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Department of Supply Chain Management & Analytics Proposed Bachelor of Science (B.S.) in Business - Analytics Track Paolo Catasti, PhD, MBA, CSSBB Teaching Assistant Professor Statistics and Analytics Top Analytics Employers in the Greater Richmond Area SCMA Major - Analytics Track Definition • Intended for those students who want to develop and sharpen their analytics skills, and are interested in pursuing a career in the fastgrowing fields of business analytics and decision sciences. • The program provides students with the knowledge, tools, and skills needed to help them make sound business decisions that improve upon the performance of organizations. SCMA Major - Analytics Track Objectives • Upon completion of the SCMA Major - Analytics Track, the student will have learned how to: 1. Manage an analytic project from concept design to communication of insights 2. Acquire data from various sources, manipulate data, and transfer data between different environments 3. Develop strategies and problem-solving skills that help manage business uncertainty and risk 4. Apply the different methodologies required to make sound analytic decisions 5. Communicate results through advanced visualization techniques SCMA Major - Analytics Track Advanced Business Program SCMA 350 – Intro to Project Management SCMA 302 Business Statistics SCMA 310 Data Management Acquire, manipulate and transfer data SCMA 303 Introduction to Analytics SCMA 410 Data Visualization Communicate results Manage business uncertainty and risk Make sound analytic decisions Manage an analytic project from concept design to communication of insights SCMA Major - Analytics Track Program Requirements • The Advanced Business Program – Analytics Track would include the following courses: • Advanced Business Core (18 credits): • SCMA 310 Data Management • SCMA 302 Business Statistics II • SCMA 303 Introduction to Analytics • SCMA 350 Introduction to Project Management • SCMA 386 Global Supply Chain Management • SCMA 410 Data Visualization • Three Advanced Business Electives (9 credits), one of which from SCMA Notes: In Red are shown the courses currently under development. In Blue are shown the courses that have recently been introduced/updated. SCMA Major - Analytics Track Proposed List of Advanced Business Electives One course (3 credit) from: • SCMA 339 Quantitative Solutions for Management • SCMA 440 Data Mining and Forecasting • SCMA 441 Prescriptive Analytics Two courses (6 credit) from: • ACCT 408 Accounting Decision Analysis • BUSN 400 & 401 Principles of Consulting & International Consulting Practicum • ECON 403 Introduction to Mathematical Economics • FIRE 312 Financial Modeling • INFO 320 Data Mining and Business Intelligence • INFO 361 Systems Analysis and Design • INFO 491 Big Data Analytics • MKTG 310 Information for Marketing Decisions New Courses Emphasis – Critical Thinking Definition • The Analytics Track will help the student refine Critical Thinking skills through the development of the following core competencies: 1. Active thinking: the ability to recognize the most efficient path to the correct solution to a project or task. 2. Pattern recognition: the ability to identify the correct approach to a problem through a framework built from experience. 3. Paraphrasing: the ability to synthesize a complex word problem into a model or algorithm. 