# Assessing the Proposed 2014 Statistics Curriculum 9/22/2013 V0A. 1

Save this PDF as:

Size: px
Start display at page:

## Transcription

1 Assessing the Proposed 2014 Statistics Curriculum 9/22/2013 V0A 1 Business Analytics vs. Data Science by Milo Schield Member: International Statistical Institute US Rep: International Statistical Literacy Project Director, W. M. Keck Statistical Literacy Project Presented at the Annual Decision Sciences Institute Meeting Tampa FL. Nov 22, Slides at: 2 Data Science (DS), Data Analytics (DA), Business Analytics (BA) In any new field, new terms are a bit vague. Distinctions are shades of grey; not black-white. DS, DA, BA all involve some combination of mathematics, computer science and statistics. Ideally, a DS major would take a substantial number of courses in all three areas. Ideally, they work from start-to-finish on a DS project. Most students don t have time for this. 3 Mathematics Aspect 4 Computer Science Perspective 2nd Stat courses can be classified by: Math pre-req: algebra, pre-calc or calculus. Topics: Just regression (Mendenhall-Sincich, Draper-Smith). Multilevel / hierarchical models (Gelman-Hill). Multivariate methods: cluster analysis, discriminant analysis, factor analysis, principle components, logistic regression, etc. (Sharma, Johnson-Wichern, Berenson-Levine- Goldstein) Data acquisition, manipulation & summarization are big topics in Computer Science. Computer software is a big issue: SQL databases, SAS, R, Hadoop, etc. 5 Data Science 6 Business Analytics Data science is dominated by computer scientists and mathematicians. The primary focus is on associations: correlations, models, prediction... Neither computer science nor mathematics has any language for causation. Both focus on what is necessary or sufficient. Both mathematics and computer science focus on the form and generally eschew the matter. For science, the goal is truth deep truths. For the physical sciences, the truth typically includes causal connections. For math and computer science, causation is conspicuously absent. For business, the goal is create products and services that will be bought by customers at a price that generates a profit. Sometimes this involves prediction; other times is involves an intervention. Both of these involve causal connections. 1

2 Assessing the Proposed 2014 Statistics Curriculum 9/22/2013 V0A 7 Four Big Ideas in Teaching Big-Data 8 Statistical Literacy: Big Idea #1: Association 1. Association is not causation, but is often a sign of causation somewhere. 2. Confounding. Why getting more data may not reduce confounding. 3. Coincidence: Why coincidence increases as the amount of data (# of rows) increases. 4. Error: Why errors (false positives) increase as the object of interest gets smaller (rarer). Just saying Association is not Causation exemplifies the abstinence approach to statistics. Abstinence may be fine in a math class. It is not acceptable in a Business program where associations are typically a sign of causation somewhere. Students should learn which statistical associations give stronger support for a causal connection. 9 Statistical Literacy: Big Idea #2: Confounding Confounders are related factors not taken into account in a study. The influence of confounders [confounding] is omni-present in observational studies. Simpson s paradox (sign reversal or confounding) is incidental when modelling or forecasting, dominates when searching for causes. 10 Statistical Literacy: Big Idea #3: Coincidence Margin of error decreases as sample size increases. The Law of Very Large Numbers: the unlikely becomes almost certain given enough tries. Coincidence may be totally spurious or a sign of causation. 11 Statistical Literacy: Big Idea #4: Tests False positive are a constant problem in tests. Qualitatively, the lower the prevalence of the group, the higher the chance of a false positive. Quantitatively, if the prevalence of the group of interest is the same as the error rate in the test, then the prediction accuracy is always 50%. The quantitative relationship is simple, memorable and helps in evaluating tests using Big Data. 12 Conclusion Business Analytics should focus on teaching the big ideas underlying the statistics produced by any analysis of observational data: big or small. Business Analytics should help students see which associations give stronger support for a causal connection. They should be able to see the influence of confounders, of coincidence and Type-1 errors in big data. 2

3 Assessing the Proposed 2014 Statistics Curriculum 9/22/2013 V0A 13 References Berenson, (2013). Statistics Course for Big Data & Analytics. Slides at Berenson, (2013). Big Data Implications for Stat Analysis & Instruction. Levine, Szabat & Stephan (2013). Data Discovery pdf/2013-levine-szabat-stephan-dsi-msmesb-slides.pdf Schield, M. (2014). Two Big Ideas for Teaching Big Data: ECOTS Paper at Schield, M. (2014). Big Data: Coincidence. National Numeracy Network. Stine, D. (2013): Big Data Implications for intro stats. 3

4 1 Business Analytics vs. Data Science by Milo Schield Member: International Statistical Institute US Rep: International Statistical Literacy Project Director, W. M. Keck Statistical Literacy Project Presented at the Annual Decision Sciences Institute Meeting Tampa FL. Nov 22, Slides at:

5 2 Data Science (DS), Data Analytics (DA), Business Analytics (BA) In any new field, new terms are a bit vague. Distinctions are shades of grey; not black-white. DS, DA, BA all involve some combination of mathematics, computer science and statistics. Ideally, a DS major would take a substantial number of courses in all three areas. Ideally, they work from start-to-finish on a DS project. Most students don t have time for this.

