How can you put data to work for you? Specifically, how can numbers in a spreadsheet tell us about present and past business activities, and how can we use them to forecast the future? The answer is in building quantitative models, and this course is designed to help you understand the fundamentals of this critical, foundational, business skill. Through a series of short lectures, demonstrations, and assignments, you’ll learn the key ideas and process of quantitative modeling so that you can begin to create your own models for your own business or enterprise. By the end of this course, you will have seen a variety of practical commonly used quantitative models as well as the building blocks that will allow you to start structuring your own models. These building blocks will be put to use in the other courses in this Specialization.
提供方
課程信息
學生職業成果
34%
35%
10%
您將獲得的技能
學生職業成果
34%
35%
10%
100% 在線
可靈活調整截止日期
完成時間大約為10 小時
英語（English）
教學大綱 - 您將從這門課程中學到什麼
Module 1: Introduction to Models
In this module, you will learn how to define a model, and how models are commonly used. You’ll examine the central steps in the modeling process, the four key mathematical functions used in models, and the essential vocabulary used to describe models. By the end of this module, you’ll be able to identify the four most common types of models, and how and when they should be used. You’ll also be able to define and correctly use the key terms of modeling, giving you not only a foundation for further study, but also the ability to ask questions and participate in conversations about quantitative models.
Module 2: Linear Models and Optimization
This module introduces linear models, the building block for almost all modeling. Through close examination of the common uses together with examples of linear models, you’ll learn how to apply linear models, including cost functions and production functions to your business. The module also includes a presentation of growth and decay processes in discrete time, growth and decay in continuous time, together with their associated present and future value calculations. Classical optimization techniques are discussed. By the end of this module, you’ll be able to identify and understand the key structure of linear models, and suggest when and how to use them to improve outcomes for your business. You’ll also be able to perform present value calculations that are foundational to valuation metrics. In addition, you will understand how you can leverage models for your business, through the use of optimization to really fine tune and optimize your business functions.
Module 3: Probabilistic Models
This module explains probabilistic models, which are ways of capturing risk in process. You’ll need to use probabilistic models when you don’t know all of your inputs. You’ll examine how probabilistic models incorporate uncertainty, and how that uncertainty continues through to the outputs of the model. You’ll also discover how propagating uncertainty allows you to determine a range of values for forecasting. You’ll learn the most-widely used models for risk, including regression models, tree-based models, Monte Carlo simulations, and Markov chains, as well as the building blocks of these probabilistic models, such as random variables, probability distributions, Bernoulli random variables, binomial random variables, the empirical rule, and perhaps the most important of all of the statistical distributions, the normal distribution, characterized by mean and standard deviation. By the end of this module, you’ll be able to define a probabilistic model, identify and understand the most commonly used probabilistic models, know the components of those models, and determine the most useful probabilistic models for capturing and exploring risk in your own business.
Module 4: Regression Models
This module explores regression models, which allow you to start with data and discover an underlying process. Regression models are the key tools in predictive analytics, and are also used when you have to incorporate uncertainty explicitly in the underlying data. You’ll learn more about what regression models are, what they can and cannot do, and the questions regression models can answer. You’ll examine correlation and linear association, methodology to fit the best line to the data, interpretation of regression coefficients, multiple regression, and logistic regression. You’ll also see how logistic regression will allow you to estimate probabilities of success. By the end of this module, you’ll be able to identify regression models and their key components, understand when they are used, and be able to interpret them so that you can discuss your model and convince others that your model makes sense, with the ultimate goal of implementation.
審閱
4.6
來自FUNDAMENTALS OF QUANTITATIVE MODELING的熱門評論
Very clear and articulate explanation of the concepts. He doesn't skip a step in the sequencing ideas, drawing comparisons and differences, and illustrating both visually and story-telling. Excellent.
The Course was easy to understand and pretty demonstrative as well. Although if the mathematics behind the equations derived were squeezed into the course briefly, it would have been of great value.
Very nice course for beginner, the mathematic level is not high (around french baccalaureat) so available to everyone. I enjoyed a lot this course that show how simple math can be used in real life.
The material is very helpful to get you up to speed quickly. If there could be more emphasis on walking one through the calculations and giving more examples, the course would be a perfect 5 stars.
Course was very interesting, However I wish there were easier ways to explain to details also the instruction , I would recommend to grab the students attention more to make it more understandable.
Course is having ultimate content regarding the understanding of Quantitative modeling and its applications. Having great explanation with examples of linear, power, exponential and log functions.
A great introductory course to quantitative modelling. Videos are very concise and clear with great examples. Course is well balanced between theory and applications. Overall a great experience.
Very good background to quantitative modelling. It gets a bit heavy on the mathematical formulas in places, but if you follow through, it helps cement understanding. Good speed/pace of material.
This is a good course for all of them who wish to work in this field and are unable to do so because of lack of core knowledge. The course helps to build this fundamental conceptual knowledge.
Just finished the course.Well designed and explained.The explanation of some equation could make it more understandable. I would have given it 5star if there was some exercise for practice.
This course helped me to understand, the main concepts about modeling with log, linear power and exponentials functions with very interesting examples of the real life, highly recommended.
I am exactly looking for this course since 7-8 months recently i come across and immedietly registered .\n\nI am going through the concepts covered here. Nice and elaborative. Thank you Dr
It's a very light introductory course, which does not go into the fine details of the modeling. However it does give a good survey of the fundamentals for quantitative business modeling.
Great professor! I enjoyed this course very much!\n\nThe only reason I'm giving 4/5 stars is because it wasn't as challenging as I would've thought. It was too simplified in my opinion.
It was a wonderful experience. The amount of knowledge have been able to acquired within the short while is quite unimaginable. I'm not the same person as at when I started the course.
This course has fundamentals of statistics. Course content is to the point and covers in depth using examples. However, brushing up of stats 101 is required before starting the course.
Great course if you already have background and exposure to statistical modeling. I could be challenging to pick up material quickly if you lack sufficient stats or quant background.
The course is extremely well crafted but brief, Don't expect a Full Fledged Analytics Deep dive rather a hawk eye understanding of the analytical business modelling methodologies.
Thorough introductions on Fundamental concepts is provided that builds upon further advanced applications with practical examples. All in all, a very useful start to the course !
This was an awesome course on quantitative modeling. The beauty of math in business modeling is clearly shown here. Very enthusiastic explanation. Thanks for such a great course!
提供方
宾夕法尼亚大学
The University of Pennsylvania (commonly referred to as Penn) is a private university, located in Philadelphia, Pennsylvania, United States. A member of the Ivy League, Penn is the fourth-oldest institution of higher education in the United States, and considers itself to be the first university in the United States with both undergraduate and graduate studies.
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