Mathematical Decision Making: Predictive Models and Optimization

Rated 5 out of 5 by from Excellent presentation, in depth coverage The course was exactly as described. It was well presented and well structured with enough detail to be useful at once. The instructor's personality and sense of humor made the course enjoyable and easy to study.
Date published: 2018-10-09
Rated 4 out of 5 by from "Mathematical" should be "Methods" While I found the course very interesting, instructive and practical, there is little math in it beyond setting up problems to be solved. The algorithms used to actually solve them are not explained, beyond how to run functions in Excel (or other spreadsheet programs). If you are looking for practical methods using Excel the course is for you, but it is not for someone wanting a deeper explanation of what is behind the Excel routines.
Date published: 2018-01-20
Rated 3 out of 5 by from Needs access to example spreadsheets I thought that the course is worthwhile but the value would be significantly enhanced if The Great Courses provided access to the spreadsheets used in Professor Stevens' examples.
Date published: 2018-01-15
Rated 5 out of 5 by from Very Interesting I got this on a lark and am enjoying the details, however it is a challanging presentation. The information density is quite high.
Date published: 2018-01-08
Rated 5 out of 5 by from One Of Your Best! This course is incredible. Professor Allitt makes the subject matter exciting, easy to follow, with the right amount of detail. His love of the era is honest-he gives the good with the bad. I didn't want it to end, it was that good. Thank you Dr. Allitt!
Date published: 2017-12-24
Rated 5 out of 5 by from Good introduction to the concepts I'm about halfway through the course and have found it to be a good introduction to the concepts of predictive modeling and optimization techniques. It's exactly the level I needed.
Date published: 2017-12-06
Rated 3 out of 5 by from Too Much Reliance on the Uses of Spread Sheets The instructor assumes proficiency in the use of Excel and other spread sheet programs. The basic technique of solving a very simple example of the Simplex algorithm illustrating the use of slack variables was not covered. Going directly to computer programs is a poor way of teaching mathematical techniques.
Date published: 2017-05-31
Rated 4 out of 5 by from Challenging Have not finished course and i am struggling with some of the concepts. Great for younger people who have College Math experience. But even at 70 years old i am still learning and i will get the most out of this course as possible. Lively and entertaining instructor. Might have to watch again!
Date published: 2017-05-03
Rated 1 out of 5 by from Mathematical decision making This fell FAR short of a course. It was more of a collection of definitions and very little actual regression equation work. VERY misleading and disappointing. Cannot be called a Course
Date published: 2017-03-07
Rated 5 out of 5 by from Review of Mathematical Decision Making Good course material and presentation. If you're new to the topic, it's a great start with lots of material to follow up for a more advanced study. If you are familiar with the topic, it's a great refresher. ... and a very entertaining overview. Highly recommend this to anyone interested in the topic. One of the best of the Great Courses I have taken!!
Date published: 2017-01-03
Rated 5 out of 5 by from Mathematical Decision Making I am well past my prime arithmetic days. Listening to 24 half hr lectures on higher mathematics is not my idea of fun but I needed to understand stochastic optimization and mean-variance optimization. These are $2 terms thrown around like nickels by a bunch of guys with big hats and no cattle in my industry. You just didn't know what these terms were describing or if the guys using them knew anything. Finally I found this course and now I'm throwing around these $2 words but I have a 5c explanation that my clients can understand.
Date published: 2016-11-29
Rated 5 out of 5 by from Excellent Survey Course Great course: another one that lives up to the Institution's namesake. It is my opinion that this course is not designed to build proficiency in any one technique; but to give a person knowledge and appreciation of the various techniques used to analyze and assess data and provide a general understanding of uses, applications, strengths, weaknesses of the various data modeling. A survey course of sorts.
Date published: 2016-09-18
Rated 5 out of 5 by from Excellent but fast paced presentation This was a very interesting although somewhat challenging course. At least I was motivated to repeat many sections of the video or entire lectures when I wasn't sure I had understood the basic points, which is actually a complement. Professor Stevens' enthusiastic and animated presentation of the subject matter was nothing short of amazing, even the Steve Martin impressions. I realize the pace of the course is a compromise due to trying to reach a broad audience, ranging from those somewhat familiar with the subject to total beginners. I especially appreciated his brief review of the principles of probability theory and matrix algebra, without which I'm sure I would have been lost. Having said that, I watched the lecture on DEA analysis three times and I still don't fully understand it. I only wish this course was available 20 or 25 years ago. It would have saved me a lot of on-the-job learning. It's really great that most of the techniques covered in the course can be accomplished using spreadsheet software that most of us have or can download easily.