6 3 Mathematics Aspect 2nd Stat courses can be classified by: Math pre-req: algebra, pre-calc or calculus. Topics: Just regression (Mendenhall-Sincich, Draper-Smith). Multilevel / hierarchical models (Gelman-Hill). Multivariate methods: cluster analysis, discriminant analysis, factor analysis, principle components, logistic regression, etc. (Sharma, Johnson-Wichern, Berenson-Levine- Goldstein)

7 4 Computer Science Perspective Data acquisition, manipulation & summarization are big topics in Computer Science. Computer software is a big issue: SQL databases, SAS, R, Hadoop, etc.

8 5 Data Science Data science is dominated by computer scientists and mathematicians. The primary focus is on associations: correlations, models, prediction... Neither computer science nor mathematics has any language for causation. Both focus on what is necessary or sufficient. Both mathematics and computer science focus on the form and generally eschew the matter.

9 6 Business Analytics For science, the goal is truth deep truths. For the physical sciences, the truth typically includes causal connections. For math and computer science, causation is conspicuously absent. For business, the goal is create products and services that will be bought by customers at a price that generates a profit. Sometimes this involves prediction; other times is involves an intervention. Both of these involve causal connections.

10 7 Four Big Ideas in Teaching Big-Data 1. Association is not causation, but is often a sign of causation somewhere. 2. Confounding. Why getting more data may not reduce confounding. 3. Coincidence: Why coincidence increases as the amount of data (# of rows) increases. 4. Error: Why errors (false positives) increase as the object of interest gets smaller (rarer).

11 8 Statistical Literacy: Big Idea #1: Association Just saying Association is not Causation exemplifies the abstinence approach to statistics. Abstinence may be fine in a math class. It is not acceptable in a Business program where associations are typically a sign of causation somewhere. Students should learn which statistical associations give stronger support for a causal connection.

12 9 Statistical Literacy: Big Idea #2: Confounding Confounders are related factors not taken into account in a study. The influence of confounders [confounding] is omni-present in observational studies. Simpson s paradox (sign reversal or confounding) is incidental when modelling or forecasting, dominates when searching for causes.

13 10 Statistical Literacy: Big Idea #3: Coincidence Margin of error decreases as sample size increases. The Law of Very Large Numbers: the unlikely becomes almost certain given enough tries. Coincidence may be totally spurious or a sign of causation.

14 11 Statistical Literacy: Big Idea #4: Tests False positive are a constant problem in tests. Qualitatively, the lower the prevalence of the group, the higher the chance of a false positive. Quantitatively, if the prevalence of the group of interest is the same as the error rate in the test, then the prediction accuracy is always 50%. The quantitative relationship is simple, memorable and helps in evaluating tests using Big Data.

15 12 Conclusion Business Analytics should focus on teaching the big ideas underlying the statistics produced by any analysis of observational data: big or small. Business Analytics should help students see which associations give stronger support for a causal connection. They should be able to see the influence of confounders, of coincidence and Type-1 errors in big data.

16 13 References Berenson, (2013). Statistics Course for Big Data & Analytics. Slides at Berenson, (2013). Big Data Implications for Stat Analysis & Instruction. Levine, Szabat & Stephan (2013). Data Discovery pdf/2013-levine-szabat-stephan-dsi-msmesb-slides.pdf Schield, M. (2014). Two Big Ideas for Teaching Big Data: ECOTS Paper at Schield, M. (2014). Big Data: Coincidence. National Numeracy Network. Stine, D. (2013): Big Data Implications for intro stats.