4. Attention to detail: the ability to thoroughly complete tasks and provide accurate reports, and to efficiently troubleshoot errors in quantitative analyses. New Courses Emphasis – Critical Thinking framework • Critical Thinking framework: o Definition of issue/problem. o Recognition of issue/problem’s thesis/hypothesis. o Validation of assumptions and conditions. o Data analysis. o Conclusions and related outcomes. New Courses Emphasis – Team Projects • Courses focus on the communication & presentation of business insight to an audience of peers. • In the later part of each half of the course, students will assemble in teams of about four, and work on a case that is designed to evaluate knowledge and understanding of the course concepts. • Team project deliverables and related weights: o Results and conclusions o Written report o Visual presentation o Oral delivery 25% 25% 25% 25% • Individual case weight: • Teammates will assign a participation weight to each team member, meant to characterize each student’s level of involvement and contribution to the project. Creation of New Courses SCMA 310 Data Management • Course Objective: This course is designed for those undergraduate business students who seek to develop intermediate to advanced spreadsheet modeling skills, and learn the basic elements of database management for data querying, manipulation, and extraction. • Learning Outcomes: The course is divided in two parts: 1. Data wrangling with OpenRefine, and database querying with Microsoft Access & SQL. 2. Intermediate spreadsheet management and modeling with Excel. Creation of New Courses SCMA 310 Data Management • Course Textbook Custom book to be created from bundling together parts of the following materials: o Monk et al., Problem-Solving Cases in Microsoft Access & Excel, 14th edition (2016), Cengage Learning. ISBN-13: 978-1305-86862-5 o Coronel & Morris, Database Systems: Design, Implementation, & Management, 12th Edition (2016), Cengage Learning. ISBN-13: 978-1-305-62748-2 o Albright & Winston, Business Analytics: Data Analysis & Decision Making, 6th Edition (2017), Cengage Learning. ISBN-13: 978-1-133-62960-3 • Prerequisites (with a grade of “C” or above): • Digital Literacy: Spreadsheets Skills I (INFO 162) • Differential Calculus and Optimization (SCMA 212) • Business Statistics I (SCMA 301) Creation of New Courses SCMA 410 Data Visualization • Course Objective: This course is designed for those undergraduate business students who want to develop basic to intermediate data visualization skills, and learn the basic elements of GIS mapping and reporting through dashboards, and the authoring of presentations through the R environment. • Learning Outcomes: The course is divided in two parts: 1. Basic to intermediate data visualization techniques using Excel and Tableau. 2. Use of open source environments such as RStudio and R Presentations to author professional and customized presentations. Creation of New Courses SCMA 410 Data Visualization • Reference Materials: o Albright & Winston, Business Analytics: Data Analysis & Decision Making, 6th Edition (2017), Cengage Learning. ISBN-13: 978-1-133-62960-3 o Peck, Tableau 9: The Official Guide, 2nd Edition (2016), Wiley. ISBN-13: 978-0071-84329-4 o Leemis, Learning Base R, 1st Edition (2015), Lawrence Leemis. ISBN-13: 978-0982-91748-0 • Prerequisites (with a grade of “C” or above): • Business Statistics II (SCMA 302) or equivalent • Introduction to Analytics (SCMA 303) Creation of New Courses SCMA 441 Prescriptive Analytics • Expands on the topics of Optimization, Simulation, and Decision Analysis introduced in SCMA 303 Introduction to Analytics. • A list of desirable topics could be: Optimization: • Linear • Integer/Binary • Non-linear Simulation: • Probability distributions (discrete and continuous) • Risk Analysis • Simulation Optimization Decision Analysis: • Decision Trees • Bayes Theorem • Utility Functions Modification of Existing Courses SCMA 303 Introduction to Analytics • Course layout changes: Proposed Existing Q1 1st Half Q2 Q3 2nd Half Q4 1. 2. 3. 4. 5. 6. 7. 8. Introduction to Data Visualization Spreadsheet Modeling Regression Analysis Time Series & Forecasting Linear Optimization Integer Optimization Simulation Decision Analysis 1. 2. 3. 4. 5. 6. 7. 