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Design Thinking: Failing Your Way to Innovation
The Design Thinking Methodology
Before we can solve anything, we must frame our problem by asking: “What are we trying to solve?” How do we tackle a question like this? Enter the design thinking methodology. Heard of it? It’s one of many innovative problem-solving tools popular in the UX/UI, product management, and technology industries. It’s a process that helps teams view their offerings through an entirely customer-centered lens. A Brief Overview of the Five Stages:
Empathize is the first stage in the design thinking process and, arguably, the most important. Empathy is the ability to understand what another person sees, feels, or experiences. In the context of product development and design, empathy means understanding the needs of the potential users we’re designing or developing for.
The second step in the process is define. Moving into the define phase means synthesizing, creatively analyzing all of the customer feedback, and forming broader understanding. Imagine we’ve solicited feedback from diners; here’s a sample of the feedback received:
Many who practice design thinking find Ideate, the third phase, the most interesting. Ideating involves working with people to develop as many creative ideas for solving the core problem as possible, regardless of how realistic they are.
Prototype, the fourth phase, is when teams build a sample or model of their product. Prototypes should be as primitive as possible: use any resources available. If a user can produce the desired outcome, (even if the prototype is sketched on construction paper with a crayon) the prototype has done its job. If not, maybe a more robust prototype, like a mock digital app, is needed.
We’ve made it to the fifth and final phase of design thinking: Test. Testing provides an opportunity to gauge how our prototypes compare to users’ expectations and needs. Once we decide how we’ll test, we need to determine how we’ll measure success.
Some teams want to measure through numerical data, using statistics to draw a conclusion to a problem. They want to understand why users are behaving a certain way and how best to solve for a user. Teams like this will likely choose quantitative research methods. Quantitative research can be as simple as sending out an online survey or a poll asking users how often they perform a particular action. The goal is to aggregate as much information as possible to come to a numerical conclusion. For instance:
Six out of 10 diners prefer email vs. postal mail.
95% of diners like to eat after 5pm.
When prompted to accept or decline an invite to dinner, meal attendance increased by 48%.
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Introduction to python
Welcome to this short introduction to the Python programming language! In this course, we’ll give you a glimpse into why Python is such a popular language, as well as how you can use it to automate tasks and streamline projects.
Do you remember the last time you applied for a credit card online? Behind the browser, your information was compared to historical data from millions of past borrowers to determine the probability that you’d make on-time payments, as depicted in the following image:
Because a computer can automatically perform some tasks faster and more efficiently than humans can, we’ll use Python to automate decision-making. Specifically, we’ll use conditional statements. A conditional is essentially a yes-or-no question written in computer syntax. This question-asking process is called conditional logic.
Though you may not realize it, you use conditional logic on a daily basis. For example, you might say “If it’s sunny outside, I’ll go to the park. If it’s raining, I won’t.” When you approach a traffic light, you stop if the light is red, or go if the light is green.
In this chart, we’re using conditional logic to determine whether to turn on the air conditioning or open the windows. The if-else statement applies to the temperature outside. If the temperature is above 80 degrees Fahrenheit, we’ll turn on the air conditioning. Otherwise (or, in Python language, else) we’ll open the windows. We’re using the comparison operator greater than (>) to make this decision.
The following chart illustrates a program to determine a letter grade based on a user’s score. In this scenario, we have a series of conditional statements, each with a different criteria. To begin the program, a user enters a score. Based on that score, Python will assign a letter grade based on a series of conditional statements.
