Capstone: Create Value from Open Data from Coursera

The Capstone project is an individual assignment.

Participants decide the theme they want to explore and define the issue they want to solve. Their “playing field” should provide data from various sectors (such as farming and nutrition, culture, economy and employment, Education & Research, International & Europe, Housing, Sustainable, Development & Energies, Health & Social, Society, Territories & Transport). Participants are encouraged to mix the different fields and leverage the existing information with other (properly sourced) open data sets.

Deliverable 1 is the preliminary preparation and problem qualification step. The objectives is to define the what, why & how. What issue do we want to solve? Why does it promise value for public authorities, companies, citizens? How do we want to explore the provided data?

For Deliverable 2, the participant needs to present the intermediary outputs and adjustments to the analysis framework. The objectives is to confirm the how and the relevancy of the first results.

Finally, with Deliverable 3, the participant needs to present the final outputs and the value case. The objective is to confirm the why. Why will it create value for public authorities, companies, and citizens.

Assessment and grading: the participants will present their results to their peers on a regular basis. An evaluation framework will be provided for the participants to assess the quality of each other’s deliverables.

What's inside

Syllabus

Introduction and step 1 : Define the analysis framework

Module 1 in the Business Analytics capstone project provides you with a clear idea of how to successfully complete the ESSEC Business Analytics MOOC. It is dedicated to ensuring that you understand the objectives of the capstone project and lets you consult the datasets to be used for the project as well as examples of what the expected deliverable should look like and contain. Before beginning the project, you are advised to review two previous modules dealing with how to effectively structure and present your findings, and how to approach and explore datasets ("Foundation of Business Analytics", the wrap up of "Case Studies in Business Analytics with Accenture"). This module also gives you the opportunity to try out the preparation of deliverable 1 and receive non-graded feedback from your peers, thereby giving you an essential insight into how the other deliverables and peer review steps will work.

Required assignement 1: Define the analysis framework

The module 2 sets the assignment of preparing deliverable 1 – your analysis framework – for assessment by your peers.

Required feed back on Delivery 1:Define the analysis framework and preparation of deliverable 2

In Module 3 you will be involved in reviewing the deliverables of a minimum of 3 other students( if you can manage more than 3, then all the better!) as well as preparing deliverable 2

Practice for Deliverable 2

Module 4 enables you to submit your draft proposal for deliverable 2 to your peers for review and feedback.

Required assignement 2: Present the intermediary outputs and adjustments to the analysis framework

The module 5 sets the assignment of preparing deliverable 2 – Present the intermediary outputs and adjustments to the analysis framework – for assessment by your peers.

Required feedback for delivery 2 and preparation of delivery 3

In Module 6 you will be involved in reviewing the deliverables of a minimum of 3 other students( if you can manage more than 3, then all the better!) as well as preparing deliverable 3

Required Delivery 3: Present the final outputs and value case

The module 7 sets the assignment of preparing deliverable 3 – Present the final outputs and value case – for assessment by your peers.

Required feedback on Assignment 3: Present the final outputs and value case

In Module 8 you will be involved in reviewing the deliverables of a minimum of 3 other students ( if you can manage more than 3, then all the better!) as well as preparing deliverable 3

Good to know

Know what's good

, what to watch for

, and possible dealbreakers

Reinforces an existing foundation for intermediate learners

Provides hands-on labs and interactive materials

Develops professional skills or deep expertise in a particular topic or set of topics

Taught by instructors who are recognized in their field for their work in business analytics

Topics covered provide a strong foundation for beginners

Appropriate for learners with a background or interest in business analytics

Reviews summary

Open data capstone

According to learners, Create Value from Open Data is a capstone course that teaches learners to create value from open data. While content is characterized as insightful and pertinent, there are complaints about lack of engagement with classmates and instructors. Moreover, some students report that while the course claims to require only about 2 hours of work per week, to legitimately complete all work and assignments, students should expect to invest more like 8 to 12 hours per week.

Course content is well received by students.

