# Mathematical Decision Making: Predictive Models and Optimization

Course No. 1342
Professor Scott P. Stevens, Ph.D.
4.7 out of 5
52 Reviews
90% of reviewers would recommend this product
Course No. 1342
Video Streaming Included Free

### What Will You Learn?

• Learn about time series forecasting and simple line regression.
• Use linear programs in spreadsheets, and learn how to visualize solutions.
• Tackle nonlinear landscapes and improve your mathematical intuition.
• Work with decision trees and see how famous theorems and models have impacted major decisions in history.

## Course Overview

Not so long ago, executives faced with complex problems made decisions based on experience, intuition, and no small measure of luck. But now there’s a better way. In recent decades, mathematics and computer science have perfected formerly top-secret techniques for predicting the best possible outcomes when faced with conflicting options. This field goes by different names—analytics, operations research, linear and nonlinear programming, management science—but its purpose is simple: to apply quantitative methods to help business managers, public servants, investors, scientific researchers, and problem solvers of all kinds make better decisions.

Consider the following applications of this powerful set of tools:

• Pricing: Costco rose to become one of the top-ranked retailers in the world by combining membership fees with the economy of selling in bulk. A mathematical technique—called genetic algorithms—shows the advantages of this strategy as well as the optimum prices to charge.
• Scheduling: Using nonlinear programming, many airlines employ scheduling software that can find the most favorable solution to unexpected disruptions—from weather to mechanical problems to crew availability—saving millions of dollars in operating costs.
• Bidding: Simulation models can take a lot of guesswork out of competitive bidding for a project. By running repeated simulations against competitors, a bidder can come up with a proposal that has a good chance of winning the job, while still making a profit.
• Queuing: Any process that reflects the behavior of waiting lines is known as queuing. Markov analysis shows how a small increase in input to a system can have a major impact on waiting times. The method also reveals surprising solutions for making long waits vanish.

These same techniques can be applied to retirement planning, stock portfolio analysis, budget forecasting, health care allocation, public relations, marketing and advertising, and many other tasks for enterprises large and small. The applications are truly endless!

Mathematical decision making got its first rigorous tests during World War II, when the Allies used top-secret operations research to protect convoys, improve the aim of anti-aircraft fire, and locate the weak points on Allied bombers. After the war, private industry adopted operations research with enthusiasm, but these new methods were expensive, computing speed was slow, and only specialized experts could take advantage of the algorithms. That situation has changed dramatically, and today anyone with a home computer and a spreadsheet program can harness the power of these methods to solve practical problems. The trick is knowing what you can do and how to do it.

Mathematical Decision Making: Predictive Models and Optimization is your guide, teaching you the major mathematical techniques, applications, and spreadsheet procedures for basic analytics in 24 information-packed half-hour lectures. Your professor is award-winning educator Scott Stevens, Professor of Computer Information Systems and Business Analytics at James Madison University.

Those who will benefit from Professor Stevens’s engaging presentation include:

• managers eager to make better decisions—whether in business, technical, or non-profit endeavors;
• professionals aspiring to advance in their careers by mastering a proven approach to problem solving;
• those who work with or review spreadsheet and graphical presentations, and need to be able to separate good data from bad;
• students in business, mathematics, finance, marketing, health care, engineering, urban planning, and a host of other fields;
• math lovers curious to see a field that is often the opposite of calculus: simple functions and complex boundary conditions, instead of complex functions with simple boundary conditions; and
• lifelong learners who want to hone their critical thinking skills with important analytical techniques, made accessible and intellectually exciting as never before.

Discover the Art of Deciding

You’ll find that the challenge of analytics is not the math, which is often surprisingly easy, but the wide choice of procedures you have at your fingertips. The art is picking the most effective one to apply to your problem, and this is what Professor Stevens walks you through in fascinating detail. All that’s needed is a willingness to use simple equations. Moreover, you’ll see how modern spreadsheets take the drudgery out of finding solutions, and they make setting up and visualizing problems simple and straightforward.

Mathematical Decision Making is vividly illustrated with graphs, charts, diagrams, and computer animations, which greatly aid understanding the material. In addition, Professor Stevens demonstrates the importance of cultivating your visual intuition. This is particularly helpful when you move from linear programming to nonlinear programming, where effects of synergy and interaction can have strong impact on the bottom line. He shows how you can visualize this new world as a landscape, and then use your natural intuition to decide how best to approach the problem. As an illustration, you see how the fight between Blu-Ray and HD DVD for dominance in the high-definition video market can be pictured as a hyperbolic paraboloid—a saddle-shaped figure—with all of the possible outcomes of the competition mapped onto its surface.

Conveniently, the course guidebook includes additional thought-provoking questions, problems, and answers for each lecture, along with recommended resources to help you dig deeper into any topic where you want to know more.

Analyze a Wealth of Cases

The beauty of this course is that it features case after case of real-life examples. Among the many you’ll explore are these:

• Public relations: The makers of Gerber baby food had experienced a public relations problem earlier in their history. See how they used decision tree analysis during a second budding crisis a dozen years later to map their options and reach a successful decision.
• Keeping clients happy: NBC schedulers once had to match advertisers to television time slots by hand, juggling a bewildering number of competing demands. You’ll learn how computer algorithms and the concept of “hard” and “soft” constraints revolutionized their job.
• Finding a missing plane: No one knew why Air France flight 447 crashed into the ocean in 2009—until Bayesian analysis led searchers to the wreck site and the black box. Bayes’s theorem tells how to compute new probabilities as new information becomes available.
• Evaluating efficiency: Non-profit organizations and government programs are notoriously hard to evaluate for efficiency. Using hospitals as a test case, you’ll discover how data envelopment analysis shows which facilities are performing effectively, as well as how to improve the ones that aren’t.

