Software Requirements

Learners will need access to Microsoft Excel in order to successfully work through content each course. Learners will need basic to intermediate knowledge of Excel in order to complete learning activities.

Is this course really 100% online? Do I need to attend any classes in person?

This course is completely online, so there’s no need to show up to a classroom in person. You can access your lectures, readings and assignments anytime and anywhere via the web or your mobile device.

Can I just enroll in a single course?

Yes! To get started, click the course card that interests you and enroll. You can enroll and complete the course to earn a shareable certificate. When you subscribe to a course that is part of a Specialization, you’re automatically subscribed to the full Specialization. Visit your learner dashboard to track your progress.

Is financial aid available?

Yes. In select learning programs, you can apply for financial aid or a scholarship if you can’t afford the enrollment fee. If fin aid or scholarship is available for your learning program selection, you’ll find a link to apply on the description page.

Can I take the course for free?

No, you cannot take this course for free. When you enroll in the course, you get access to all of the courses in the Specialization, and you earn a certificate when you complete the work. If you cannot afford the fee, you can apply for financial aid.

Will I earn university credit for completing the Specialization?

This Specialization doesn't carry university credit, but some universities may choose to accept Specialization Certificates for credit. Check with your institution to learn more.

Analytics for Decision Making Specialization

Analytics for Decision Making Specialization

Boost your career through business analytics. Learn the basics of predictive and prescriptive analytics to further your career options in the area of business analytics.

Instructors: Soumya Sen

Included with Coursera Plus

Learn more

4 course series

Get in-depth knowledge of a subject

4.7

(163 reviews)

Beginner level

Recommended experience

1 month at 10 hours a week

Flexible schedule

Earn a career credential

Share your expertise with employers

4 course series

Get in-depth knowledge of a subject

4.7

(163 reviews)

Beginner level

Recommended experience

1 month at 10 hours a week

Flexible schedule

Earn a career credential

Share your expertise with employers

What you'll learn

The field of analytics is typically built on four pillars: Descriptive Analytics, Predictive Analytics, Causal Analytics, and Prescriptive Analytics. Descriptive analytics (e.g., visualization, BI) deal with the exploration of data for patterns, predictive analytics (e.g., data mining, time-series forecasting) identifies what can happen next, causal modeling establishes causation, and prescriptive analytics help with formulating decisions. This specialization focuses on the Prescriptive Analytics (the final pillar). This specialization will review basic predictive modeling techniques that can be used to estimate values of relevant parameters, and then use optimization and simulation techniques to formulate decisions based on these parameter values and situational constraints. The specialization will teach how to model and solve decision-making problems using predictive models, linear optimization, and simulation methods.

What’s included

Shareable certificate

Add to your LinkedIn profile

Taught in English

48 practice exercises

Advance your subject-matter expertise

Learn in-demand skills from university and industry experts
Master a subject or tool with hands-on projects
Develop a deep understanding of key concepts
Earn a career certificate from University of Minnesota

Specialization - 4 course series

Introduction to Predictive Modeling

Course 112 hours4.8 (137 ratings)View course

What you'll learn

Welcome to Introduction to Predictive Modeling, the first course in the University of Minnesota’s Analytics for Decision Making specialization.

This course will introduce to you the concepts, processes, and applications of predictive modeling, with a focus on linear regression and time series forecasting models and their practical use in Microsoft Excel. By the end of the course, you will be able to: - Understand the concepts, processes, and applications of predictive modeling. - Understand the structure of and intuition behind linear regression models. - Be able to fit simple and multiple linear regression models to data, interpret the results, evaluate the goodness of fit, and use fitted models to make predictions. - Understand the problem of overfitting and underfitting and be able to conduct simple model selection. - Understand the concepts, processes, and applications of time series forecasting as a special type of predictive modeling. - Be able to fit several time-series-forecasting models (e.g., exponential smoothing and Holt-Winter’s method) in Excel, evaluate the goodness of fit, and use fitted models to make forecasts. - Understand different types of data and how they may be used in predictive models. - Use Excel to prepare data for predictive modeling, including exploring data patterns, transforming data, and dealing with missing values. This is an introductory course to predictive modeling. The course provides a combination of conceptual and hands-on learning. During the course, we will provide you opportunities to practice predictive modeling techniques on real-world datasets using Excel. To succeed in this course, you should know basic math (the concept of functions, variables, and basic math notations such as summation and indices) and basic statistics (correlation, sample mean, standard deviation, and variance). This course does not require a background in programming, but you should be familiar with basic Excel operations (e.g., basic formulas and charting). For the best experience, you should have a recent version of Microsoft Excel installed on your computer (e.g., Excel 2013, 2016, 2019, or Office 365).

Skills you'll gain

Microsoft Excel, Regression Analysis, Pivot Tables And Charts, Forecasting, Time Series Analysis and Forecasting, Predictive Modeling, Performance Metric, Statistical Methods, Data Transformation, Trend Analysis, Predictive Analytics, and Data Cleansing

Optimization for Decision Making

Course 213 hours4.8 (80 ratings)View course

What you'll learn

In this data-driven world, companies are often interested in knowing what is the "best" course of action, given the data. For example, manufacturers need to decide how many units of a product to produce given the estimated demand and raw material availability? Should they make all the products in-house or buy some from a third-party to meet the demand? Prescriptive Analytics is the branch of analytics that can provide answers to these questions. It is used for prescribing data-based decisions. The most important method in the prescriptive analytics toolbox is optimization. This course will introduce students to the basic principles of linear optimization for decision-making. Using practical examples, this course teaches how to convert a problem scenario into a mathematical model that can be solved to get the best business outcome. We will learn to identify decision variables, objective function, and constraints of a problem, and use them to formulate and solve an optimization problem using Excel solver and spreadsheet.