Date published: 2016-09-03
Rated 3 out of 5 by from Yet another math course with little math I ordered this course after reviewers of the Big Data course said this one had far better coverage of the topic of data analytics. In retrospect, I preferred the Big Data course but both were unsatisfactory. This is yet another watered-down "math" course with hardly any math. I find it ironic that the math courses aimed at adults are simplified compared to the ones aimed at high school and college students. This course makes extensive use of spreadsheets to solve the problems, which is probably very useful for business applications, but I'm a software engineer. Spreadsheets don't meet my particular needs. Lecture 9 had a repeated static sound every time the professor's jacket hit his microphone. The Teaching Company should have re-filmed that lecture. In the end, I found this course to be a boring slog to get through. Not difficult, just boring. I really wanted to like it, but for the most part I didn't.
Date published: 2016-08-28
Rated 5 out of 5 by from Wonderful introduction and presentation The professor has me very excited and encouraged in being able to learn to draw.
Date published: 2016-07-20
Rated 5 out of 5 by from Excellent presentation This course is an excellent overview of the subject with enough detail to useful and interesting but not so much as to be overwhelming. Mr. Stevens is one of those rare teachers who can take a complex idea and present it to you with absolute clarity, AND maintain your interest. I'll definitely be on the look out for more of his work.
Date published: 2016-07-15
Rated 4 out of 5 by from I liked very much decision trees Besides decision trees, I liked linear programming, although I got a little disappointed because the spreadsheets for doing optimization (Calc or Excel, at least the more interesting ones) are not available for replicating what the Professor was teaching. Otherwise, the course is a good introduction to the mathematical tool-box useful for business.
Date published: 2016-06-10
Rated 5 out of 5 by from Great Insights into Mathematical Decision Making! This is a great course to get the feel of what's possible in mathematical decision making. The instructor is very knowledgeable and fun to listen to. I am an Operations Research practitioner and these are the tools I use in my line of work. However, even with this background, I found this to be a great review of concepts that I formally studied 8 years ago. If you are new to these techniques, I think the lectures will give you a good feel of the kinds of techniques possible with mathematical decision making (e.g., forecasting, optimization, simulation, decision analysis), and the math prerequisite to understand the concepts is high school level math. In practice, these are advanced mathematical techniques, so it will likely require further study in addition to this series, but it's a great overview of an entire field. I could see this being very applicable to managers who perhaps oversee analysis activities and want to understand the language and techniques used to produce the products they may see; or to a math major who may want to pursue graduate education or career in Operations Research/Predictive Analytics; all the way down to a curious person who wants to understand the "secret weapons" used by most large companies (e.g., HP, UPS) to maximize profits and do things better.
Date published: 2016-05-26
Rated 5 out of 5 by from Excellent Everything was done right both from the professor's side and the Great Courses side. Students studying Quantitative Methods for Decisions Making will find this course extremely helpful.
Date published: 2016-05-05
Rated 5 out of 5 by from Money Well Spent The recorded format is critical for non-specialists like me. If I could only experience the course once, most of the information would go over my head. But I’ve been rewatching the lectures and recreating some of the Solver examples myself. As many of the other commenters have noted, the “Ah-ha!” moments are plentiful and rewarding. Recognizing that I still have only a beginner’s understanding of the topics, it is, nevertheless, very satisfying to realize that I have learned as much as I have -- which is quite a bit -- from this course.
Date published: 2016-03-20
Rated 5 out of 5 by from Crucial for Data Analytics /Modeling I rate this course very high because the topic very pervasive in business and technology given the internet of things to come. The course provides a solid background in differentiating how to use the right approach / tools to define optimization models given specific problems. I like that the course provides contemporary examples of companies, rescue scenarios, and simulations that are real world concepts. Most important, you realize that a combination of these tools are necessary to solve today's complex challenges of optimization given real constraints. Although a challenge, linear / non linear programming is fundamental to understanding how to design algorithmic intelligence for optimizing demand vs. supply constraints. Learning that its practical use in many industries makes it worth the practical education. Also, the Game Theory Course is also very good...I reflect on it often in my career...especially, the benefits of workers / sherkers collaborating in the long run result in optimal outcomes. Have fun with these courses.