An acclaimed instructor who practices what he teaches, Professor Stevens has pushed the boundaries of mathematical decision making on many fronts. His research has addressed such problems as neural network prediction of survival in trauma patients and how to optimize the market for natural gas from the Gulf of Mexico.

Above all, he loves mathematics and the wonders it can perform. “Math is an absolutely beautiful thing,” he marvels. “I’m at my happiest when I can get someone else to see just a piece of that. It’s lovely, structured, consistent, reliable, surprising, enticing, exotic. It’s a great world!” With Mathematical Decision Making, see for yourself how mathematics can make the everyday world we all inhabit a more comprehensible and much better place.

24 lectures
|  Average 31 minutes each
• 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. x
• 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. x
• 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. x
• 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. x
• 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. x
• 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." x
• 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. x
• 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. x
• 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. x
• 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. x
• 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! x
• 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. x
• 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. x
• 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. x
• 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. x
• 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. x
• 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. x
• 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." x
• 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. x
• 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 this simple but powerful idea serves as a corrective to bad decision making in many spheres. x
• 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. x
• 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. x
• 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. x
• 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. x

## Lecture Titles

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## What's Included

### What Does Each Format Include?

• Download 24 video lectures to your computer or mobile app
• Downloadable PDF of the course guidebook
• FREE video streaming of the course from our website and mobile apps
##### DVD Includes:
• 24 lectures on 4 DVDs
• 208-page printed course guidebook
• Downloadable PDF of the course guidebook
• FREE video streaming of the course from our website and mobile apps

### What Does The Course Guidebook Include?

##### Course Guidebook Details:
• 208-page printed course guidebook
• Equations, tables & diagrams
• Glossary

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Scott P. Stevens, Ph.D.
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...
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## Reviews

Mathematical Decision Making: Predictive Models and Optimization is rated 4.7 out of 5 by 52.
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
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 http://cob.jmu.edu/stevensp/analytics.htm 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
Rated 5 out of 5 by from Application of Math Concepts Math concepts that are applicable to life experiences are the ones that stick in your mind and spark the imagination. Mathematical Decision Making is one of those courses that answer the question that is often asked by many math students; "What am I going to use this math for?" The beauty of this course is that it is all about application. The presentation is inspiring in that the instructor brings you into the application and keeps the math as a lower priority. By the time you get half way through this course, you will have learned how to use those math skills in ways you never thought possible. Another inspiring feature of this course is that it presents some fresh ideas to math instructors on how to make math more fun and interesting. If you want to learn more about math and enjoy it, this is loaded with "Ah ha" moments.
Date published: 2015-04-15
Rated 4 out of 5 by from Excellent course, advanced level I have not yet finished the course, but the content is excellent and even exciting. The course is advanced level, and at least a rudimentary understanding of statistics is necessary. The instructor talks a bit fast for my southern ears. For the first time in the many great courses i have taken, I find it necessary to read the accompanying book to review. All said, i recommend this course highly.
Date published: 2015-03-07
Rated 5 out of 5 by from Another excellent course that is very useful This course is excellent as the Professor delivers a fast paced and interesting explanation of each topic with real-life examples! Fun!
Date published: 2015-02-27
Rated 5 out of 5 by from Outstanding Introduction to Operations Research! I have purchased 30 courses from The Great Courses. The majority of them are really good courses but Mathematical Decision Making: Predictive Models and Optimization raised the bar! Prof. Scott Stevens is an outstanding lecturer and has a great teaching style. The lectures are introductory and very practical explaining important OR fundamentals. The conceptual models explanations followed by their creation in a spreadsheet environment are perfect. I do strongly recommend this course. A great course!
Date published: 2015-02-27
Rated 5 out of 5 by from Nice and concise. The teacher pulls no punches with these lectures. He gets you to think practical and leaves you trying to get you to find more uses for yourself. Great for a thinker like me.
Date published: 2015-02-23
Rated 5 out of 5 by from Fantastic scope, variable depth Professor Stevens' knowledge and delivery are exemplary -- the pace is brisk but extremely well-structured allowing easily working through difficult areas or concepts. The graphics are exemplary. The really brilliant part, especially for such a short course, is his way of melding a survey-level coverage of the concepts with an inspiring amount of understanding, along with a post-lesson opportunity to travel deeper into the subject with simple and available tools, such as Excel. At the end of a lesson you have an understanding of the idea, a feeling for the underlying mathematics and its relevance, and with a little effort, hands-on experience with some very powerful tools. The subjects covered in this course are closely related but were distributed over perhaps five courses in my pass through college. Seeing all the concepts developed so concisely and with emphasis on intuitive understanding establishes an often-missing context and in some cases promotes options and synergies. To productively use what this course provides little beyond desire and some capability with Excel is required. Had someone told me that was possible before I took this course I'd not have believed them. In my opinion this is one of the Great Courses best.
Date published: 2015-02-22