Predictive Modeling with Logistic Regression using SAS

Go to Course: https://www.coursera.org/learn/sas-predictive-modeling-using-logistic-regression

Introduction

### Course Review: Predictive Modeling with Logistic Regression using SAS **Overview:** In the realm of data analytics and predictive modeling, mastering the right tools and techniques is crucial. "Predictive Modeling with Logistic Regression using SAS" on Coursera stands out as an exceptional course designed for anyone keen on diving into the intricacies of predictive modeling with a focus on the LOGISTIC procedure of SAS/STAT software. This course not only delves into the theoretical aspects of logistic regression but also provides practical insights into its applications, making it a comprehensive learning experience. **Course Structure and Syllabus:** The course is structured into six well-defined modules, each meticulously crafted to build upon the previous one. 1. **Course Overview and Logistics:** This initial module sets the stage for your learning journey, providing clarity on the course structure and objectives while introducing you to the business scenario data that you'll be working with. 2. **Understanding Predictive Modeling:** Here, participants will explore the foundational principles of predictive modeling. This module highlights common analytical challenges faced in real-world scenarios, allowing learners to anticipate and prepare for potential hurdles. 3. **Fitting the Model:** This is where the magic of logistic regression begins. Learners will delve into the mechanics of the logistic regression model and how to effectively utilize the LOGISTIC procedure. You'll practice fitting the model, scoring new cases, and adjusting for oversampling, enhancing your confidence in real-world applications. 4. **Preparing the Input Variables, Part 1:** This module emphasizes the importance of clean and effective data preparation. You'll tackle common predictor variable issues such as missing values and redundant predictors, while also learning to manage nonlinear relationships, which is crucial for successful modeling. 5. **Preparing the Input Variables, Part 2:** The second installment in input variable preparation zeroes in on variable selection. By focusing on the most predictive variables, participants will sharpen their analytical skills, ultimately improving the accuracy of their models. 6. **Measuring Model Performance:** In the concluding module, you'll learn to assess the performance of your model critically. This includes determining allocation rules for maximizing profit and understanding how to create a family of models, enabling you to select the best option based on performance metrics. 7. **SAS Certification Practice Exam:** While the course aims primarily at practical skills, it also prepares learners for certification, providing a practice exam in statistical business analysis using SAS®9. **Recommendation:** If you're aspiring to enhance your predictive modeling skills, particularly through logistic regression, this course is highly recommended. It's suitable for beginners who are new to SAS as well as for seasoned professionals looking to refresh their skills in predictive analytics. The comprehensive syllabus ensures that you not only learn how to use SAS effectively but also understand the underlying concepts of predictive modeling which are universally applicable. The emphasis on practical applications via hands-on data scenarios alongside the theoretical insights is crucial for mastering logistic regression. Moreover, the course's structure allows for progressive learning, ensuring that each module builds on the last while reinforcing your understanding of essential concepts. Ultimately, "Predictive Modeling with Logistic Regression using SAS" equips you with the skills and knowledge to tackle complex data analysis projects and solve real-world analytical challenges, making it a valuable addition to your educational portfolio. Whether for professional development or personal interest, enrolling in this course is a step towards becoming proficient in predictive modeling and utilizing SAS effectively. Don’t miss the opportunity to expand your analytical capabilities and potentially influence business outcomes through data-driven decisions!

Syllabus

Course Overview and Logistics

In this module, you review the fundamentals of predictive modeling. Then you explore the business scenario data that is used throughout the course. Finally, you learn about common analytical challenges that you might encounter as a modeler.

In this module, you investigate the concepts behind the logistic regression model. Then you learn to use the LOGISTIC procedure to fit a logistic regression model. Finally, you learn how to score new cases and adjust the model for oversampling.

In this module, you learn how to deal with common problems with your predictor variables such as missing values, categorical predictors with many levels, a high number of redundant predictors, and nonlinear relationships with the response variable.

In this module, you learn how to select the most predictive variables to use in your model.

In this module, you learn how to assess the performance of your model and how to determine allocation rules that maximize profit. Finally, you learn how to generate a family of increasingly complex predictive models and how to select the best model.