Data Science and Design Thinking

Data Science and Design ThinkingData science can be defined as analyzing and explaining phenomena from data by trying to identify repetitive patterns from large masses of data using mathematical, statistical, and machine learning models. The goal remains to identify substantial information and to present it in a format that is easy to comprehend. A billion rows of data could, for example, tell us a lot of things. 
Design can be seen as a concrete plan that helps us to reach the desired outcome, whether that outcome be a product, service, process, or strategy. Good design is goal-oriented and based on insight and absolutely no guesswork. Designers have a specialization in using qualitative research methods to understand human needs and behavior.

Data Science without Design has no potential of its own

Data science provides information on what is happening. Design methods help us to understand why those things happen, and what we should do about them. We are able to prove customer behavior hypotheses right or wrong when we combine numerical data and qualitative customer understanding. After thousands of hypotheses, tests, and improvement measures, we know an astonishing amount of information about the desires, habits, and behavior of customers.
Data science is a goldmine of insight for design work. It is not a coincidence that Amazon, Netflix, and Facebook dominate the markets. They have made their consumer research an exact science that predicts the future behavior of their users. Yet, data alone is not enough to explain the whole world. We also need design research to provide us with a deeper understanding of the future. Design research helps to uncover the unknown which feeds hypotheses and data insights. Noe wonder, data and design go hand in hand.
Numbers may “lie” to us. If we ask the wrong questions, we can end up drawing the wrong conclusions. Unfortunately, this is all too easy for us. While they might sound like polar opposites, data and design represent an interdisciplinary approach to design intuitive, user-friendly products made expressly for humans. There are certain factors that led to data science and design uniting for a better possibility. 

Inspiration

The initial phase of any design process starts with searching for inspiration in unlikely places. 
Using data science during this process allowed the designers to explore blind spots. The proprietary data from the client or publicly available data sources like social media are a great place to start. 

Research and synthesis

From a human-centered design standpoint, research involves stepping into the prospective user’s shoes and building empathy for them to understand their pain points, each one of which represents a design opportunity. In order to help the researchers gain an objective view of qualitative data, applying a data science approach to this phase of the design process is necessary. 

Prototyping

As soon as the designers have done sufficient research to understand the pain points of their target audiences, they start brainstorming some potential solutions and then design a tangible product. They test the product in the field and iterate in response to user feedback. Prototypes can be low-tech. Visuals are an ideal medium for data scientists and designers to communicate their ideas in a universal language, whether it’s a sketch of a potential interface or a graphical representation of data analysis. Data and design complement each other for the best outcome.

Communication

The final phase of the amalgamation of data science and design process involves presenting the final idea to the client. This is where communication comes to place in a broad manner. It doesn’t limit the communication to charts, reports, and PowerPoint presentations, it involves having the client try a prototype of a mobile app by assuming the role of a particular user. The use of data science techniques in communication allows the designers to showcase the prototype in a real-life situation. This is done by using real or simulated data. Such simulation helps the teams from both areas do their job better. 
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Data Science has undoubtedly unlocked various potentials and allowed data science enthusiasts to learn and grow. In addition to that, the consolidation of the design approach just makes the whole concept much more relatable and interactive. At Ivy Professional School, we aim towards helping data science enthusiasts reach their goal by enhancing their knowledge in data science. We provide certifications that broaden their approach towards this subject including a NASSCOM certified diploma.We also have our own blogs where we are continuously trying to keep aspiring enthusiasts updated about the newest developments in data science. If you are such a data science enthusiast looking to enhance your career in the field of data science and analytics then our amazing team is here to help. Give us a call and we would help you get one of the hottest jobs of the century!

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