### ECOTS Two Big Ideas for Teaching Big Data: Coincidence and Confounding 2014

Welcome to this ECOTS invited webinar: Two big ideas for teaching big data. Thanks to Jean and Michelle for heroic efforts in trying to make the originally scheduled webinar work despite the technical

### ANALYTICS CENTER LEARNING PROGRAM

Overview of Curriculum ANALYTICS CENTER LEARNING PROGRAM The following courses are offered by Analytics Center as part of its learning program: Course Duration Prerequisites 1- Math and Theory 101 - Fundamentals

### Proposal for New Program: BS in Data Science: Computational Analytics

Proposal for New Program: BS in Data Science: Computational Analytics 1. Rationale... The proposed Data Science: Computational Analytics major is designed for students interested in developing expertise

### SPPH 501 Analysis of Longitudinal & Correlated Data September, 2012

SPPH 501 Analysis of Longitudinal & Correlated Data September, 2012 TIME & PLACE: Term 1, Tuesday, 1:30-4:30 P.M. LOCATION: INSTRUCTOR: OFFICE: SPPH, Room B104 Dr. Ying MacNab SPPH, Room 134B TELEPHONE:

### STATISTICAL LITERACY SURVEY ANALYSIS: READING GRAPHS AND TABLES OF RATES AND PERCENTAGES. Milo Schield Augsburg College, United States milo@pro-ns.

STATISTICAL LITERACY SURVEY ANALYSIS: READING GRAPHS AND TABLES OF RATES AND PERCENTAGES Milo Schield Augsburg College, United States milo@pro-ns.net In 2002, an international survey on reading graphs

### Is a Data Scientist the New Quant? Stuart Kozola MathWorks

Is a Data Scientist the New Quant? Stuart Kozola MathWorks 2015 The MathWorks, Inc. 1 Facts or information used usually to calculate, analyze, or plan something Information that is produced or stored by

### Creating Pivot Tables Using Excel 2003

1 Creating Pivot Tables Using Excel 2003 Creating Six Kinds of Tables Milo Schield Member: International Statistical Institute US Rep: International Statistical Literacy Project Director, W M Keck Statistical

### WASHBURN UNIVERSITY COLLEGE OF ARTS & SCIENCES. MATHEMATICS Bachelor of Arts (B.A.) 2015-2016

MATHEMATICS Bachelor of Arts (B.A.) 201-2016 Requirements for Major: At least 7 credit hours in the department, including: MA 11 Calculus and Analytic Geometry I MA 12 Calculus and Analytic Geometry II

### Department/Academic Unit: Public Health Sciences Degree Program: Biostatistics Collaborative Program

Department/Academic Unit: Public Health Sciences Degree Program: Biostatistics Collaborative Program Department of Mathematics and Statistics Degree Level Expectations, Learning Outcomes, Indicators of

Elisabeth Versailles elisabeth.versailles@sas.com Academic Relationships Manager SAS Academic Program SAS usage Software Professors Researchers Students SAS Analytics U SAS Certification SAS Analytics

### Implications of Big Data for Statistics Instruction 17 Nov 2013

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

### COMMON CORE STATE STANDARDS FOR

COMMON CORE STATE STANDARDS FOR Mathematics (CCSSM) High School Statistics and Probability Mathematics High School Statistics and Probability Decisions or predictions are often based on data numbers in

### SAS and OSU Data Mining Certificate and Marketing Analytics Certificate Program

SAS Analytics Day SAS and OSU Data Mining Certificate and Marketing Analytics Certificate Program Goutam Chakraborty Professor (Marketing) Agenda Conference details 190+ registered attendees (60+ companies)

### RUTHERFORD HIGH SCHOOL Rutherford, New Jersey COURSE OUTLINE STATISTICS AND PROBABILITY

RUTHERFORD HIGH SCHOOL Rutherford, New Jersey COURSE OUTLINE STATISTICS AND PROBABILITY I. INTRODUCTION According to the Common Core Standards (2010), Decisions or predictions are often based on data numbers

### DATA ANALYTICS USING R

DATA ANALYTICS USING R Duration: 90 Hours Intended audience and scope: The course is targeted at fresh engineers, practicing engineers and scientists who are interested in learning and understanding data

### Graduate Standing. ACC 357 Equity Accounting or equivalent; no credit for both ACC 416 and ACC 516. Summer. Graduate Standing and ACC 360

This document represents a guide for College of Business graduate students to utilize for on-campus and online course program planning. Please consult COB registration email notices and the Schedule of

### Credit Risk Models. August 24 26, 2010

Credit Risk Models August 24 26, 2010 AGENDA 1 st Case Study : Credit Rating Model Borrowers and Factoring (Accounts Receivable Financing) pages 3 10 2 nd Case Study : Credit Scoring Model Automobile Leasing

### Statistics for BIG data

Statistics for BIG data Statistics for Big Data: Are Statisticians Ready? Dennis Lin Department of Statistics The Pennsylvania State University John Jordan and Dennis K.J. Lin (ICSA-Bulletine 2014) Before

### A Primer on Forecasting Business Performance

A Primer on Forecasting Business Performance There are two common approaches to forecasting: qualitative and quantitative. Qualitative forecasting methods are important when historical data is not available.

