Predictive Modeling and Analytics

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

Introduction

**Course Review: Predictive Modeling and Analytics** Are you ready to elevate your data analytics skills and dive deep into the world of predictive modeling? Look no further than the "Predictive Modeling and Analytics" course offered on Coursera, the second course in the Data Analytics for Business specialization. This comprehensive course will equip you with the foundational knowledge and practical skills needed to leverage predictive analytics effectively. ### Overview The course begins by laying a strong groundwork in predictive analytics, focusing on widely used techniques and their core principles. Through a series of thoughtfully structured modules, you will explore various tools and methodologies that allow you to build statistical and machine learning models to make informed predictions based on the data available to you. By the end of this course, you will have a robust understanding of how to perform exploratory data analysis, identify suitable modeling techniques, and implement advanced predictive models. ### In-Depth Syllabus Breakdown #### 1. **Exploratory Data Analysis and Visualizations** In this initial module, you will learn to conduct effective exploratory data analysis (EDA) to derive insights and prepare datasets for predictive modeling. Key skills you will acquire include: - **Summary and Visualization:** Learn how to summarize datasets using appropriate tools and choose the right visualizations to convey insights effectively. - **Modeling Techniques:** Identify suitable modeling techniques for predicting both continuous and discrete outcomes. - **Tools:** Gain hands-on experience using Excel for EDA, performing common data preprocessing steps, and selecting the right graphs to display your findings. #### 2. **Predicting a Continuous Variable** Delve into regression techniques designed to predict the values of continuous variables, covering essential concepts such as: - **Predictive Modeling Foundations:** Explore topics like cross-validation, model selection, and the risks of overfitting. - **XLMiner Implementation:** Learn how to build predictive models using the XLMiner software tool, which will become an invaluable asset in your analytical toolkit. #### 3. **Predicting a Binary Outcome** This module shifts focus to logistic regression models that predict binary outcomes. You will dive into: - **Classification Concepts:** Understand key classification metrics such as confusion matrices, ROC curves, and cost-sensitive classification. - **Practical Application:** Build and validate classification models with XLMiner, providing practical, hands-on experience in the modeling process. #### 4. **Trees and Other Predictive Models** The final module covers advanced predictive models, including decision trees and neural networks. Essential takeaways include: - **Model Versatility:** Learn how both trees and neural networks can be applied to predict continuous or binary variables. - **XLMiner Modeling Skills:** Build sophisticated predictive models using XLMiner, equipping you with valuable skills for future analytical projects. ### Why You Should Enroll The "Predictive Modeling and Analytics" course is not just a learning experience; it is an investment in your professional skills. Whether you are a business analyst, data scientist, or someone looking to explore the data-driven decision-making landscape, this course offers: - **Practical Skills:** Gain hands-on experience with industry-standard software, empowering you to apply your learning in real-world scenarios. - **Comprehensive Coverage:** The course structure ensures that you build a solid foundation while also progressing to advanced topics, making it suitable for learners at various levels. - **Flexibility:** As an online course on Coursera, you can progress at your own pace, making it ideal for busy professionals or students. ### Conclusion In conclusion, the "Predictive Modeling and Analytics" course on Coursera is highly recommended for anyone eager to master the art of predictive analytics. The combination of theoretical knowledge and practical application makes this course a standout option. By completing this course, you will not only enhance your analytical skills but also open doors to new career opportunities in the ever-evolving field of data analytics. Don’t miss out on the chance to elevate your expertise—enroll today!

Syllabus

Exploratory Data Analysis and Visualizations

At the end of this module students will be able to: 1. Carry out exploratory data analysis to gain insights and prepare data for predictive modeling 2. Summarize and visualize datasets using appropriate tools 3. Identify modeling techniques for prediction of continuous and discrete outcomes. 4. Explore datasets using Excel 5. Explain and perform several common data preprocessing steps 6. Choose appropriate graphs to explore and display datasets

This module introduces regression techniques to predict the value of continuous variables. Some fundamental concepts of predictive modeling are covered, including cross-validation, model selection, and overfitting. You will also learn how to build predictive models using the software tool XLMiner.

This module introduces logistic regression models to predict the value of binary variables. Unlike continuous variables, a binary variable can only take two different values and predicting its value is commonly called classification. Several important concepts regarding classification are discussed, including cross validation and confusion matrix, cost sensitive classification, and ROC curves. You will also learn how to build classification models using the software tool XLMiner.

This module introduces more advanced predictive models, including trees and neural networks. Both trees and neural networks can be used to predict continuous or binary variables. You will also learn how to build trees and neural networks using the software tool XLMiner.