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Advanced Business Analytics Capstone

Approx. 19 hours to complete

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Course Summary

Learn how to use data analytics to drive business decisions and solve real-world problems in this capstone course.

Key Learning Points

Develop a business problem, gather and analyze data, and present findings to stakeholders
Use data visualization techniques to communicate insights and results
Apply statistical and machine learning techniques to make data-driven decisions

Learning Outcomes

Ability to identify and define business problems
Proficiency in data gathering, cleaning, and analysis
Expertise in data visualization and communication

Prerequisites or good to have knowledge before taking this course

Familiarity with statistics and data analysis
Knowledge of programming languages such as Python or R

Course Difficulty Level

Intermediate

Course Format

Online
Self-paced

Similar Courses

Business Analytics Capstone
Data Science Capstone
Applied Data Science Capstone

Related Education Paths

Notable People in This Field

Statistician, Author, and Founder of FiveThirtyEight
Data Scientist and Founder of Fast Forward Labs
Statistician and Creator of the R Programming Language

Related Books

Description

The analytics process is a collection of interrelated activities that lead to better decisions and to a higher business performance. The capstone of this specialization is designed with the goal of allowing you to experience this process. The capstone project will take you from data to analysis and models, and ultimately to presentation of insights.

In this capstone project, you will analyze the data on financial loans to help with the investment decisions of an investment company. You will go through all typical steps of a data analytics project, including data understanding and cleanup, data analysis, and presentation of analytical results. For the first week, the 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. In the second 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. Beginning in the third 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. In the last week, you are expected to present your analytics results to your clients. Since you will obtain many results in your project, it is important for you to judiciously choose what to include in your presentation. You are also expected to follow the principles we covered in the courses in preparing your presentation.

Outline

Module 1 - Understand the data and prepare your data for analysis
Data Cleanup and Transformation
Dealing with Missing Values
Dealing with Outliers
What is Good Data Visualization
Graphical Excellence
Introduction to the Project
Register for Analytic Solver Platform for Education (ASPE)

Module 2 - Perform predictive analytics tasks
Cross Validation and Confusion Matrix
Assessing Predictive Accuracy Using Cross-Validation
Building Logistic Regression Models using XLMiner
How to Build a Model using XLMiner

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

Module 4 - Present your analytics results to your clients

Summary of User Reviews

The Data Analytics Business Capstone course on Coursera has received positive reviews from many users. The course has been praised for its comprehensive curriculum and practical approach to data analytics. The overall rating of the course is high, with many users recommending it to others.

Key Aspect Users Liked About This Course

The course has been praised for its practical approach to data analytics.

Pros from User Reviews

Comprehensive curriculum
Practical approach to data analytics
Well-designed assignments and quizzes
Engaging and knowledgeable instructors
Excellent peer feedback system

Cons from User Reviews

Some users found the course material to be too advanced
Occasional technical issues with platform
Limited interaction with instructors
Not enough focus on data visualization
The pace of the course may be too fast for some learners

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