### 2012 3 R s and Predictive Modeling Boot Camp Nov. 8-9, 2012. Session #1: Predictive Modeling: An Overview Syed Muzayan Mehmud, ASA, FCA, MAAA

2012 3 R s and Predictive Modeling Boot Camp Nov. 8-9, 2012 Session #1: Predictive Modeling: An Overview Syed Muzayan Mehmud, ASA, FCA, MAAA Predictive Modeling: An Overview November 8, 2012 Syed M. Mehmud

### Virtual Site Event. Predictive Analytics: What Managers Need to Know. Presented by: Paul Arnest, MS, MBA, PMP February 11, 2015

Virtual Site Event Predictive Analytics: What Managers Need to Know Presented by: Paul Arnest, MS, MBA, PMP February 11, 2015 1 Ground Rules Virtual Site Ground Rules PMI Code of Conduct applies for this

### Graduate Standing. ACC 357 Equity Accounting or equivalent; no credit for both ACC 416 and ACC 516. Summer. Graduate Standing and ACC 360

This document represents a guide for College of Business graduate students to utilize for on-campus and online course program planning. Please consult COB registration email notices and the Schedule of

### What? So what? NOW WHAT? Presenting metrics to get results

What? So what? NOW WHAT? What? So what? Visualization is like photography. Impact is a function of focus, illumination, and perspective. What? NOW WHAT? Don t Launch! Prevent your own disastrous decisions

### HANDBOOK FOR THE APPLIED AND COMPUTATIONAL MATHEMATICS OPTION. Department of Mathematics Virginia Polytechnic Institute & State University

HANDBOOK FOR THE APPLIED AND COMPUTATIONAL MATHEMATICS OPTION Department of Mathematics Virginia Polytechnic Institute & State University Revised June 2013 2 THE APPLIED AND COMPUTATIONAL MATHEMATICS OPTION

### R YOU READY FOR PYTHON? Sunday 19th April, 2015

R YOU READY FOR PYTHON? Sunday 19th April, 2015 THIS IS NOT A PYTHON VS R TALK credits - https://meetmrholland.wordpress.com/2013/02/03/creative-5-tips-to-make-all-your-meetings-exactly-the-same/ WHO ARE

### Simplifying the audit through innovation. PwC Accounting Symposium August 7, 2015. PwC Audit Transformation 19

Simplifying the audit through innovation PwC Accounting Symposium August 7, 2015 PwC Audit Transformation 19 Simplifying the audit through innovation A closer look What s driving our transformation Globalization

### Ch.3 Demand Forecasting.

Part 3 : Acquisition & Production Support. Ch.3 Demand Forecasting. Edited by Dr. Seung Hyun Lee (Ph.D., CPL) IEMS Research Center, E-mail : lkangsan@iems.co.kr Demand Forecasting. Definition. An estimate

### Information from participating colleges regarding QR at their institutions

Strengthening Quantitative Reasoning: From Campus Conversations to Effective Curricular Reforms An ACM-Sponsored Conference on the Carleton College Campus September 30 and October 1, 2005 Information from

### ACC 357 Equity Accounting or equivalent; no credit for both ACC 416 and ACC 516. Fall, Winter

This document represents a guide for College of Business graduate students to utilize for on-campus and online course program planning. Please consult COB registration email notices and the Schedule of

### Chapter 14: Analyzing Relationships Between Variables

Chapter Outlines for: Frey, L., Botan, C., & Kreps, G. (1999). Investigating communication: An introduction to research methods. (2nd ed.) Boston: Allyn & Bacon. Chapter 14: Analyzing Relationships Between

### Introduction to Longitudinal Data Analysis

Introduction to Longitudinal Data Analysis Longitudinal Data Analysis Workshop Section 1 University of Georgia: Institute for Interdisciplinary Research in Education and Human Development Section 1: Introduction

### ACCOUNTING School of Business Central Connecticut State University

ACCOUNTING School of Business Central Connecticut State University English Literature History *ECON 200 *ECON 201 *ENG 110 Macroeconomics Microeconomics Freshman Composition *MATH 125 Applied Calculus

### Sunnie Chung. Cleveland State University

Sunnie Chung Cleveland State University Data Scientist Big Data Processing Data Mining 2 INTERSECT of Computer Scientists and Statisticians with Knowledge of Data Mining AND Big data Processing Skills:

