Introduction to Predictive Modeling

Go to Course: https://www.coursera.org/learn/introduction-to-predictive-modeling

Introduction

### Course Review: Introduction to Predictive Modeling In today’s data-driven world, the ability to analyze and predict future trends using past data is an invaluable skill. The "Introduction to Predictive Modeling" course offered by the University of Minnesota on Coursera is a fantastic starting point for anyone looking to break into the field of analytics and data science. This course serves as the first installment of the broader "Analytics for Decision Making" specialization, and it lays a solid foundation for understanding predictive modeling concepts, particularly linear regression and time series forecasting. ### Course Overview **Course Structure:** The course is structured into four comprehensive modules, each designed to build upon the knowledge gained in the previous one: 1. **Simple Linear Regression:** The course starts with the basics of predictive modeling, focusing on simple linear regression. The use of visual tools helps demystify concepts like the structure of regression models and Ordinary Least Squares (OLS). By employing Microsoft Excel's built-in tools such as trendlines and the Regression tool, learners are guided through the creation of their own models and making predictions. 2. **Multiple Linear Regression:** Building on the concepts from the first module, this week delves into multiple linear regression, emphasizing its versatility and broad applications. The course addresses potential pitfalls such as overfitting and underfitting, providing essential insights into creating robust models. The use of Excel's regression tools for fitting models and the introduction of backward elimination techniques make this section particularly valuable for those seeking to refine their modeling skills. 3. **Data Preparation:** Effective data preparation is crucial for successful predictive modeling. In this module, students learn about the types of variables, including categorical and datetime values, and gain practical skills in handling data with tools such as Pivot Tables, VLOOKUP, and more. The discussion surrounding multicollinearity and techniques for managing missing values are additional highlights that equip learners with the skills to make their datasets model-ready. 4. **Time Series Forecasting:** The final module transitions into time series forecasting, addressing the unique challenges associated with temporal data. The course covers various methods for analyzing stationary and trending data, including techniques like Holt-Winters' method. Students walk away with the ability to apply these forecasting methods in Excel, enhancing the applicability of their skill set in real-world scenarios. ### Course Benefits - **Hands-On Experience:** Each module emphasizes practical application, allowing students to work directly in Microsoft Excel. This hands-on approach reinforces learning and ensures that theoretical concepts are grounded in real-world utility. - **Comprehensive Instruction:** The sequential progression from simple to complex models enables learners to build their knowledge incrementally. The course also addresses common challenges in predictive modeling, equipping participants with strategies to navigate these issues effectively. - **Expert Guidance:** The instructional quality is high, with experienced educators guiding learners through both the theory and practice of predictive modeling. Their support fosters a conducive learning environment. ### Recommendation I highly recommend the "Introduction to Predictive Modeling" course to anyone interested in analytics, whether you are a complete beginner or someone with some experience seeking to solidify your knowledge. The course is versatile and can benefit professionals across various fields, such as finance, marketing, health care, and beyond. In summary, this course not only provides valuable knowledge about predictive modeling methodologies but also empowers participants to utilize Microsoft Excel for making data-driven decisions. By the end of the course, you'll be well-equipped to tackle predictive modeling challenges and contribute meaningfully to your organization's data analytics efforts. If you're ready to enhance your analytical skills and take your first steps into the world of predictive modeling, enroll in this Coursera course today!

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.

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.

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.

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.