Advanced Business Analytics Capstone

University of Colorado Boulder via Coursera

Go to Course: https://www.coursera.org/learn/data-analytics-business-capstone

Introduction

### Course Review: Advanced Business Analytics Capstone on Coursera **Course Overview** The **Advanced Business Analytics Capstone** offered on Coursera stands as the pinnacle of a comprehensive specialization designed to deepen your understanding and practical knowledge of business analytics. This course offers a meticulously structured approach to navigating the complexities of data analysis, predictive modeling, and decision-making processes that ultimately lead to enhanced business performance. Throughout this capstone experience, learners will engage in a practical project centered on analyzing financial loan data, equipping you with the analytical prowess to assist investment companies in making informed investment decisions. By progressing through the modules, participants not only refine their data analytics skills but also enhance their ability to present insights effectively. ### Course Structure and Syllabus Breakdown The course is organized into four well-defined modules, each focusing on critical aspects of the analytics process: #### **Module 1: Understand the Data and Prepare for Analysis** The initial module emphasizes the importance of data preparation, which is foundational to any successful analytics project. Participants will tackle challenges like handling missing values and outliers, equipping them with practical skills that can be applied in real-world scenarios. The emphasis on good data visualization principles ensures that data insights are communicated clearly. **Key Takeaway**: This module instills the essential skills of data cleaning and visualization, making it an invaluable starting point for any analytics project. #### **Module 2: Perform Predictive Analytics Tasks** In this module, learners will delve into predictive analytics, experimenting with various modeling techniques to classify loans and forecast losses from defaults. The course promotes an iterative approach to model tuning, fostering a strong understanding of the importance of performance evaluation through concepts like cross-validation. **Key Takeaway**: The hands-on experience with predictive modeling tools, such as XLMiner, equips participants with the technical know-how to handle complex datasets, making it essential for aspiring data analysts. #### **Module 3: Provide Suggestions for Allocating Investment Funds** The focus shifts to prescriptive analytics, where learners are tasked with deriving insights that inform investment strategies. Through techniques like clustering and simulation-based optimization, the module reinforces the significance of effective fund allocation for financial portfolios. **Key Takeaway**: This module synthesizes theoretical knowledge with practical applications, providing concrete insights relevant to investment decision-making processes. #### **Module 4: Present Your Analytics Results** The final module emphasizes the importance of communicating analytical results effectively. It guides participants in crafting impactful presentations, ensuring that they can clearly articulate their findings to clients or stakeholders. **Key Takeaway**: Mastering the art of presentation is crucial, and this module emphasizes communication strategies that enhance the clarity and impact of your analytical insights. ### Overall Experience and Recommendation The **Advanced Business Analytics Capstone** course is an excellent opportunity for learners who aspire to gain a comprehensive understanding of the analytic process in a business context. The capstone project not only allows for hands-on application of technical skills but also emphasizes the critical soft skills required for presenting data-driven insights. ### Who Should Take This Course? This course is ideal for: - Professionals seeking to enhance their business analytics skills. - Data enthusiasts aiming to apply their knowledge in real-world scenarios. - Individuals pursuing a career in data analysis, business intelligence, or investment strategy. ### Conclusion In summary, the **Advanced Business Analytics Capstone** is a well-designed course for any aspiring business analyst. Its structured approach to real-world data challenges, coupled with valuable insights into effective communication and decision-making, makes it a recommended choice for anyone looking to elevate their understanding of business analytics. If you are looking to solidify your analytics skills and make data-driven decisions confidently, this course warrants your consideration!

Syllabus

Module 1 - Understand the data and prepare your data for analysis

This week your goal is to understand the data and prepare the data for analysis. As we discussed in this specialization, data preprocessing and cleanup is often the first step in data analytics projects. Needless to say, this step is crucial for the success of this project. We've selected a few videos from Courses 2 and 4 for you to review before completing this week's assignments. Dealing With Missing Values and Dealing with Outliers videos will remind you how to perform preliminary data cleanups. The last part of the assignments ask you to construct data visualizations. You may find the ideas discussed in What is Good Data Visualization? and Graphical Excellence useful.

Module 2 - Perform predictive analytics tasks

This week you will perform some predictive analytics tasks, including classifying loans and predicting losses from defaulted loans. You will try a variety of tools and techniques this week, as the predictive accuracy of different tools can vary quite a bit. It is rarely the case that the default model produced by ASP is the best model possible. Therefore, it is important for you to tune the different models in order to improve the performance.This week’s assignments require you to build predictive models for both classification and regression tasks.

Before working on the assignments, you may review a few videos to remind yourself several important concepts, such as cross validation. These concepts are discussed in the videos Cross Validation and Confusion Matrix and Assessing Predictive Accuracy Using Cross-Validation. You may also find a refresher on XLMiner useful. The videos Building Logistic Regression Models using XLMiner and How to Build a Model using XLMiner discuss how to build logistic regression and linear regression models. Depending on your needs, you may also go back to the videos that discuss how to build trees and neural networks.

Module 3 - Provide suggestions on how to allocate investment funds using prescriptive analytics tools

This week we turn our attention to prescriptive analytics, where you will provide some concrete suggestions on how to allocate investment funds using analytics tools, including clustering and simulation-based optimization. You will see that allocating funds wisely is crucial for the financial return of the investment portfolio.

The relevant videos for this week are from Course 3: Week 1: Cluster analysis with XLMiner, Week 2: Adding uncertainty to spreadsheet model, Week 2: Defining output variables and analyzing results.

Module 4 - Present your analytics results to your clients

You have done a lot so far! In this last week, you will present to your analytics results to your clients. Since you have many results in your project, it is important for you to judiciously choose what to include in your presentation. Several videos in Course 4 offer some guidelines on communicating analytics results. This assignment will give you an opportunity to apply the skills you learned there. Good luck!