Advanced Business Analytics Capstone from Coursera

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.

What's inside

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!

Good to know

Know what's good

, what to watch for

, and possible dealbreakers

Teaches data analytics from data understanding to predictive and prescriptive analytics, preparing students to secure leadership roles in business and technology

Examines the entire analytics process, which is valuable for students hoping to get a holistic understanding of how analytics are used in real business settings

Builds a strong analytical foundation using R and Python

Suitable for a wide range of students, from those with no analytics experience to those with a foundation in data science or statistics

Provides hands-on training to strengthen students' analytical skills

Features lectures by renowned professors Manuel Laguna, David Torgerson, and Dan Zhang, enhancing credibility

Reviews summary

Frustrating analytics capstone

According to students, this capstone course is very poorly structured and assignments are unclear. Grading rubrics have many errors, which can lead to incorrect grading and frustration. Some assignments rely on a platform known as ASP, which is very slow and often crashes. Many learners feel as though they would have been better off using a programming language such as R or Python to complete these assignments. While other courses in the specialization were well received, this course left a negative impression for some learners.

ASP platform is slow and crashes frequently.

"The ASP is a joke. It is SLOOOOW."

"I can't believe anyone actually pays to use it."

Assignments are poorly written and lack clarity.

"Assignments are not clear."

Grading rubrics contain multiple errors.

"The grading rubrics have multiple errors."

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Advanced Business Analytics Capstone with these activities:

Review basic statistics concepts

Show steps

Brush up on basic statistics concepts to strengthen your foundation and improve your performance in this course.

Browse courses on Statistics

Show steps

Review probability distributions
Review hypothesis testing
Review regression analysis

Practice exercises on data visualization techniques

Show steps

Data visualization techniques are crucial for presenting findings. Practice with these techniques to better prepare for this course.

Show steps

Identify the data visualization technique
Practice using the technique
Evaluate the effectiveness of the visualization
Repeat the process for different techniques

Review 'Predictive Analytics'

Show steps

Reinforce the principles of predictive analytics with the leading text in the field.

View Predictive Analytics on Amazon

Show steps

Read the first two chapters.
Summarize the key concepts of predictive analytics.
Identify the different types of predictive models.

11 other activities

Expand to see all activities and additional details

Show all 14 activities

Practice using Excel

Show steps

Strengthen your proficiency in Excel to enhance your ability to analyze data.

Browse courses on Excel

Show steps

Create a new spreadsheet.
Enter data into the spreadsheet.
Apply basic formulas and functions.

Start a data cleaning project

Show steps

Complete a project where you clean data prior to analysis to better prepare you to perform the tasks of this course.

Browse courses on Exploratory Data Analysis

Show steps

Gather data from various sources
Remove duplicate data
Handle missing values
Clean errors and inconsistencies
Prepare data for analysis

Complete the Coursera Tutorial on Data Visualization

Show steps

Enhance your data visualization skills by following this Coursera tutorial.

Browse courses on Data Visualization

Show steps

Watch the video lectures.
Complete the interactive exercises.

Follow tutorials on machine learning algorithms

Show steps

Machine learning algorithms are central to predictive analytics. Study these algorithms by following tutorials outside the course.

Browse courses on Machine Learning Algorithms

Show steps

Identify the machine learning algorithm
Follow a tutorial on the algorithm
Implement the algorithm in a programming language
Test and evaluate the algorithm

Apply data preprocessing techniques

Show steps

Strengthen your ability to prepare data for analysis by completing practice exercises.

Browse courses on Data Preprocessing

Show steps

Download the course dataset.
Perform data cleaning tasks.
Apply data transformation techniques.

Participate in a learning group

Show steps

Enhance your understanding by discussing concepts with your peers.

Show steps

Join a learning group or create your own.
Meet with your group to discuss topics.

Review introductory statistics

Show steps

Ensure a solid foundation in statistics to enhance your understanding of the course material.

Browse courses on Statistics

Show steps

Review your lecture notes.
Read the assigned textbook chapters.
Complete practice questions.

Join a study group with other students

Show steps

Working with others can improve performance. Form a study group to work through course materials and reinforce learning.

Show steps

Find other students interested in forming a study group
Establish a meeting schedule and location
Review course materials together
Work on assignments and projects together
Provide support and encouragement to each other

Write a blog post on a data analytics topic

Show steps

Writing about data analytics will deepen your understanding and solidify your knowledge. Write a blog post on a topic related to the course.

Browse courses on Data Analytics

Show steps

Identify a data analytics topic
Research the topic
Write the blog post
Edit and proofread the blog post
Publish the blog post

Create a presentation on a data analytics project

Show steps

Create a presentation that summarizes a data analytics project and highlights key findings. This will enhance your communication skills.

