial intelligence, autonomous vehicles, augmented/virtual reality, post-screen usability and cybersecurity. | |
| 534 | Discrete-Event Simulation for Process Management (1.5 ) Application of discrete-event simulation models to events that occur randomly over time. Representation using process flow diagrams. Use of simulation methodology to improve process performance. Corequisite: DSO 516. (Duplicates credit in the former DSO 532.) | |
| 536 | Monte Carlo Simulation and Decision Models (1.5) Application of Monte Carlo simulation to determine a range of outcomes for all possible courses of action. Application of Excel simulation. | |
| 545 | Statistical Computing and Data Visualization (3) Data cleaning and reshaping; good vs. bad graphics; univariate, bivariate, trivariate, hypervariate, and time series graphics; interactive graphics; web-related computing. Extensive computer applications using R. | |
| 547 | Designing Spreadsheet-Based Business Models (3) Application of decision analysis, simulation and optimization techniques to managerial problems. Students learn how to create and present useful spreadsheet models to analyze practical business models. Recommended preparation: completion of required M.B.A. course work. | |
| 548 | Emerging Technologies in Supply Chain Management (3) Insights into the emerging technologies of artificial intelligence, machine learning, Blockchain and the theoretical difficulties and implementation challenges of these technologies. | |
| 549 | Application of Lean Six Sigma (3) Application of Six Sigma practices and techniques to improve operations in organizations. Duplicates credit in ISE 507. | |
| 551 | Digital Transformation in the Global Enterprise (3) Leveraging large enterprise system applications for strategic value; managing organizational transformation of global enterprises through digital business platforms; coping with disruptive technologies. | |
| 552 | SQL Databases for Business Analysts (1.5) SQL; relational database systems; data storage; data manipulation; data aggregation. | |
| 553 | NoSQL Databases in Big Data (1.5) NoSQL; semi-structured and unstructured databases; data storage; data manipulation; distributed databases. Prerequisite: DSO 552. | |
| 554 | Digital Strategies for Sustainability in Global Markets (3) Designing and executing business strategies for sustainability (environmental, economic, social/cultural) enabled by digital technologies. Global market contexts; team consulting project; international travel. | |
| 556 | Business Models for Digital Platforms (3) Managing business models in digital platform ecosystems; designing new products and services for digital platforms; establishing digital platform leadership; assessing emerging niches in digital spaces. | |
| 559 | Introduction to Python for Business Analytics (3) Python programming for descriptive data analytics and technical tools for business applications. Solving business problems and formulating actionable business recommendations including their limitations. | |
| 560 | Text Analytics and Natural Language Processing (1.5) Acquire, analyze, visualize and perform natural language processing (NLP) on text data. Apply Python, machine learning packages, statistical methodology and computer code to business decision-making. Prerequisite: DSO 545; Corequisite: DSO 530 | |
| 562 | Fraud Analytics (3) Fraud detection model systems; identify normal vs. outlying behavior; malicious adversaries; complex data sets; supervised and unsupervised fraud statistical models; measures of model efficacy. Prerequisite: DSO 545; Corequisite: DSO 530; Recommended Preparation: Proficiency in Python programming language and machine learning algorithms | |
| 565 | Supply Chain Analytics (3) Analytics for supply chain planning. Data-driven decision making, solving real-world problems, utilizing scalable technology, current industry best practices and inventory/network optimization. | |
| 566 | Enroll in MKT 566 Decision Making Using Marketing Analytics (3) Applications and models of marketing-related data analyses to the development of data-driven marketing strategies and making data-driven marketing decisions. | |
| 569 | Deep Learning for Business Applications (1.5) Apply machine learning tools to business. Write code to solve complex pattern recognition. Build strategies, technical planning, research and analyze data. Present complex technical data. Prerequisite: DSO 545. Corequisite: DSO 530. | |
| 570 | The Analytics Edge: Data, Models, and Effective Decisions (3) Decision making under uncertainty using real data applying the most advanced optimization, statistical and probability methods. | |
| 572 | Strategies for Digital Analytics (1.5) Foundation in digital analytics in tandem with digital strategy and solutions through a design thinking approach to working with digital and web data. Recommended Preparation: DSO 545. | |
| 573 | Data Analytics Driven Dynamic Strategy and Execution (3) Achieving and enhancing competitive advantage through applications of data analytics, continuous insight discovery, strategy formulation and execution for the next generation of corporate leaders. | |
| 574 | Using Big Data: Challenges and Opportunities (3) How companies can implement 'big data' initiatives to improve business activities. How leading companies have successfully implemented 'big data' initiatives and why some have failed. | |