Date published: 2016-02-28
Rated 5 out of 5 by from Well worth the investment I am an instructor for information systems, and most of the focus up to this point has been on database and general business systems (e.g. ERP). Given the extension of information through social networks and "big data" sources, I found this course valuable in understanding some foundational concepts of data science to effectively use information. Dr. Stevens clearly explains the concepts with good case studies. I also appreciated the balance kept in emphasizing what can be obviously done with existing tools versus going into the math behind the concepts. Thank you, Dr. Stevens for the excellent presentation! Your instructional skills are inspirational.
Date published: 2016-01-25
Rated 5 out of 5 by from Decision Making Made Fun I didn’t have a professional reason to purchase this course. I was drawn to it out of curiosity and a love for all things mathematical. From that perspective, I thought the course was outstanding. It left me looking for a reason to apply the theory presented. The math is pretty straight forward, but very powerful. Professor Stevens is really great. He is super energetic and knowledgeable. I don’t have any hesitation rating this course two thumbs up. I’m sorry I ran out of thumbs because it deserves more.
Date published: 2016-01-15
Rated 5 out of 5 by from Among the very best - gets down to business TGC's technical courses are hit and miss. At the top, you have those by Scott Stevens and Art Benjamin, which are truly excellent. I won't mention any at the bottom, but almost always their problem is too much (or all) fluff, and too little (or no) technical content. But this course is truly one of the best. First of all, the coverage is both broad and deep. Topics from what many call "Data Science" are treated: regression, time series, PCA, decision trees, and clustering. And the key topics from OR are covered: linear and integer programming with sensitivity analysis, some nonlinear programming, queuing and stochastic models and optimization. In each and every case, Prof. Stevens works through non trivial examples in complete detail so you actually learn the material honestly - all nuts and bolts with no fluff. (His website at James Madison University also has the excel workbooks if you want to download them.) You develop tools to solve problems on your own - not just "conceptual understanding" and buzzwords - but real hands on stuff. In addition, his presentation style is outstanding, and there are many excellent illustrative graphics which are not just pretty - they really do aid in understanding. I can hardly imagine how the presentation could be made any better. Would top students benefit from this course? Perhaps not, but average Joes like me, who are still technically literate and do want to work and learn something for real cannot go wrong here. A true gem.
Date published: 2015-12-26
Rated 5 out of 5 by from Great Teacher for the Future of Digital Business Professor's delivery and command of knowledge makes this easy to watch and learn. Kind of wish there were supplementary tools for use or purchase to apply concepts. With freeware, mathematics can be explored without expensive lab equipment or telescopes in space. This is the future of all business and functions.
Date published: 2015-11-08
Rated 5 out of 5 by from Wonderful series! I took a full semester course on decision science in my Master's degree, but this course tops it in so many ways! Prof. Stevens is simply the best at explaining these very complex ideas and concepts in a way that makes them understandable to me. I write very few reviews - but I just had to write one here. Thanks for the terrific course! Dan
Date published: 2015-10-06
Rated 5 out of 5 by from Great Course! This is a fantastic course. Dr. Stevens is a wonderful communicator. His examples were very clear, and he often used real-world examples. I was very impressed with the way Prof. Stevens simplified complex principles, resulting in an intuitive and engaging format. This course is definitely worth the money. i recommend it to anyone interested in prediction, modeling, data science, big data, business intelligence, operations research, etc. Thanks for a great course!
Date published: 2015-08-19