Skills you'll gain

Process Optimization, Mathematical Modeling, Linear Algebra, Microsoft Excel, Resource Allocation, Decision Making, Analytics, Business Mathematics, Solution Design, Business Modeling, Graphing, Data-Driven Decision-Making, Business Analytics, and Operations Research

Advanced Models for Decision Making

Course 313 hours4.8 (54 ratings)View course

What you'll learn

Business analysts need to be able to prescribe optimal solution to problems. But analytics courses are often focused on training students in data analysis and visualization, not so much in helping them figure out how to take the available data and pair that with the right mathematical model to formulate a solution. This course is designed to connect data and models to real world decision-making scenarios in manufacturing, supply chain, finance, human resource management, etc. In particular, we understand how linear optimization - a prescriptive analytics method - can be used to formulate decision problems and provide data-based optimal solutions. Throughout this course we will work on applied problems in different industries, such as:

(a) Finance Decisions: How should an investment manager create an optimal portfolio that maximizes net returns while not taking too much risks across various investments? (b) Production Decisions: Given projected demand, supply of raw materials, and transportation costs, what would be the optimal volume of products to manufacture at different plant locations? (c) HR Decisions: How many workers need to be hired or terminated over a planning horizon to minimize cost while meeting operational needs of a company? (c) Manufacturing: What would be the profit maximizing product mix that should be produced, given the raw material availability and customer demand? We will learn how to formulate these problems as mathematical models and solve them using Excel spreadsheet.

Skills you'll gain

Process Optimization, Mathematical Modeling, Microsoft Excel, Workforce Planning, Operations Research, Operations Management, Workforce Management, Business Analysis, Financial Modeling, Production Planning, Portfolio Management, Business Analytics, Decision Making, Supply Chain, Finance, Manufacturing Operations, Resource Allocation, Applied Mathematics, Analytics, and Investment Management

Simulation Models for Decision Making

Course 417 hours4.5 (58 ratings)View course

What you'll learn

This course is primarily aimed at third- and fourth-year undergraduate students or graduate students interested in learning simulation techniques to solve business problems.

The course will introduce you to take everyday and complex business problems that have no one correct answer due to uncertainties that exist in business environments. Simulation modeling allows us to explore various outcomes and protect personal or business interests against unwanted outcomes. We can model uncertainties by using the concepts of probability and stepwise thinking. Stepwise thinking allows us to break down the problem in smaller components, explore dependencies between related events and allows us to focus on aspects of problem that are prone to changes due to future uncertainties. The course will introduce you to advanced Excel techniques to model and execute simulation models. Many of the Excel techniques learned in the course will be useful beyond simulation modeling. We will learn both Monte Carlo simulation techniques where overall outcome is of primary interest and discrete event simulation where intermediate dependencies between related events might be of interest. The course will introduce you to several practical issues in simulation modeling that are normally not covered in textbooks. The course uses a few running examples throughout the course to demonstrate concepts and provide concrete modeling examples. After taking the course a student will be able to develop fairly advanced simulation models to explore fairly broad range of business environments and outcomes.

Skills you'll gain

Microsoft Excel, Probability Distribution, Simulations, Financial Modeling, Probability, Risk Modeling, Business Mathematics, Data Modeling, Analysis, Operations Research, Statistics, Statistical Analysis, Strategic Thinking, Business Modeling, and Complex Problem Solving

Earn a career certificate

Add this credential to your LinkedIn profile, resume, or CV. Share it on social media and in your performance review.

Instructors

Soumya Sen

University of Minnesota

11 Courses47,523 learners

De Liu

University of Minnesota

1 Course13,617 learners

Alok Gupta

University of Minnesota

1 Course7,514 learners

Offered by

University of Minnesota

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Frequently asked questions

The specialization should take an average learner approximately 4 months to complete, working an average of about 10 hours per week.

A learner with little or no business analytics experience will be able to follow the courses in the specialization. Learners will need access to Microsoft Excel in order to successfully follow and complete learning activities in the specialization. Learners will need basic to intermediate knowledge of Microsoft Excel. However, while this specialization is designed with learners new to field of prescriptive analytics in mind, learners who already have prior knowledge of other branches of analytics and data science will still find this specialization useful, if they are interested in applying analytics for the final stage of data-driven decision making.

There is no requirement to take the courses in a specific order. Learners may complete the Specialization's courses in any order and independently from one another. However, learners having limited prior experience in data analytics may find that taking the courses in sequence will optimize their learning experience of a new learning domain.

Learners will be able to model and solve decision-making problems using predictive models, linear optimization, and simulation methods. By the end of this specialization, learners will have completed four different types of projects using analytics, modeling, and simulation techniques to prescribe and inform decisions for a wide range of business problems.

Analytics for Decision Making Specialization

What you'll learn

Skills you'll gain

Tools you'll learn

What’s included

Advance your subject-matter expertise