Introduction to Predictive Modeling from Coursera

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).

What's inside

Syllabus

Week/Module 1: Simple Linear Regression

This module provides a brief overview of predictive modeling problems, illustrating their broad applications. It then focuses on the simplest form of predictive models: simple linear regression. The module follows a graphical approach to illustrate the structure of a simple linear regression model, the intuition for Ordinary Least Squares, and related concepts. Finally, we demonstrate how to use various Excel tools, including trendlines, the Regression tool, and the Trend() function, to fit a simple linear regression model and use it to form predictions.

Week/Module 2: Multiple Linear Regression

Building on Week 1, in this week we introduce multiple linear regression and its broad applications. Then, we cover how to fit a multiple linear regression model using Excel’s Regression tool and Trend() function and use the resulting model for predictions. The module further discusses the overfitting/underfitting problems and the basic principles of a good regression model. The module also introduces one approach for selecting a good model: backward elimination that can be implemented in Excel.

Week/Module 3: Data Preparation

In this week, we will learn how to prepare a dataset for predictive modeling and introduce Excel tools that can be leveraged to fulfill this goal. We will discuss different types of variables and how categorical, string, and datetime values may be leveraged in predictive modeling. Furthermore, we will discuss the intuition for including high-order and interaction variables in regression models, the issue of multicollinearity, and how to handle missing values. We will also introduce several handy Excel tools for data handling and exploration, including Pivot Table, IF() function, VLOOKUP function, and relative reference.

Week/Module 4: Time Series Forecasting

This module focuses on a special subset of predictive modeling: time series forecasting. We discuss the nature of time-series data and the structure of time series forecasting problems. We then introduce a host of time series models for stationary data and data with trends and seasonality, with a focus on techniques that are easily implemented within Excel, including moving average, exponential smoothing, double moving average, Holt’s method, and Holt-Winters’ method. The module also covers linear-regression-based forecasting and a composite forecasting technique for boosting accuracy.

Good to know

Know what's good

, what to watch for

, and possible dealbreakers

Provides a gentle introduction to predictive modeling, making it ideal for beginners

Emphasizes practical application of predictive models in Microsoft Excel, enhancing its relevance

Covers various types of predictive models, including linear regression and time series forecasting

Incorporates real-world datasets and hands-on exercises, fostering practical understanding

Suitable for individuals with basic math and statistics knowledge, making it accessible to a wide audience

Requires familiarity with basic Excel operations but does not necessitate programming knowledge

Reviews summary

Introduction to predictive modeling: hands-on

Learners say this course on predictive modeling is well-received and largely positive with engaging assignments. The course is described as well-paced with a clear and logical structure. According to students, the instructor does an amazing job presenting the material. Many remark on the number of practical examples which they feel helps them understand the concepts of predictive modeling.

Highly rated instructor.

""Best instructor""

"“The professor presents very well and easy to follow.”"

"“De Liu did a wonderful job of explaining the concepts”"

Organized, logical structure.

"“This course is amazing. very well structured and logical teaching sequence and explaination.”"

"“Best course structure, very practical, the professor presents very well and easy to follow.”"

"“Great course, good topic material and examples and well taught. Overall it was useful and relevant.”"

Numerous, helpful examples.

"“I really like how there were lots of examples for us to practice on.”"

"“This course did a great job of covering many topics and explaining their applications so that you can use the tools in real world scenarios.”"

"“This course is the basic of business analytic course but not easy for me because my English is not good. Instructor De Liu teach easy to listen ,clearly and very helpfully for choosing accuracy data analytic model with demonstrated examples."

May be challenging for some.

"“I found that the course exams questions were difficult to read for a dyslexic because of the cell format and font”"

"“I thought the week 4 coursework was substantially more difficult than the prior three weeks, so be prepared for that.”"

"“It was great until the last week, making you interpret excell formulas instad of calculating values makes it harder in an useless way."

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Introduction to Predictive Modeling with these activities:

Identify mentors in the field of predictive modeling

Show steps

Seek out individuals with experience and expertise in predictive modeling who can provide guidance, advice, and ongoing support.

Browse courses on Predictive Modeling

Show steps

Attend industry events and conferences to network with potential mentors.
Reach out to professors or industry professionals through LinkedIn or email.

Review basic math concepts

Show steps

Refresh your understanding of basic math concepts such as variables, functions, and notations to strengthen your foundation for predictive modeling.