### Common Core Standards for Mathematics Alignment to Early Numeracy

Common Core Standards for Mathematics Alignment to Early Numeracy The Common Core State Standards (http://www.corestandards.org/ ) provide a consistent, clear understanding of what students are expected

### DATA MINING TECHNIQUES AND APPLICATIONS

DATA MINING TECHNIQUES AND APPLICATIONS Mrs. Bharati M. Ramageri, Lecturer Modern Institute of Information Technology and Research, Department of Computer Application, Yamunanagar, Nigdi Pune, Maharashtra,

### EVENT HISTORY AND MULTILEVEL ANALYSIS UNIT

WARSAW SCHOOL OF ECONOMICS EVENT HISTORY AND MULTILEVEL ANALYSIS UNIT HEADED BY PROFESSOR EWA FRĄTCZAK EVENT HISTORY & MULTILEVEL ANALYSIS UNIT The fact that our efforts are appreciated, motivates the

### M.Ed. in Educational Psychology: Research, Statistics, and Evaluation

M.Ed. in Educational Psychology: Research, Statistics, and Evaluation The M.Ed. program in Research, Statistics, and Evaluation prepares students for advanced graduate study in educational research or

### Total Credits: 32 credits are required for master s program graduates and 53 credits for undergraduate program.

Middle East Technical University Graduate School of Social Sciences Doctor of Philosophy in Business Administration In the Field of Quantitative Methods Aim of the PhD Program: Quantitative Methods is

### Outline. Chapter 1 - Introduction. Examples. Basic Terminology. Examples Basic terminology Choosing a statistical procedure.

Outline Chapter 1 - Introduction PSY 295 Oswald Examples Basic terminology Choosing a statistical procedure Chapter 1 Introduction 2 Examples Do caffeine and/or a quiet environment tend to improve studying

### Getting Ready for Big Data

Getting Ready for Big Data Implications for intro stats Bob Stine, www-stat.wharton.upenn.edu/~stine Change is upon us... Session topics Shifting away from classical methods Communication skills Data visualization

### What is Data Science? Data, Databases, and the Extraction of Knowledge Renée T., @becomingdatasci, November 2014

What is Data Science? { Data, Databases, and the Extraction of Knowledge Renée T., @becomingdatasci, November 2014 Let s start with: What is Data? http://upload.wikimedia.org/wikipedia/commons/f/f0/darpa

### CS&SS / STAT 566 CAUSAL MODELING

CS&SS / STAT 566 CAUSAL MODELING Instructor: Thomas Richardson E-mail: thomasr@u.washington.edu Lecture: MWF 2:30-3:20 SIG 227; WINTER 2016 Office hour: W 3.30-4.20 in Padelford (PDL) B-313 (or by appointment)

### Analytics Center. Creating a Data-Driven Culture

Analytics Center Creating a Data-Driven Culture We entered an era of Analytics & Big Data Big Data market is expected to reach \$84 billion in 2026 Source: Wikibon Big Data Market and Forecast, 2011-2020

### Internal and External Validity

Internal and External Validity ScWk 240 Week 5 Slides (2 nd Set) 1 Defining Characteristics When research is designed to investigate cause and effect relationships (explanatory research) through the direct

### What is the purpose of this document? What is in the document? How do I send Feedback?

This document is designed to help North Carolina educators teach the Common Core (Standard Course of Study). NCDPI staff are continually updating and improving these tools to better serve teachers. Statistics

### Master of Science in Healthcare Informatics and Analytics Program Overview

Master of Science in Healthcare Informatics and Analytics Program Overview The program is a 60 credit, 100 week course of study that is designed to graduate students who: Understand and can apply the appropriate

### Health Policy and Administration PhD Track in Health Services and Policy Research

Health Policy and Administration PhD Track in Health Services and Policy INTRODUCTION The Health Policy and Administration (HPA) Division of the UIC School of Public Health offers a PhD track in Health

### 1.1 What is statistics? Data Collection. Important Definitions. What is data? Descriptive Statistics. Inferential Statistics

1.1 What is statistics? Data Collection Chapter 1 A science (bet you thought it was a math) of Collecting of data (Chap. 1) Organizing data (Chap. 2) Summarizing data (Chap. 2, 3) Analyzing data (Chap.

### Example: Credit card default, we may be more interested in predicting the probabilty of a default than classifying individuals as default or not.