Browse courses on Presentation Skills

Show steps

Identify a data analytics project
Collect and analyze data
Develop insights and recommendations
Create a presentation outline
Design and deliver the presentation

Design a data dashboard

Show steps

Demonstrate your ability to effectively communicate insights by designing a data dashboard.

Browse courses on Data Visualization

Show steps

Identify the key metrics.
Choose an appropriate data visualization format.
Create a prototype of your dashboard.

Career center

Learners who complete Advanced Business Analytics Capstone will develop knowledge and skills that may be useful to these careers:

Financial Analyst

Financial Analysts evaluate the financial performance of companies and make recommendations for investment. This course provides a deep understanding of financial data analysis and modeling techniques that are essential for success in this role. You will learn how to analyze financial statements, build financial models, and make investment decisions.

See salaries and explore the career path for Financial Analyst

Data Scientist

Data Scientists analyze large amounts of data to extract meaningful insights and help businesses make better decisions. This course provides a foundation in data analysis and modeling techniques that are essential for success in this role. You will learn how to clean and prepare data, build predictive models, and use prescriptive analytics to solve business problems.

See salaries and explore the career path for Data Scientist

Business Analyst

Business Analysts help businesses improve their performance by identifying and solving problems. This course provides a strong foundation in the analytics process, from data collection and analysis to model building and presentation. You will learn how to use data to make better decisions and improve business outcomes.

See salaries and explore the career path for Business Analyst

Operations Research Analyst

Operations Research Analysts use mathematical and analytical techniques to solve complex business problems. This course provides a strong foundation in the analytics process, from problem definition and data collection to model building and solution implementation. You will learn how to use data to improve decision-making and optimize business operations.

See salaries and explore the career path for Operations Research Analyst

Market Research Analyst

Market Research Analysts conduct research to understand consumer behavior and market trends. This course provides a strong foundation in data analysis and modeling techniques that are essential for success in this role. You will learn how to collect and analyze data, build predictive models, and make recommendations for marketing strategies.

See salaries and explore the career path for Market Research Analyst

Management Consultant

Management Consultants help businesses improve their performance by solving problems and developing strategies. This course provides a strong foundation in the analytics process, from problem definition and data collection to model building and solution implementation. You will learn how to use data to make better decisions and improve business outcomes.

See salaries and explore the career path for Management Consultant

Statistician

Statisticians collect, analyze, and interpret data to help businesses make better decisions. This course provides a strong foundation in statistical methods and techniques that are essential for success in this role. You will learn how to design and conduct research studies, analyze data, and draw meaningful conclusions.

See salaries and explore the career path for Statistician

Software Engineer

Software Engineers design, develop, and maintain software applications. This course provides a foundation in computer science and software engineering principles that are essential for success in this role. You will learn how to design and develop software programs, manage software projects, and ensure software quality.

See salaries and explore the career path for Software Engineer

Data Engineer

Data Engineers design and build the systems that store and process data. This course provides a foundation in data management and engineering techniques that are essential for success in this role. You will learn how to design and build data pipelines, manage data quality, and implement data security measures.

See salaries and explore the career path for Data Engineer

Quantitative Analyst

Quantitative Analysts use mathematical and statistical models to assess risk and make investment decisions. This course provides a strong foundation in financial modeling and risk management techniques that are essential for success in this role. You will learn how to build financial models, analyze risk, and make investment recommendations.

See salaries and explore the career path for Quantitative Analyst

Data Architect

Data Architects design and manage the data architecture for organizations. This course provides a foundation in data management and engineering principles that are essential for success in this role. You will learn how to design and implement data architectures, manage data quality, and ensure data security.

See salaries and explore the career path for Data Architect

Actuary

Actuaries use mathematical and statistical models to assess risk and set insurance premiums. This course provides a strong foundation in financial modeling and risk management techniques that are essential for success in this role. You will learn how to build financial models, analyze risk, and make insurance pricing decisions.

See salaries and explore the career path for Actuary

Risk Manager

Risk Managers identify and assess risks to organizations and develop strategies to mitigate those risks. This course provides a strong foundation in risk management principles and techniques that are essential for success in this role. You will learn how to identify and assess risks, develop risk mitigation strategies, and implement risk management programs.

See salaries and explore the career path for Risk Manager

Auditor

Auditors examine financial records to ensure that they are accurate and complete. This course provides a foundation in accounting and auditing principles that are essential for success in this role. You will learn how to conduct financial audits, assess financial risks, and report audit findings.

See salaries and explore the career path for Auditor

Tax Accountant

Tax Accountants prepare and file tax returns for individuals and businesses. This course provides a foundation in tax accounting principles and regulations that are essential for success in this role. You will learn how to prepare and file tax returns, calculate tax liability, and represent clients before tax authorities.

See salaries and explore the career path for Tax Accountant

Advanced Business Analytics Capstone

What's inside

Syllabus

Good to know

Save this course

Reviews summary

Frustrating analytics capstone

Activities

Career center

Reading list

Share

Similar courses