Individual assignment Minor Data-Driven Decision Making

Objective: Enhance your understanding and application of data driven decision making through a curated selection of videos, leading to the development of a minimum viable data product. And maybe even into your own data business!

Overview

We have carefully selected an extensive collection of videos to deepen your knowledge in various key areas of data driven decision making, including machine learning, artificial intelligence, data analysis, visualization, application development, cutting-edge technologies, specialized applications, and quality assurance.

Assignment Details

Exploration: Begin your journey by exploring our curated collection of videos.
Selection: Choose at least one video that resonates with your interests or curiosities. This video will serve as the cornerstone of your exploration journey.
In-depth Study: Dive deep into the topic of the selected video. Understand the methodologies, technologies, and concepts discussed. This phase is crucial for building a solid foundation for your project.
Identify a Real-world Problem: Inspired by the suggestion to make this assignment more applied, we encourage you to identify a real-world problem. This problem could be something you've encountered during an internship, in your studies, or even in your personal life. The problem should be relevant to the themes covered in your selected video and should be solvable using the methodologies that are discussed in the video.
Develop a Data Product: Utilizing the knowledge and insights gained from your selected video and in-depth study, develop a minimum viable data product that addresses the identified problem. Your data product could be a model, an application, a visualization, or any other form of data-driven solution that offers a practical resolution to the problem.
Presentation: Conclude your assignment by preparing a presentation of your data product. This presentation should not only showcase your final product but also detail the journey you undertook to get there, including how the video inspired your approach, the problem-solving process, and the application of learned methodologies.

Deliverables

A concise report on your exploration journey, including the rationale behind your video selection and an in-depth analysis of topics covered in the chosen video.
A brief description of the identified problem and why it is significant.
Documentation of your data product development process, including data sources, methodologies, and technologies used.
A short video (max. 15 minutes) containing your final presentation. The video should be uploaded to YouTube and the link should be shared with the instructors. If you do not want to make your video publicly available, you can set it to unlisted.

This assignment is an opportunity to not only deepen your understanding of data driven decision making but also to apply your skills in a meaningful way. We encourage creativity, critical thinking, and a problem-solving mindset throughout this journey. Good luck!

Agile Tips for MVP Development

Embarking on your individual Minimum Viable Product (MVP) project with Agile methodologies presents a unique set of challenges and opportunities for growth. By focusing on flexibility, continuous improvement, and customer satisfaction, Agile principles will guide you through. Below are essential tips designed to help you navigate and excel in your MVP development journey

Develop your MVP in small, manageable tasks, emphasizing core functionalities through short, iterative cycles. Use the MoSCoW method to prioritize tasks as "Must have," and visualize your workflow on a Kanban board (with tools like Trello or Github) to track progress across "To Do," "In Progress," and "Done" stages.
Regularly solicit feedback from potential users and engage in discussions with sponsors (e.g., industry experts, potential customers) on business feasibility to ensure your MVP meets real user needs and achieves market fit. Share progress and challenges, seeking expertise from peers across disciplines. Collaborative cross-creation and sponsor involvement enrich the MVP's development and validation, making it more market-ready.
Conduct sprint reviews and retrospectives to assess progress and identify areas for improvement. Ensure every feature delivers value to end-users, continuously validating your MVP against real user feedback. This cycle of reflection, review, and user-focused development ensures that the MVP evolves in alignment with user needs and market demands.
Keep documentation concise, focusing on key decisions, changes, and learnings. This approach ensures that essential information is captured without bogging down the process with excessive reading material.
Manage your time and tasks proactively, adapting to insights and challenges as they arise. This self-driven approach is crucial for navigating the uncertainties and rapid changes inherent in MVP development.

Grading

The focus of this assignment lies specifically on the development of minimum viable data products. It emphasizes the lean startup approach, focusing on rapid prototyping, validating learning, user feedback, and iterative design. Below are the criteria for evaluating the creation and development of MVPs in the context of data products.

The criteria are designed to be applicable across a wide range of MVP developments, focusing on the strategic, user-centric, and iterative nature of creating viable data products with technology. This approach encourages a holistic view of product development, highlighting the importance of market alignment, user feedback, and technological robustness.

Concerns: areas that need work.	Expected standards.	Advanced: evidence of exceeding standards.	Weight
Score: 1	Score: 2	Score: 3
	Value Proposition and Market Validation: Clearly defines the value proposition of the MVP for a data product, including its potential market fit. The proposition is supported by market exploration, user needs assessment, and preliminary validation efforts. Emphasizes the importance of solving a real problem or addressing a specific need with the data product.		20%
	Design and Feasibility: Demonstrates the application of appropriate tools and methodologies in the design and development of the MVP. Justifies the choice of technologies and methodologies with a focus on feasibility, cost-effectiveness, and speed. The implementation strategy should reflect an understanding of MVP principles, aiming to quickly test hypotheses and gather user feedback.		25%
	User Experience and Interaction Design: Focuses on creating an MVP with user-centric design, ensuring ease of use, and facilitating quick user feedback. The approach to UI/UX design should be justified, emphasizing simplicity and functionality that meets user needs with minimal features.		25%
	Iterative Development Process and Feedback Loop: Reflects on the iterative development process of the MVP, including how user feedback was solicited, analyzed, and integrated into successive iterations. Highlights the adaptability of the development process and the ability to pivot based on insights gained from early adopters.		15%
	Technical Quality and Documentation: Evaluates the technical robustness and scalability potential of the MVP. Includes documentation, and the setup of a testing framework. Emphasizes the importance of a well-documented and maintainable techhnology stack that allows for easy updates and scaling based on user feedback and growing demands.		15%

Rubric

3 = Standards are exceeded, excellent performance
2 = Most of the expected standards are met, along with a very good performance
1 = Although some of the expected standards are met, the performance could be significantly improved

If two or more criteria fall below the expected standards, the final score will automatically be set to 1.

Projects overview

Below you will find a selection of videos that you can use as a starting point for your exploration journey. You can use the pagination buttons to navigate through the videos. Each video is accompanied by a brief description to help you decide which video to explore further.