Experiences and Lessons Learned from UNC Wilmington

Slides:

Advertisements

Similar presentations

Implications of Big Data for Statistics Instruction Mark L. Berenson Montclair State University MSMESB Mini-Conference DSI - Baltimore November 17, 2013.

Advertisements

Psychology 202b Advanced Psychological Statistics, II February 10, 2011.

Overview of STAT 270 Ch 1-9 of Devore + Various Applications.

Data Analytics Program at Drake Brad C. Meyer, Chair Information Management and Business Analytics.

1 STATISTICS FOR BUSINESS DECISION MAKING Academic year Dott. Francesca Gagliardi.

Data Mining – Intro.

© Heikki Topi Computing and University Education in Analytics ACM Education Council San Francisco, CA November 2, 2013 Heikki Topi, Bentley University.

Adapting to Different Audiences Chris Lowery and Bill Miller Georgia College & State University.

Research Terminology for The Social Sciences.  Data is a collection of observations  Observations have associated attributes  These attributes are.

Business Analytics at LSSU C. Christopher Lee Associate Professor of Management Business Analytics at LSSU Presented by C. Christopher Lee Associate Professor.

Department of Business Information & Analytics MSMESB: Experience with Adding Analytics to the Academic Program Kellie Keeling University of Denver.

Satish Nargundkar Georgia State University Presented at the Decision Sciences Institute Annual Meeting, Baltimore, Nov , Business Analytics.

Stats for Engineers Lecture 9. Summary From Last Time Confidence Intervals for the mean t-tables Q Student t-distribution.

1 SUPPORT VECTOR MACHINES İsmail GÜNEŞ. 2 What is SVM? A new generation learning system. A new generation learning system. Based on recent advances in.

Examples of Computing Uses for Statisticians Data management : data entry, data extraction, data cleaning, data storage, data manipulation, data distribution.

Quantitative Methods in Finance (FINA 514/614) Assoc.Prof.Dr. Salih KATIRCIOGLU (Ph.D in Economic Development and International Economics)

Statistical Tools for Solar Resource Forecasting Vivek Vijay IIT Jodhpur Date: 16/12/2013.

Managerial Decision Analysis  Survey results  Course curriculum development.

Advanced Predictive Analytics Prof Sunil Wattal. Agenda Introductions Intro to Data Analytics Course Logistics Overview of Topics Setting up SAS EM.

Software Prediction Models Forecasting the costs of software development.

PY 603 – Advanced Statistics II TR 12:30-1:45pm 232 Gordon Palmer Hall Jamie DeCoster.

Implementing a Randomization-Based Curriculum for Introductory Statistics Robin H. Lock, Burry Professor of Statistics St. Lawrence University Breakout.

Chapter 5: Business Intelligence: Data Warehousing, Data Acquisition, Data Mining, Business Analytics, and Visualization DECISION SUPPORT SYSTEMS AND BUSINESS.

Analysis of sapflow measurements of Larch trees within the inner alpine dry Inn- valley PhD student: Marco Leo Advanced Statistics WS 2010/11.

Statistics: Unlocking the Power of Data Lock 5 Exam 2 Review STAT 101 Dr. Kari Lock Morgan 11/13/12 Review of Chapters 5-9.

Experience With Adding Analytics to the Academic Program Stephen Hill Barry Wray Cem Canel Lessons Learned from Business Analytics at UNCW.

Mathematical Sciences MA206 Probability and Statistics Program Director: LTC John Jackson Course Director: MAJ Kevin Cummiskey Assistant CD:MAJ Nick Howard.

Bioinformatics Curriculum Issues, goals, curriculum.

Neural Networks Demystified by Louise Francis Francis Analytics and Actuarial Data Mining, Inc.

1 STATISTICS FOR BUSINESS DECISION MAKING Academic year University of Siena Dott. Francesca Gagliardi.

Course Outline Presentation Reference Course Outline for MTS-202 (Statistical Inference) Fall-2009 Dated: 27 th August 2009 Course Supervisor(s): Mr. Ahmed.

Business Analytics Skills

Data Mining and Decision Support

OMIS 694, Big Data Analytics

FNA/Spring CENG 562 – Machine Learning. FNA/Spring Contact information Instructor: Dr. Ferda N. Alpaslan

Transportation and Transshipment Models Chapter 11 Supplement Russell and Taylor Operations and Supply Chain Management, 8th Edition.