Statistical Learning: Chapter 4 Classification 4.1 Introduction Supervised learning with a categorical (Qualitative) response Notation: - Feature vector X, - qualitative response Y, taking values in C

### Towards a Critical Epistemology of Analytical Statistics

Towards a Critical Epistemology of Analytical Statistics Wendy Olsen and Jamie Morgan, British Sociological Association, March 2004 (York) Please write to the authors for a copy of the paper. It is under

### Analyzing data. Thomas LaToza. 05-899D: Human Aspects of Software Development (HASD) Spring, 2011. (C) Copyright Thomas D. LaToza

Analyzing data Thomas LaToza 05-899D: Human Aspects of Software Development (HASD) Spring, 2011 (C) Copyright Thomas D. LaToza Today s lecture Last time Why would you do a study? Which type of study should

### Opportunities with Predictive Analytics. Greg Leflar, Vice President greg.leflar@parivedasolutions.com

Opportunities with Predictive Analytics Greg Leflar, Vice President greg.leflar@parivedasolutions.com Opportunities for Predictive Analytics We help you separate the Value from the Hype The field of predictive

### Mathematical Sciences Bachelors Degrees and Enrollments in Four-Year Colleges and Universities

Chapter 3 Mathematical Sciences Bachelors Degrees and Enrollments in Four-Year Colleges and ersities Mathematics and statistics departments in the nation s four-year colleges and universities offer a wide

### School of Public Health and Health Services Department of Epidemiology and Biostatistics

School of Public Health and Health Services Department of Epidemiology and Biostatistics Master of Public Health and Graduate Certificate Biostatistics 0-04 Note: All curriculum revisions will be updated

### Executive Master of Public Administration. QUANTITATIVE TECHNIQUES I For Policy Making and Administration U6310, Sec. 03

INSTRUCTORS: PROFESSOR: Stuart E. Ward TEACHING ASSISTANT: Hamid Rashid E-Mail: sew9@columbia.edu hr99@columbia.edu Office Phone# 212.854.5941 (o) To Be Announced Office Room# 407A To Be Announced MEETING

### 3. Data Analysis, Statistics, and Probability

3. Data Analysis, Statistics, and Probability Data and probability sense provides students with tools to understand information and uncertainty. Students ask questions and gather and use data to answer

### Statistical Models in Data Mining

Statistical Models in Data Mining Sargur N. Srihari University at Buffalo The State University of New York Department of Computer Science and Engineering Department of Biostatistics 1 Srihari Flood of

### Learning outcomes. Knowledge and understanding. Competence and skills

Syllabus Master s Programme in Statistics and Data Mining 120 ECTS Credits Aim The rapid growth of databases provides scientists and business people with vast new resources. This programme meets the challenges

### EBM Cheat Sheet- Measurements Card

EBM Cheat Sheet- Measurements Card Basic terms: Prevalence = Number of existing cases of disease at a point in time / Total population. Notes: Numerator includes old and new cases Prevalence is cross-sectional

### Data Science and Business Analytics Certificate Data Science and Business Intelligence Certificate

Data Science and Business Analytics Certificate Data Science and Business Intelligence Certificate Description The Helzberg School of Management has launched two graduate-level certificates: one in Data

### Proposal for Undergraduate Certificate in Large Data Analysis

Proposal for Undergraduate Certificate in Large Data Analysis To: Helena Dettmer, Associate Dean for Undergraduate Programs and Curriculum From: Suely Oliveira (Computer Science), Kate Cowles (Statistics),

### χ 2 = (O i E i ) 2 E i

Chapter 24 Two-Way Tables and the Chi-Square Test We look at two-way tables to determine association of paired qualitative data. We look at marginal distributions, conditional distributions and bar graphs.

### Master of Science (MS) in Biostatistics 2014-2015. Program Director and Academic Advisor:

Master of Science (MS) in Biostatistics 014-015 Note: All curriculum revisions will be updated immediately on the website http://publichealh.gwu.edu Program Director and Academic Advisor: Dante A. Verme,

Data Mining with SAS Mathias Lanner mathias.lanner@swe.sas.com Copyright 2010 SAS Institute Inc. All rights reserved. Agenda Data mining Introduction Data mining applications Data mining techniques SEMMA

### A Common Vision for the Undergraduate Mathematics Program in 2025. Karen Saxe AMS Committee on Education October 16-17, 2014

A Common Vision for the Undergraduate Mathematics Program in 2025 Karen Saxe AMS Committee on Education October 16-17, 2014 Common Vision 2025 Starting Points Freshman and sophomore courses in the mathematical

### Data Analytics in Organisations and Business

Data Analytics in Organisations and Business Dr. Isabelle E-mail: isabelle.flueckiger@math.ethz.ch 1 Data Analytics in Organisations and Business Some organisational information: Tutorship: Gian Thanei:

