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Design Thinking: How to link methodology and Data Science?

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Design Thinking: How to link methodology and Data Science?

Design Thinking is a framework created by IDEO to support design projects. However, this methodology can also be applied to Data Science. Find out how design principles can benefit data science projects...

At first glance, design and data science seem to be worlds apart. One is associated with art and creativity, the other with scientific and mathematical rigor. Yet the two disciplines can be complementary…

The principles of the design process, also known as “design thinking”, can help reveal the full potential of Data Science. They offer a methodology for tackling data-related challenges. Similarly, human-centered design helps to ensure that the insights gained from data analysis are actionable and relevant.

What are the challenges of Data Science?

In general, all projects start with an assessment of available resources. In the case of Data Science projects, this means analyzing the available data in an attempt to uncover relevant information.

Similarly, the design of a new product is often based on existing solutions. This approach is favored, rather than investigating why the new product is necessary.

However, this approach is problematic. It means rushing to find answers, without even knowing what the questions are. Even when the data reveals information, it’s hard to know how to relate it to the project, and whether it will really be useful.

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When should you use Design Thinking?

Design Thinking is a methodology created to tackle ambiguous and ill-defined problems. It involves using the designer’s strengths, such as empathy and experimentation, to find innovative solutions.

Popularized by design firm IDEO, Design Thinking is an iterative, human-centered process. This framework breaks down into six stages: empathy, definition, conception, prototyping, testing and implementation.

Starting from a problem with many unknowns, this progressive framework identifies a well-defined solution step by step. Along the way, the level of confidence and sophistication increases.

What design principles are useful in Data Science?

There are many design principles that can be very useful for Data Science. Here are just a few examples.
First of all, like design, Data Science can be human-centered. It’s worth remembering that humans are the real beneficiaries of any information derived from data.

By taking the time to understand stakeholders, their objectives and their frustrations, it is possible to identify the actions to be taken and the decisions to be made, and to anticipate their impact.

Furthermore, designers are conditioned not to think about solutions until they have succeeded in formulating the problem and understanding its domain.

As Tim Brown writes in his book “Change by Design: How Design Thinking Transforms Organisations and Inspires Innovation”: “there’s nothing more frustrating than finding the right answer to the wrong question”.

In the same way, a Data Science problem needs to be framed. By narrowing down the list of hypotheses and questions to be answered with data, it is possible to extract relevant information.

Another design method is to forget your preconceived ideas and prejudices about a field, in order to think freely and differently. This allows ideas to flow freely, before choosing the most appropriate one.

In Data Science, this approach helps to avoid getting bogged down in technical limitations, at the risk of crowding out the best potential solutions. The optimum solution can then be chosen, taking into account requirements and feasibility.

Rapid visual prototyping is a popular design technique. This is because it’s easier to react to visual and interactive elements than to verbal descriptions of concepts and ideas.

A simple graphic is worth thousands of words, and enables more intuitive exchanges with stakeholders. The same principle applies to Data Science, which is why DataViz or data visualization is so widely used.

In UX design, piling up tools and options is no guarantee of quality. On the contrary, it can unnecessarily weigh down the user experience.

Similarly, a report or dashboard based on data analysis should be as minimalist and intuitive as possible. It’s best to create a light, understandable narrative through which customers can be guided. Conversely, a report that is too heavy-handed can be confusing.

The best way to apply Design Thinking to a Data Science project is simply to integrate a designer into the team of Data Scientists.

By bringing his or her creative and atypical way of thinking, this expert can harmoniously complement the logical mind of the scientists…

How do I take a Data Science course?

As you can see, Design Thinking is very useful in Data Science, and designers can be a valuable asset to data science teams. As a designer, you can take a Data Science training course with DataScientest.

Our Data Analyst, Data Engineer and Data Scientist training courses give you all the skills you need for these fast-growing professions.

You’ll learn about data analysis techniques, Business Intelligence, Machine Learning, the Python language and databases. You’ll also learn DataViz or data visualization techniques, closely linked to design.

By completing this course, you will receive triple recognition: a certificate from Mines ParisTech PSL Executive Education, validation of Block 3 of the state-approved RNCP 36129 “Project Manager in Artificial Intelligence” certification, and Microsoft Azure or Amazon Web Services cloud certification.

Our innovative blended learning approach combines online learning on a coached platform and Masterclasses. All our training courses are delivered remotely, and can be completed by sandwich courses, continuing education or intensive BootCamp.

Recognized by the French government, our organization is eligible for funding options. Don’t waste another moment and discover DataScientest!

Now you know all about the link between Design Thinking and Data Science. For more information on the subject, take a look at our complete dossier on DataViz and our dossier on Business Intelligence.

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