"Great course"

"I think the contents of this specialization were interesting"

Students should expect to commit 8-12 hours a week to this course.

"Even if all tasks were supposed to be done in around 2 hours, it is definitely not possible to satisfy your classmates' expectations if you only invest 2 hours... it will be even difficult to satisfy them if you invest less than 8-12 hours per assignment."

Students were unsatisfied with engagement levels among both classmates and instructors.

"lack of interaction with both students (there weren't any doing the course at the same stage I was"

"no one ever replied to forum posts"

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 Capstone: Create Value from Open Data with these activities:

Review Statistical Concepts

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Refreshing your knowledge of statistical concepts will help you better understand the data analysis techniques used in the course.

Browse courses on Statistics

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Review your notes from previous statistics courses.
Go through online tutorials or textbooks on statistical concepts.
Read research papers or articles that use statistical methods.
Work on practice problems to reinforce your understanding.

Peer-to-Peer Data Analysis

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Engaging in peer-to-peer data analysis will allow you to share knowledge, learn from others, and enhance your analytical skills.

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Find a peer with similar interests.
Select a dataset for analysis.
Develop a plan for data analysis.
Work together to analyze the data.
Share your findings.

Machine Learning Algorithms Tutorial

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Following guided tutorials on machine learning algorithms will provide additional reinforcement and practice beyond the course materials.

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Find a tutorial on a specific machine learning algorithm.
Follow the steps in the tutorial to implement the algorithm.
Test the algorithm on a dataset.
Evaluate the performance of the algorithm.

Three other activities

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Data Preprocessing Practice

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Working through practice drills will help cement the concepts and techniques of data preprocessing covered in the course.

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Find a dataset with missing values and outliers.
Apply data cleaning techniques to remove or impute missing values.
Apply data transformation techniques to handle outliers.
Visualize the transformed data to check for errors.

Data Visualization Project

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Creating a data visualization project will allow you to apply the visualization techniques learned in the course to a real-world problem.

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Identify a data visualization problem.
Gather and explore the relevant data.
Choose appropriate data visualization techniques.
Create the visualizations.
Present your findings.

Contribute to Open-Source Data Analytics Projects

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Contributing to open-source data analytics projects allows you to gain hands-on experience, learn from others, and contribute to the community.

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Find open-source data analytics projects on platforms like GitHub.
Choose a project that aligns with your skills.
Read the project documentation.
Make contributions to the project's codebase.
Collaborate with other contributors.
Review pull requests.

Career center

Learners who complete Capstone: Create Value from Open Data will develop knowledge and skills that may be useful to these careers:

Project Manager

Project Managers plan and execute projects. They work with stakeholders to identify project goals and objectives, and they develop and implement project plans. This course can help you develop the skills you need to be a successful Project Manager. You will learn how to use data visualization tools to communicate your findings and how to use statistical techniques to analyze data. You will also learn how to work with different types of data, including structured and unstructured data.

See salaries and explore the career path for Project Manager

Data Scientist

Data Scientists use data to build models that can predict future events. They use a variety of statistical and machine learning techniques to develop these models. This course can help you develop the skills you need to be a successful Data Scientist. You will learn how to use data visualization tools to communicate your findings and how to use statistical techniques to analyze data. You will also learn how to work with different types of data, including structured and unstructured data.

See salaries and explore the career path for Data Scientist

Machine Learning Engineer

Machine Learning Engineers develop and deploy machine learning models. They use a variety of programming languages and tools to build these models. This course can help you develop the skills you need to be a successful Machine Learning Engineer. You will learn how to use data visualization tools to communicate your findings and how to use statistical techniques to analyze data. You will also learn how to work with different types of data, including structured and unstructured data.

See salaries and explore the career path for Machine Learning Engineer

Business Analyst

Business Analysts use data to help businesses make better decisions. They analyze data to identify trends, patterns, and insights. This course can help you develop the skills you need to be a successful Business Analyst. You will learn how to use data visualization tools to communicate your findings and how to use statistical techniques to analyze data. You will also learn how to work with different types of data, including structured and unstructured data.