Rated 5 out of 5 by from Very Practical Course Taught Well The material presented in this course is very pragmatic. It is aimed at solving real problems found in the workplace for optimizing a business. Anyone who runs a small business or is part of finance, planning, or management in a larger one will find this course very helpful. The tools and skills taught in this course can also be readily applied in government, NGO, non-profit, or any other organization that needs to manage actions, output, and productivity against a budget. The tools presented are not only useful for optimizing operations but can also help in making strategic decisions about resource/investment allocations and predicting potential returns. . And all of these techniques are taught such that they can be performed using an Excel spreadsheet. I have been a manager, senior executive, and/or board director in the high tech sector for more than 3 decades and I wish I had more familiarity with this subject much earlier in my career. Dr. Stevens is an excellent instructor. He takes a subject matter which on the surface looks pretty dry and teaches it in a lively yet organized fashion. He has a great sense of humor, speaks in a clear voice, at a good pace, and with appropriate accompanying body language. He picks examples for highlighting each predictive model or algorithm which are easy to relate to. Yet everything he teaches is based on sound mathematical principles. In short, he is a joy to watch and listen to. No offense to anyone, but it is quite rare to see a college professor with the understanding of "real world", front line business issues that Dr. Stevens seems to have. I say this after having taken courses and seminars, as well as using consultants, from the faculty of the best business schools in the USA. The course production is done quite well. Good use is made of split screens and picture in picture. The spreadsheets that are presented in the examples have the relevant information highlighted to coincide with the speaker, making them easy to follow.. The course guide is excellent with complete lecture summaries, thought provoking questions to consider, an extensive glossary, and a bibliography. While the specific examples of each concept that Dr. Stevens uses are manageable in a spreadsheet, he does indicate how these same algorithms can be used for "Big Data" problems. A case in point is the route scheduling (clustering) algorithm used by UPS to schedule their driver delivery routes in the most time efficient and fuel efficient manner. It is easy to see how the same predictive modeling algorithms could be extended using computer programming to such "Big Data" problems. In this sense, I found this course more useful as a "how to" course than the TGC "Big Data" course. Of course a background in mathematics and/or operations research will be helpful for this course, but it is not really necessary. Dr. Stevens teaches the basic mathematics needed to solve the kind of problems he discusses. For those who work (or hope to work) in those areas mentioned in the opening paragraph I recommend this course with two thumbs up.
Date published: 2015-07-24
Rated 5 out of 5 by from Course includes all 33 Excel files I agree with all the other five star reviews. The course is outstanding. It's amazing what can be done with the knowledge and a computer. The instructor only uses Microsoft Excel and Open Office Calc - and nothing more - to do what was impossible just a few short years ago. The graphics are outstanding, the presentation is clear and logical. The book that comes with the course is excellent. The one point I wish to add is that TGC has made all 33 Excel files used in the course at no extra charge. They can be found via a link at the course's page on TGC web site. Log in to your TGC page, then go to this course. Next click on Course Starter Materials, and look at the links on the far right of the page. The one that shows will take you to all the Excel files, straight from the professor! Thank you very very much for making these available - The Excel files are the difference between watching and learning for me! This course is simply outstanding, and a pure delight to learn from. There is a lot I can apply right away, and I fully intend to watch the course again to learn even more. Thank and a hearty congratulations to everyone involved in the production of Mathematical Decision Making!
Date published: 2015-06-29
Rated 5 out of 5 by from Ancient History My undergraduate (circa 1977) minor was Management Science, and I spent many hours in the pre personal computer age punching cards and reading printouts. We thought we were going to change the world, and everything would be models and statistics, and it just wasn't so. One reason it wasn't was the poor fashion in which this material was taught. It was ALL formula and calculation, we came out knowing how to manually invert matrices, and with no concept of problem formulation. The PC has now made regression analysis trivial. Even my ancient HP-12C can do a limited number of pairs and simple regression. And yet even today, few understand this stuff well enough to apply it. I enjoyed this course because Stevens tries to avoid raw number crunching, and focuses on the appropriate use of technique. But, I would caution against enthusiasm. Business is much more art than method, and even when I was trying to do statistical analysis at data driven Bell Labs, I found just getting accurate business data was far more difficult than calculating optimal paths. It takes a long time before things happen quickly.
Date published: 2015-06-27
  • y_2021, m_4, d_21, h_17
  • bvseo_bulk, prod_bvrr, vn_bulk_3.0.14
  • cp_2, bvpage2n
  • co_hasreviews, tv_5, tr_55
  • loc_en_CA, sid_1342, prod, sort_[SortEntry(order=SUBMISSION_TIME, direction=DESCENDING)]
  • clientName_teachco
  • bvseo_sdk, p_sdk, 3.2.1
  • CLOUD, getReviews, 7.7ms
Mathematical Decision Making: Predictive Models and Optimization
Course Trailer
The Operations Research Superhighway
1: The Operations Research Superhighway