### Keywords Big Data; OODBMS; RDBMS; hadoop; EDM; learning analytics, data abundance.

Volume 4, Issue 11, November 2014 ISSN: 2277 128X International Journal of Advanced Research in Computer Science and Software Engineering Research Paper Available online at: www.ijarcsse.com Analytics

### The following are the measurable objectives for graduated computer science students (ABET Standards):

Computer Science A Bachelor of Science degree (B.S.) in Computer Science prepares students for careers in virtually any industry or to continue on with graduate study in Computer Science and many other

### DecisionCraft Proposition

Ltd. DecisionCraft Proposition Background Consulting firm with focus on quantitative modeling & analytical techniques Founded by thought leaders with vast experience in academia & industry Teams with ideal

### Measurement and Metrics Fundamentals. SE 350 Software Process & Product Quality

Measurement and Metrics Fundamentals Lecture Objectives Provide some basic concepts of metrics Quality attribute metrics and measurements Reliability, validity, error Correlation and causation Discuss

### Masters in Financial Economics (MFE)

Masters in Financial Economics (MFE) Admission Requirements Candidates must submit the following to the Office of Admissions and Registration: 1. Official Transcripts of previous academic record 2. Two

### RUSRR048 COURSE CATALOG DETAIL REPORT Page 1 of 6 11/11/2015 16:33:48. QMS 102 Course ID 000923

RUSRR048 COURSE CATALOG DETAIL REPORT Page 1 of 6 QMS 102 Course ID 000923 Business Statistics I Business Statistics I This course consists of an introduction to business statistics including methods of

### Predicting US Unemployment Rates Based On Housing Starts

Predicting US Unemployment Rates Based On Housing Starts Summary: Housing starts have an extremely high correlation with unemployment rate in the United States. The probability that the two variables have

### SAS JOINT DATA MINING CERTIFICATION AT BRYANT UNIVERSITY

SAS JOINT DATA MINING CERTIFICATION AT BRYANT UNIVERSITY Billie Anderson Bryant University, 1150 Douglas Pike, Smithfield, RI 02917 Phone: (401) 232-6089, e-mail: banderson@bryant.edu Phyllis Schumacher

### New Course Proposal: ITEC-621 Predictive Analytics. Prerequisites: ITEC-610 Applied Managerial Statistics

New Course Proposal: ITEC-621 Predictive Analytics Academic Unit: KSB Teaching Unit: Information Technology Course Title: Predictive Analytics Course Number : ITEC-621 Credit Hours: 3 Proposed effective

### X X X a) perfect linear correlation b) no correlation c) positive correlation (r = 1) (r = 0) (0 < r < 1)

CORRELATION AND REGRESSION / 47 CHAPTER EIGHT CORRELATION AND REGRESSION Correlation and regression are statistical methods that are commonly used in the medical literature to compare two or more variables.

### 2015 Workshops for Professors

SAS Education Grow with us Offered by the SAS Global Academic Program Supporting teaching, learning and research in higher education 2015 Workshops for Professors 1 Workshops for Professors As the market

### Attaining Supply Chain Analytical Literacy. Sr. Director of Analytics Wal Mart (Bentonville, AR)

Attaining Supply Chain Analytical Literacy Rhonda R. Lummus F. Robert Jacobs Indiana University Bloomington Kelley School of Business Sr. Director of Analytics Wal Mart (Bentonville, AR) The Senior Director

### What is Data Science? Girl Develop It! Meetup Renée M. P. Teate, March 2015

What is Data Science? { Girl Develop It! Meetup Renée M. P. Teate, March 2015 Let s start with: What is Data? http://upload.wikimedia.org/wikipedia/commons/f/f0/darpa _Big_Data.jpg https://encryptedtbn2.gstatic.com/images?q=tbn:and9gcs9dku3_tzi-swwyaqee5y0ehuvoiznsya_raknubbd0jyxpx7pw

### Indiana s. Requirements (Class of 2016 & Beyond)

Indiana s Graduation Requirements (Class of 2016 & Beyond) The Importance of Academic Rigor A rigorous high school academic curriculum is the singlemost significant factor determining a student s success

### STATISTICAL LITERACY SURVEY RESULTS: READING GRAPHS AND TABLES OF RATES AND PERCENTAGES

STATISTICAL LITERACY SURVEY RESULTS: READING GRAPHS AND TABLES OF RATES AND PERCENTAGES Milo Schield Director of the W. M. Keck Statistical Literacy Project Augsburg College Minneapolis, MN 55454 In 2002,