| 578 | Fundamentals of Sports Performance Analytics (1.5) Statistical models for pro sports industry business application. Effectively communicate findings for practical actionable results. Sports science data protocol. | |
| 579 | Advanced Sports Performance Analytics (1.5) Implement supervised and unsupervised machine learning models to a specific sports performance analytics scenario. Help assess and predict performance. | |
| 580 | Project Management (3) Applications of systems theory and concepts, matrix organizational structures, PERT/CPM project modeling, and management information systems to the management of complex and critical projects. Recommended preparation: GSBA 504b or GSBA 534 or MBA-level Operations Management course. | |
| 581 | Supply Chain Management (3) Issues in supply chain management. Supply chain performance and dynamics. Tools for planning, control and coordination. Supply chain design and strategy. Recommended preparation: GSBA 504b or GSBA 534 or MBA-level Operations Management course. | |
| 582 | Service Management: Economics and Operations (3) Examination of the service industry from a managerial and entrepreneurial perspective; emphasis on the tactical decisions needed to design and deliver successful and profitable services. Recommended preparation: GSBA 504b or GSBA 534 or MBA-level Operations Management course. | |
| 583 | Operations Consulting (3) Development of conceptual and analytic skill for improving operations. Analysis of business strategy, formulating and implementing operations strategy, process analysis and design, and project management. Recommended preparation: GSBA 504b or GSBA 534 or MBA-level Operations Management course. | |
| 586 | Global Healthcare Operations Management (3) Application of operations management tools and techniques to improve the performance of healthcare delivery systems. May include international travel. | |
| 588 | Supply Chain Finance (3) Combines finance and supply chain management. Assess financial opportunities, finance fragmentation, challenges, optimizing working capital and managing risk in supply chain finance. | |
| 592 | Field Research in Data Sciences or Operations (.5-4, repeatable subject to approval by student's program director) Individual or team projects studying the business practices of an industry, company, government agency, country, geographic region, etc. Proposal, data collection, analyses, and written report. Recommended preparation: completion of required M.B.A.,MSGSCM, MS-BUAN, M.Acc., or M.B.T. course work. Visit the Independent Study page of this site for instructions and the application. Graded CR/NC. | |
| 593 | Independent Research in Data Sciences or Operations (.5-4 repeatable subject to approval by student's program director) Individual research beyond normal course offerings. Proposal, research and written report/paper required. Recommended preparation: completion of required M.B.A., MSGSCM, MS-BUAN, M.Acc., or M.B.T. course work. Visit the Independent Study page of this site for instructions and the application. Graded CR/NC. | |
| 595 | Internship in Data Sciences or Operations (.5-2 repeatable; units credited toward degree usually limited to 1) Supervised on-the-job business experience in the student's area of interest. (Curricular Practical Training) Recommended preparation: completion of required M.B.A., MSGSCM, MS-BUAN, M.Acc., or M.B.T. course work. Visit the Independent Study page of this site for instructions and the application. Graded CR/NC. | |
| 596 | Research Practicum in Data Sciences or Operations (.5-2 repeatable subject to approval by student's program director) Hands-on practical experience working with a Marshall faculty member from the Information and Operations Management department on an ongoing research project. Recommended preparation: completion of required M.B.A., MSGSCM, MS-BUAN, M.Acc., or M.B.T. course work. Visit the Independent Study page of this site for instructions and the application. Graded CR/NC. | |
| 597 | Consulting Project in Data Sciences or Operations (.5-5 repeatable subject to approval by student's program director) Individual or team projects solving real business problems for an existing business entity, domestic and/or international. Proposal, field research, analyses and oral and written presentations. Recommended preparation: completion of required M.B.A., MSGSCM, MS-BUAN, M.Acc., or M.B.T. course work. Visit the Independent Study page of this site for instructions and the application. Graded CR/NC | |
| 598 | Special Topics (1, 1.5, 2, or 3, max 9) Selected topics reflecting current trends and recent developments in operations management, information systems, and decision support systems. Visit the Independent Study page of this site for instructions and the application. Graded CR/NCr. | |
| 599 |
Special Topics (1, 1.5, 2, or 3, max 9) Selected topics reflecting current trends and recent developments in operations management, information systems, and decision support systems. Letter graded. [Courses offered on an trial basis prior to conversion into permanent courses.]
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Not all courses are offered every semester. To view class schedules, please visit the USC Schedule of Classes at www.usc.edu/soc.