Survey the extraordinary range of applications for operations research and predictive analytics. Professor Stevens defines these fields, previews the mathematical techniques that underlie them, and charts their history, from World War II defense research to their rapid growth in the computer era.

33 min
Forecasting with Simple Linear Regression
2: Forecasting with Simple Linear Regression

Linear regression is a powerful method for describing connections between related quantities. Analyze several problems using linear regression. For example, predict the waiting time for an eruption of the Old Faithful geyser based on how long the previous eruption lasted....

32 min
Nonlinear Trends and Multiple Regression
3: Nonlinear Trends and Multiple Regression

Explore more complex linear regression problems, which involve nonlinear functions and/or multiple inputs. Many real-life situations require these approaches, called transformation of variables and multiple linear regression. Learn how to envision the data graphically, and witness the ease with which spreadsheets solve these problems....

32 min
Time Series Forecasting
4: Time Series Forecasting

Time series forecasting is a valuable tool when there's little data on what drives a process. Using the example of U.S. housing starts, learn how to glean information from historical figures, taking into account both long-term trends and seasonal fluctuations to create a forecast and assess its reliability....

32 min
Data Mining-Exploration and Prediction
5: Data Mining-Exploration and Prediction

Plunge into the fast-growing field of data mining, which exploits computational power and innovative algorithms to analyze the ever-increasing deluge of data. Focus on classification and prediction, seeing how classification trees can help solve the problem of building a filter that predicts spam email messages....

32 min
Data Mining for Affinity and Clustering
6: Data Mining for Affinity and Clustering

Delve deeper into data mining by exploring affinity analysis, or "what goes with what." One approach uses association rules to discover relevant connections between variables, while another employs clustering. For example, Pandora Radio uses these tools to make music recommendations based on a listener's song preferences....

30 min
Optimization-Goals, Decisions, and Constraints
7: Optimization-Goals, Decisions, and Constraints

Get the big picture on optimization, which is the focus of the next section of the course. Optimization seeks the best possible answer to a given problem. Learn how to model an optimization problem by asking four key questions. Then trace the steps in an example from the airline industry. ...

29 min
Linear Programming and Optimal Network Flow
8: Linear Programming and Optimal Network Flow

Continue your study of optimization problems by looking at solutions that use linear programming-an approach of exceptional power, speed, and simplicity. See how linear programming showed Union Pacific a cost-saving way to distribute railroad cars to locations throughout the country....

32 min
Scheduling and Multiperiod Planning
9: Scheduling and Multiperiod Planning

Investigate multiperiod planning problems. You will apply the tools from previous lectures to schedule activities and control inventory. You will also map out an investment plan that gives you the money you need, when you need it....

29 min
Visualizing Solutions to Linear Programs
10: Visualizing Solutions to Linear Programs

Mathematical intuition can be a powerful tool for solving mathematical problems. See how the answer almost jumps out at you when you apply a graphical method to certain types of optimization problems. Professor Stevens walks you through a real-life example involving personal financial investments-and spaghetti....

31 min
Solving Linear Programs in a Spreadsheet
11: Solving Linear Programs in a Spreadsheet

Learn how to solve a linear program using the famous simplex algorithm, developed by George Dantzig. Follow this easy, step-by-step approach that will allow you to use a spreadsheet, such as Calc or Excel, to find the optimal solution to virtually any linear program that has one. Watch how fast you get results!...

31 min
Sensitivity Analysis-Trust the Answer?
12: Sensitivity Analysis-Trust the Answer?

How much can you change a parameter in a problem before you affect the optimal solution? How do you forecast the tipping point at which dramatic changes occur? Sensitivity analysis will do the trick. Investigate the application of this valuable tool to linear programs....

31 min
Integer Programming-All or Nothing
13: Integer Programming-All or Nothing

Many problems contain variables that must be integers-for example, the number of units of a product or the number of production plants. Explore the special challenges presented by integer programs. Solve examples using the graphical method, then see how to find solutions with a spreadsheet....

31 min
Where Is the Efficiency Frontier?
14: Where Is the Efficiency Frontier?

Rating the efficiency of an operation is difficult if multiple inputs and outputs are involved. This often happens when trying to evaluate productivity among non-profits or government programs. Learn to use a popular technique that makes such comparisons simple, thanks to data envelopment analysis....