Audit Analytics --An innovative course at Rutgers Qi Liu Roman Chinchila.

Joan Donohue University of South Carolina

Introduction Data Mining for Business Analytics (3rd ed.)

Department of Supply Chain Management & Analytics Proposed Bachelor of Science (B.S.) in Business - Analytics Track Paolo Catasti, PhD, MBA, CSSBB Teaching.

Wake Technical CC Located in Raleigh, NC

as presented on that date, with special formatting removed

Joan Donohue University of South Carolina

DATA MINING © Prentice Hall.

Business analytics Lessons from an undergraduate introductory course

Inference in Simple Linear Regression

Analytics and OR DP- summary.

Teaching Statistics Courses in the Wave of Business Analytics

Database Clearinghouse Update

Experiences with Business Analytics Curriculum Implementation

Business Statistics and Operations Management: Teaching Strategies and Assessment of Student Learning in Traditional vs. Online Classes presented by Dr.

The Business Analytics Program at Old Dominion University

What Statistical Knowledge Should Students Know to Prepare for Graduate Business Analytics? Jeffrey D. Camm Wake Forest University School of Business

Cse 344 May 30th – analysis.

GEOP 4355 SC Analytics Outline What is Analytics?

Session 2:50 PM - 4:20 PM; Saturday, Nov 18, 2017

David M. Levine, Baruch College (CUNY)

Data Analytics at CNU Dmitriy Shaltayev

Descriptive Statistics

Transportation and Transshipment Models

Is there a Gap Between Analytics Practice and our Academic Analytics Preparation? Data, Analytics, and Statistics Instruction DSI 2016 Austin, TX Kellie.

Workshop: JMP & R for Analytics Instruction

Descriptive vs. Inferential

INNOvation in TRAINING BUSINESS ANALYSTS HAO HElEN Zhang UniVERSITY of ARIZONA

Course Introduction CSC 576: Data Mining.

What’s Needed in a Second Statistics Class to Prepare Undergraduates for a Data Analytics Environment? A Panel Discussion at Decision Sciences Institute.

ADVANCED DATA ANALYSIS IN SPSS AND AMOS

How to Effectively Communicate Commonly misinterpreted Statistical terms in experimentation AMAZON IPC LAB.

Data Mining Overview.

REVIEW Course Review.

Presentation transcript:

Experiences and Lessons Learned from UNC Wilmington Barry Wray and Stephen Hill 2017 DSI Annual Meeting Washington D.C. November 18th 8:00 am – 9:30 am

Background Fall 2017 Conversion of Operations Management Concentration to Business Analytics (BAN) and Supply Chain Management (SCM)

Sequence of Courses (OPS) Business Analytics Supply Chain Purchasing + 3 Electives

Sequence of Courses (BAN) Business Analytics Big Data Analytics Advanced Statistics Database + 3 Electives

Prescriptive Analytics Sequence of Courses Advanced Statistics Business Analytics Prescriptive Analytics Simulation Optimization Predictive Analytics Healthcare Analytics Big Data Analytics Database

BAN Core Classes Business analytics Excel-Based Statistics Review and Overview of Descriptive, Predictive, and Prescriptive Analytics Advanced Statistics Excel and Other Software Tools (Tableau, JMP, etc.) Review of Basic Stats with an emphasis on Parameter Estimation Type I and II Errors, Power and Confidence Hypothesis Testing – Understanding P-Values ANOVA Appropriate Statistical Tools for Qualitative verses Quantitative Data Data Collection, Cleaning, and Visualization

BAN Core Classes Predictive Analytics R Based Correlation Regression (SLR, MLR, Logit) Classification and Regression Trees Neural Networks Supervised vs. Unsupervised Learning Prescriptive Analytics Excel and Other Tools (AMPL, ExtendSim, etc.) Linear Programming Integer/Binary Programming Nonlinear Programming

Challenges/Lessons Learned Ensure Adequate Coverage of Necessary Topics Topic/Tool Coordination Between Courses Minimize Prerequisite “Chains” Lessons learned “If You Build It, They Will Come” Be Prepared to Change Be Aware of Potential Push-Back