Browse courses on Math

Show steps

Review notes from a previous math course or textbook.
Practice solving basic math problems involving variables and functions.

Review basic statistics concepts

Show steps

Sharpen your understanding of basic statistics concepts such as correlation, sample mean, standard deviation, and variance to enhance your comprehension of predictive modeling techniques.

Browse courses on Statistics

Show steps

Review notes from a previous statistics course or textbook.
Practice calculating basic statistics measures from given datasets.

Five other activities

Expand to see all activities and additional details

Show all eight activities

Read 'Introduction to Statistical Learning'

Show steps

Gain a deeper understanding of predictive modeling concepts by reading 'Introduction to Statistical Learning,' a comprehensive resource that covers fundamental principles and advanced techniques.

View An Introduction to Statistical Learning: with... on Amazon

Show steps

Read the first few chapters to grasp the basics of predictive modeling.
Focus on chapters related to linear regression and time series forecasting.

Solve practice problems on linear regression

Show steps

Reinforce your understanding of linear regression by solving practice problems that cover fitting models, interpreting results, and making predictions.

Browse courses on Linear Regression

Show steps

Find practice problems online or in textbooks.
Solve the problems step-by-step, showing your work.

Follow tutorials on time series forecasting

Show steps

Enhance your skills in time series forecasting by following online tutorials that demonstrate different forecasting techniques and their applications.

Browse courses on Time Series Forecasting

Show steps

Search for tutorials on exponential smoothing, Holt's method, and Holt-Winters' method.
Follow the tutorials step-by-step and apply the techniques to real-world datasets.

Build a predictive model for a real-world dataset

Show steps

Apply your knowledge by building a predictive model for a real-world dataset, which will provide hands-on experience and enhance your understanding of the entire modeling process.

Browse courses on Predictive Modeling

Show steps

Choose a dataset and define the prediction goal.
Prepare the data and explore patterns.
Select and fit a predictive model.
Evaluate the model's performance and make predictions.

Write a report on the predictive model

Show steps

Solidify your understanding of the predictive model by writing a report that documents the project, including the dataset used, modeling techniques employed, and the results obtained.

Browse courses on Predictive Modeling

Show steps

Organize the report into sections covering the project overview, data preparation, model selection, evaluation, and key findings.
Write clearly and concisely, using appropriate statistical terminology and visualizations.

Career center

Learners who complete Introduction to Predictive Modeling will develop knowledge and skills that may be useful to these careers:

Data Scientist

A Data Scientist is responsible for collecting, manipulating, and analyzing data to find meaningful insights and patterns that empower making informed decisions. The Introduction to Predictive Modeling course can be valuable to a Data Scientist because it provides a solid foundation in the concepts and applications of predictive modeling, including data preparation, linear regression, and time series forecasting. These techniques are widely used by Data Scientists to build predictive models that uncover hidden trends, predict future outcomes, and drive data-driven decision-making.

See salaries and explore the career path for Data Scientist

Machine Learning Engineer

Machine Learning Engineers are experts in designing, developing, and deploying machine learning models to solve complex problems. The Introduction to Predictive Modeling course can be useful for a Machine Learning Engineer by providing an understanding of the fundamental principles and applications of predictive modeling. The course covers linear regression, time series forecasting, and data preparation techniques, which are essential for building and evaluating machine learning models.

See salaries and explore the career path for Machine Learning Engineer

Business Analyst

Business Analysts specialize in analyzing business processes, identifying pain points, and recommending solutions to improve operational efficiency and performance. The Introduction to Predictive Modeling course may be helpful for a Business Analyst by providing them with an understanding of data analysis techniques, data preparation, and predictive modeling. These skills can be applied to analyze business data, identify trends and patterns, and develop data-driven recommendations for business improvement.

See salaries and explore the career path for Business Analyst

Quantitative Analyst

Quantitative Analysts use mathematical and statistical models to analyze financial data and make investment decisions. The Introduction to Predictive Modeling course can be beneficial for a Quantitative Analyst by providing a foundation in predictive modeling techniques, including linear regression and time series forecasting. These techniques are widely used in financial modeling and analysis to predict market trends, assess risk, and optimize investment portfolios.