32 min
Programs with Multiple Goals
15: Programs with Multiple Goals

How do you evaluate the quality of a solution based on more than a single objective? Focus on three approaches: the weighted average, soft constraints combined with penalties, and prioritizing goals. Evaluate these in terms of NBC's difficulty in setting television advertising schedules, due to multiple objectives....

30 min
Optimization in a Nonlinear Landscape
16: Optimization in a Nonlinear Landscape

Review the lessons of linear programming, which you have been studying since Lecture 8. Then venture into the world of nonlinear programming. Professor Stevens orients you to this fascinating realm by demonstrating techniques that build your mathematical intuition for solving nonlinear problems....

31 min
Nonlinear Models-Best Location, Best Pricing
17: Nonlinear Models-Best Location, Best Pricing

Roll up your sleeves and tackle two practical problems in nonlinear programming: pick a location for a hub in an airline flight network, and price a retail product for maximum sales. In the latter case, you learn to model what makes Costco such a runaway success....

33 min
Randomness, Probability, and Expectation
18: Randomness, Probability, and Expectation

Probability allows you to evaluate situations where only partial control is possible-such as investment opportunities, public relations problems, and waiting lines. Hone your skills in elementary probability with simple challenges, including a game called "Cat or No Cat."...

32 min
Decision Trees-Which Scenario Is Best?
19: Decision Trees-Which Scenario Is Best?

See how decision trees and probability analysis can lead to optimal decisions in situations that seem bewilderingly uncertain. Professor Stevens focuses on a potential public relations disaster faced by executives at Gerber Products and how they used a decision tree to chart a successful strategy....

31 min
Bayesian Analysis of New Information
20: Bayesian Analysis of New Information

According to Bayes's theorem, the chance that something is true changes as new and better information becomes available. Trace the use of this principle in the search for wreckage from Air France flight 447, and learn how itthis simple but powerful idea serves as a corrective to bad decision making in many spheres....

31 min
Markov Models-How a Random Walk Evolves
21: Markov Models-How a Random Walk Evolves

Peer into the future with Markov analysis, which studies random systems to predict possible future outcomes. Apply this technique to the downed plane example from the previous lecture, and then see how Markov analysis helped a German direct-marketing firm avoid financial ruin....

31 min
Queuing-Why Waiting Lines Work or Fail
22: Queuing-Why Waiting Lines Work or Fail

Extend your use of Markov analysis to waiting lines, or queues. Discover how a random arrival process is analogous to the sound of popcorn popping. Then probe the dramatic decrease in waiting times that can result from relatively minor adjustments in workforce or equipment....

30 min
Monte Carlo Simulation for a Better Job Bid
23: Monte Carlo Simulation for a Better Job Bid

Graduate to one of the most versatile and widely used techniques in operations research: simulation, which models the intricate interplay of variables in complicated situations. Focus on a competitive bid for a building project and how simulation can come up with a winning strategy....

30 min
Stochastic Optimization and Risk
24: Stochastic Optimization and Risk

Bring your entire toolkit to bear on the case history from Lecture 23, using stochastic optimization to take the full measure of your competitors for the building project. With this closing problem, you'll see how combining predictive analytics and optimization can help you stay one step ahead of the competition....

32 min
Scott P. Stevens

If you keep your eyes and mind open, you're going to find a lot of other places that our ideas apply.


The Pennsylvania State University


James Madison University

About Scott P. Stevens

Dr. Scott P. Stevens is Professor of Computer Information Systems and Management Science at James Madison University in Harrisonburg, Virginia, where he has taught since 1984. Professor Stevens holds a Ph.D. in Mathematics from The Pennsylvania State University, where he received B.S. degrees in both Mathematics and Physics and graduated first in his class in the College of Science. Honored many times over for his remarkable abilities in the classroom, Professor Stevens has been a recipient of the Carl Harter Award, his university's highest teaching award; been named the outstanding graduate teacher in JMU's M.B.A. program; and has on five occasions been selected by students as the outstanding teacher in JMU's undergraduate business program, the first teacher to be so honored. A frequent consultant in the business arena, Professor Stevens has been published in a broad variety of academic and professional journals, writing or collaborating on subjects as varied as neural network prediction of survival in blunt-injured trauma patients; the effect of private school competition on public schools; standards of ethical computer usage in different countries; automatic data collection in business; and optimization of the purchase, transportation, and deliverability of natural gas from the Gulf of Mexico.

Also By This Professor