See salaries and explore the career path for Business Analyst

Statistician

Statisticians use data to make inferences about the world. They design and conduct surveys, experiments, and other studies to collect data. This course can help you develop the skills you need to be a successful Statistician. You will learn how to use data visualization tools to communicate your findings and how to use statistical techniques to analyze data. You will also learn how to work with different types of data, including structured and unstructured data.

See salaries and explore the career path for Statistician

Data Analyst

Data Analysts use data to solve business problems. They collect, clean, and analyze data to identify trends, patterns, and insights. This course can help you develop the skills you need to be a successful Data Analyst. You will learn how to use data visualization tools to communicate your findings and how to use statistical techniques to analyze data. You will also learn how to work with different types of data, including structured and unstructured data.

See salaries and explore the career path for Data Analyst

Product Manager

Product Managers develop and manage products. They work with engineers, designers, and marketers to bring products to market. This course can help you develop the skills you need to be a successful Product Manager. You will learn how to use data visualization tools to communicate your findings and how to use statistical techniques to analyze data. You will also learn how to work with different types of data, including structured and unstructured data.

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Consultant

Consultants use their expertise to help organizations solve problems. They work with clients to identify problems and develop solutions. This course can help you develop the skills you need to be a successful Consultant. You will learn how to use data visualization tools to communicate your findings and how to use statistical techniques to analyze data. You will also learn how to work with different types of data, including structured and unstructured data.

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Data Engineer

Data Engineers build and maintain the infrastructure that stores and processes data. They use a variety of programming languages and tools to build and maintain these systems. This course can help you develop the skills you need to be a successful Data Engineer. You will learn how to use data visualization tools to communicate your findings and how to use statistical techniques to analyze data. You will also learn how to work with different types of data, including structured and unstructured data.

See salaries and explore the career path for Data Engineer

Quantitative Analyst

Quantitative Analysts use data to make investment decisions. They use a variety of statistical and econometric techniques to analyze data and make predictions. This course can help you develop the skills you need to be a successful Quantitative Analyst. You will learn how to use data visualization tools to communicate your findings and how to use statistical techniques to analyze data. You will also learn how to work with different types of data, including structured and unstructured data.

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Actuary

Actuaries use data to assess risk. They use a variety of statistical and mathematical techniques to analyze data and make predictions. This course can help you develop the skills you need to be a successful Actuary. You will learn how to use data visualization tools to communicate your findings and how to use statistical techniques to analyze data. You will also learn how to work with different types of data, including structured and unstructured data.

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Epidemiologist

Epidemiologists study the causes of disease. They use a variety of statistical and mathematical techniques to analyze data and make predictions. This course can help you develop the skills you need to be a successful Epidemiologist. You will learn how to use data visualization tools to communicate your findings and how to use statistical techniques to analyze data. You will also learn how to work with different types of data, including structured and unstructured data.

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Demographer

Demographers study population trends. They use a variety of statistical and mathematical techniques to analyze data and make predictions. This course can help you develop the skills you need to be a successful Demographer. You will learn how to use data visualization tools to communicate your findings and how to use statistical techniques to analyze data. You will also learn how to work with different types of data, including structured and unstructured data.

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Public Health Analyst

Public Health Analysts use data to improve the health of populations. They use a variety of statistical and mathematical techniques to analyze data and make predictions. This course can help you develop the skills you need to be a successful Public Health Analyst. You will learn how to use data visualization tools to communicate your findings and how to use statistical techniques to analyze data. You will also learn how to work with different types of data, including structured and unstructured data.

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Data Journalist

Data Journalists use data to tell stories. They use a variety of data visualization tools and techniques to communicate data to audiences. This course can help you develop the skills you need to be a successful Data Journalist. You will learn how to use data visualization tools to communicate your findings and how to use statistical techniques to analyze data. You will also learn how to work with different types of data, including structured and unstructured data.

See salaries and explore the career path for Data Journalist