### Data Mining Introduction

Data Mining Introduction Bob Stine Dept of Statistics, School University of Pennsylvania www-stat.wharton.upenn.edu/~stine What is data mining? An insult? Predictive modeling Large, wide data sets, often

### Understanding the Transformative Power of Big Data & Predictive Analytics

Understanding the Transformative Power of Big Data & Predictive Analytics Dallas Fort Worth Area IMA Meeting September 18, 2014 Today s topics Demystify the buzz of big data and analytics Discuss the relevance

### Concentration (15 cr hrs) Marketing Management International Business

Articulation Agreement Curriculum Sequence Onondaga Community College - Business Administration (A.S.) Buffalo State College - Business Administration (B.S.) First Semester Second Semester ENG 103 Freshman

### Quantitative or Qualitative?

2.1 Data: and Levels of Measurement 1 Quantitative or Qualitative?! Quantitative data consist of values representing counts or measurements " Variable: Year in school! Qualitative (or non-numeric) data

### First-Year Courses in Four-Year Colleges and Universities

Chapter 5 First-Year Courses in Four-Year Colleges and Universities Tables in this chapter further explore topics from Tables S.7 to S.13 in Chapter 1 and Tables E.2 to E.9 of Chapter 3, presenting details

### Principles of Data Mining by Hand&Mannila&Smyth

Principles of Data Mining by Hand&Mannila&Smyth Slides for Textbook Ari Visa,, Institute of Signal Processing Tampere University of Technology October 4, 2010 Data Mining: Concepts and Techniques 1 Differences

### Quantitative Finance

Quantitative Finance Joel Hasbrouck October 23, 2002 Copyright (c) 2002, Joel Hasbrouck, All rights reserved. 1 Topics What is quantitative finance? Where is it used? / Where are the jobs? Where are the

### Actuary vs Data Scientist

Actuary vs Data Scientist Richard Pugh Chief Data Scientist, Mango Solutions President @ R Consortium Chris Reynolds Head of Life Solutions Actuarial, PartnerRe 10 November 2015 Disclaimer The following

### Department of Computer Science and Computer Engineering Program Change Proposal. BA in Computer Science

Department of Computer Science and Computer Engineering Program Change Proposal BA in Computer Science Motivation: The Department of Computer Science and Engineering has had a Bachelor of Arts since its

### Mathematics Education Master of Education Degree Program PROFESSIONAL PORTFOLIO RUBRIC Candidate: ID#: Date: Evaluator:

Mathematics Education Master of Education Degree Program PROFESSIONAL PORTFOLIO RUBRIC Candidate: ID#: Date: Evaluator: Recommendations: Evaluator Signature: Grade: PASS FAIL Developing Acceptable Optimum

### Can Annuity Purchase Intentions Be Influenced?

Can Annuity Purchase Intentions Be Influenced? Jodi DiCenzo, CFA, CPA Behavioral Research Associates, LLC Suzanne Shu, Ph.D. UCLA Anderson School of Management Liat Hadar, Ph.D. The Arison School of Business,

9/3/2013 Course Descriptions: Undergraduate/Graduate Certificate Program in Data Visualization and Analysis Seton Hall University, South Orange, New Jersey http://www.shu.edu/go/dava Visualization and

### Practical Data Science with Azure Machine Learning, SQL Data Mining, and R

Practical Data Science with Azure Machine Learning, SQL Data Mining, and R Overview This 4-day class is the first of the two data science courses taught by Rafal Lukawiecki. Some of the topics will be

### M E M O R A N D U M. Faculty Senate Approved April 2, 2015

M E M O R A N D U M Faculty Senate Approved April 2, 2015 TO: FROM: Deans and Chairs Becky Bitter, Sr. Assistant Registrar DATE: March 26, 2015 SUBJECT: Minor Change Bulletin No. 11 The courses listed

### MATH PLACEMENT & PREREQUISITE GUIDE

MATH PLACEMENT & PREREQUISITE GUIDE WHAT YOU SHOULD KNOW! As part of the University of Wyoming s University Studies program (USP), all undergraduate students seeking a degree from the University of Wyoming

### Azure Machine Learning, SQL Data Mining and R

Azure Machine Learning, SQL Data Mining and R Day-by-day Agenda Prerequisites No formal prerequisites. Basic knowledge of SQL Server Data Tools, Excel and any analytical experience helps. Best of all:

### Information Management course

Università degli Studi di Milano Master Degree in Computer Science Information Management course Teacher: Alberto Ceselli Lecture 01 : 06/10/2015 Practical informations: Teacher: Alberto Ceselli (alberto.ceselli@unimi.it)