8. Introduction to Data Visualization Time Series & Forecasting Introduction to Optimization Linear Optimization Integer Optimization Introduction to Simulation Simulation Models Decision Analysis • Pre-requisite changes: Existing Proposed SCMA 212 (or equivalent) SCMA 301 (or equivalent) SCMA 212 (or equivalent) SCMA 301 (or equivalent) SCMA 302 (or equivalent) SCMA 310 Data Management Modification of Existing Courses SCMA 440 Data Mining and Forecasting List of desirable topics: 1st Half - Data Mining: Q1 - Descriptive: Data partitioning Hierarchical clustering K-means clustering Q2 - Predictive: Logistic regression Classification trees 2nd Half - Forecasting: Q3 - Time Series: Moving average Exponential smoothing Holt-Winters Q4 - Forecasting: Autoregressive models Regression-based forecasting Appendix New Course Syllabi SCMA 310 Data Management – Class Schedule (First Half) Class # 1 2 3 4 5 6 7 8 9 10 11 12 13 14 Date 1/17 1/19 1/24 1/26 1/31 2/2 2/7 2/9 2/14 2/16 2/21 Att. 1 1 1 1 1 1 2/23 2/28 3/2 3/7 3/9 3/14 1 1 2 1 1 1 Lesson type Introduction/Learn Practice Learn Practice Learn Practice Quiz Discussion Learn Practice Learn Practice/Learn Practice Quiz No class No class Presentation Subjects Data wrangling Openrefine examples Database design and Microsoft Access Create tables, forms, queries, and reports Design a relational database Relational database design applications Quiz 1 Quiz debrief and midterm case introduction Introduction to Structured Query Language (SQL) Examples of SQL query applications SQL data definition and manipulation commands SQL manipulation commands and SELECT queries Examples of SELECT query applications Quiz 2 Spring Break Spring Break Midterm case study SCMA 310 Data Management – Class Schedule (Second Half) Class # 15 16 17 18 19 20 21 22 23 24 25 26 27 28 Date 3/16 3/21 3/23 3/28 3/30 4/4 4/6 4/11 4/13 4/18 4/20 4/25 4/27 5/2 Att. 1 1 1 1 1 1 1 1 1 1 1 Lesson type Learn Practice Learn Practice Learn Practice Quiz Discussion Learn Practice Learn Practice Quiz Presentation Subjects Import data in Excel and use of functions Spreadsheet management applications Finding relationships among variables Pivot Tables, filtering, and what-if analyses Decision Support System using Scenario Manager Examples of decision support applications Quiz 3 Quiz debrief and final case introduction Visual Basic for Applications (VBA) Examples of macros for spreadsheet management Events, functions, and Application object in VBA Examples of events, functions, and Application object Quiz 4 Final case study SCMA 410 Data Visualization– Class Schedule (First Half) Class # 1 2 3 4 5 6 7 8 9 10 11 12 13 14 Date 1/17 1/19 1/24 1/26 1/31 2/2 2/7 2/9 2/14 2/16 2/21 2/23 2/28 3/2 3/7 3/9 3/14 Att. 1 1 1 1 1 1 2 1 1 1 1 1 Lesson type Introduction/Learn Practice Learn Practice Learn Practice Quiz Discussion Learn Practice Learn Practice/Learn Practice Quiz No class No class Presentation Subjects Introduction to basic chart types in Excel Visualizations of variables and their relationships The Tableau interface Examples of basic chart applications with Tableau Join types, visual analytics, sorting and grouping Visual analytics applications Quiz 1 Quiz debrief and midterm case introduction Table calculations and parameters Calculated field applications GIS mapping Geocoding and layers applications. Dashboards Dashboard and storyboard applications Quiz 2 Spring Break Spring Break Midterm case study SCMA 410 Data Visualization– Class Schedule (Second Half) Class # 15 16 17 18 19 20 21 22 23 24 25 26 27 28 Date 3/16 3/21 3/23 3/28 3/30 4/4 4/6 4/11 4/13 4/18 4/20 4/25 4/27 5/2 Att. 1 1 1 1 1 1 1 1 1 1 1 Lesson type Learn Practice Learn Practice Learn Practice Quiz Discussion Learn Practice Learn Practice Quiz Presentation Subjects Introduction to R and RStudio Examples of data manipulations with R Bar charts and histograms with Rstudio Applications on bar charts and histograms Box plots and scatter plots with Rstudio Applications on plots with partition Quiz 3 Quiz debrief and final case introduction Spatial data in R GIS applications in Rstudio Introduction to Markdown and R Presentations Applications of HTML5 authoring with R Quiz 4 Final case study