See salaries and explore the career path for Quantitative Analyst

Data Analyst

Data Analysts use their expertise in data collection, analysis, and interpretation to uncover meaningful insights from data. The Introduction to Predictive Modeling course may be useful for a Data Analyst as it provides an understanding of data preparation techniques, linear regression, and time series forecasting. These skills empower Data Analysts to build predictive models that can identify patterns, forecast trends, and make data-informed decisions.

See salaries and explore the career path for Data Analyst

Market Research Analyst

Market Research Analysts gather and analyze data to understand market trends, customer behavior, and industry dynamics. The Introduction to Predictive Modeling course can be helpful for a Market Research Analyst as it provides an understanding of data analysis techniques and predictive modeling. The course covers linear regression and time series forecasting, which can be used to identify market trends, forecast demand, and optimize marketing strategies.

See salaries and explore the career path for Market Research Analyst

Financial Analyst

Financial Analysts use their expertise in financial data to analyze financial performance, make investment decisions, and advise clients on financial matters. The Introduction to Predictive Modeling course may be useful for a Financial Analyst by providing an understanding of data analysis techniques and predictive modeling. The course covers linear regression and time series forecasting, which can be used to analyze financial data, forecast future financial performance, and make informed investment recommendations.

See salaries and explore the career path for Financial Analyst

Operations Research Analyst

Operations Research Analysts are responsible for applying analytical techniques to solve complex business problems and improve operational efficiency. The Introduction to Predictive Modeling course may be helpful for an Operations Research Analyst as it provides an understanding of data analysis techniques and predictive modeling. The course covers linear regression and time series forecasting, which can be used to analyze operational data, identify inefficiencies, and optimize operational processes.

See salaries and explore the career path for Operations Research Analyst

Risk Analyst

Risk Analysts identify, assess, and mitigate risks in various domains, including finance, insurance, and healthcare. The Introduction to Predictive Modeling course may be useful for a Risk Analyst as it provides an understanding of data analysis techniques and predictive modeling. The course covers linear regression and time series forecasting, which can be used to analyze risk data, assess risk exposure, and develop risk management strategies.

See salaries and explore the career path for Risk Analyst

Insurance Analyst

Insurance Analysts use their understanding of insurance products and risk assessment to evaluate and underwrite insurance policies. The Introduction to Predictive Modeling course may be helpful for an Insurance Analyst by providing an understanding of data analysis techniques and predictive modeling. The course covers linear regression and time series forecasting, which can be used to analyze insurance data, assess risk exposure, and develop pricing strategies.

See salaries and explore the career path for Insurance Analyst

Econometrician

Econometricians use statistical and mathematical models to analyze economic data and test economic theories. The Introduction to Predictive Modeling course may be helpful for an Econometrician as it provides an understanding of data analysis techniques and predictive modeling. The course covers linear regression and time series forecasting, which can be used to analyze economic data, test economic theories, and forecast economic outcomes.

See salaries and explore the career path for Econometrician

Actuary

Actuaries use mathematical and statistical models to assess risk and uncertainty in the insurance and finance industries. The Introduction to Predictive Modeling course may be helpful for an Actuary as it provides an understanding of data analysis techniques and predictive modeling. The course covers linear regression and time series forecasting, which can be used to analyze insurance and financial data, assess risk exposure, and develop pricing and risk management strategies.

See salaries and explore the career path for Actuary

Statistician

Statisticians use statistical methods and models to collect, analyze, interpret, and present data. The Introduction to Predictive Modeling course may be helpful for a Statistician as it provides an understanding of data analysis techniques and predictive modeling. The course covers linear regression and time series forecasting, which can be used to analyze data, draw meaningful conclusions, and make informed predictions.

See salaries and explore the career path for Statistician

Data Management Analyst

Data Management Analysts are responsible for planning, implementing, and managing data management systems and processes. The Introduction to Predictive Modeling course may be helpful for a Data Management Analyst as it provides an understanding of data analysis techniques and data preparation. The course covers data exploration, data cleaning, and data transformation, which are essential skills for managing and preparing data for predictive modeling.

See salaries and explore the career path for Data Management Analyst

Database Administrator

Database Administrators are responsible for managing and maintaining databases, ensuring data integrity and availability. The Introduction to Predictive Modeling course may be helpful for a Database Administrator as it provides an understanding of data management principles and practices. The course covers data modeling, data normalization, and data security, which are important concepts for designing and managing databases that support predictive modeling applications.

See salaries and explore the career path for Database Administrator