View from the Inside: Coding and Data Analysis

Continuing the View from the Inside series, Zahira Tasabehji reflects on the coding and data analysis process.

Zahira is studying Political Science, Psychology, and Education Studies at UTM. She’s passionate about transforming education, so her role at the Innovation Hub is the perfect place to use  her leadership and creative skills to enhance the student experience!

All my life, the word coding was associated in my brain with computers and technological systems that I knew nothing about. Data analysis was a recurring term in my second year statistics class that I very much despised. To me, all that jargon was linked to math, numbers, and complicated computational programs. Don’t get me wrong, I have always been a math lover, but only for the pure enjoyment I get from problem-solving. I never thought I would one day dive into the mysterious coding world that was nothing but an abstraction to me. Being introduced to Design Thinking has taught me that coding and data analysis are not reserved for computer science gurus; there is a whole side to this that concerns itself with understanding human thought and behaviour.

When taking a human-centred approach, we collect data from the stories that people share with us, in hopes that we can identify and solve a problem. When sharing their experiences, students are entrusting us to help them find a solution to their perceived challenges. In interviews, many students were inclined to answer in a certain way because they assumed that they were providing us with the solution. But before looking for solutions, we need to fully understand the problem space: my team and I often explained that the interview process allows us to find common themes and real needs in students’ experiences, which emerge through coding. Coding allows us to extract insights from qualitative stories—to converge on needs before we diverge and let our brains loose on potential solutions.

After transcribing all the interviews, compiling our observation notes and other data, we attach codes to the data. This part feels like cleaning out your closet, super messy and driving you crazy at first, but culminating in a wonderful result. Step one: take everything out—the shoes, pants, sweaters, and that empty shoebox that you’ve had for three months. This step requires you to extract all the codes from the transcripts, regardless of their seeming relevance. Step two: organize into piles of similar kind. Pair up the codes that are alike and place them in their own category. Step three: put the items that you use more often in the forefront. Rank the clusters of codes according to priority. Step four: match up outfits that go together or those of similar colours. Make connections between clusters, and see how they complement each other. Step five: show everyone and brag about your newly organized closet. Describe to others what you have synthesized and allow them to give their feedback. From chaos comes order.

Most people know that the hardest part of cleaning your closet is not the actual process of cleaning, but what comes after: keeping everything organized. Now that you’ve organized the data, how do you move forward into interpreting meaning? This requires a lot of discussion and questioning. The crucial part of this step is to come up with as many models as you can, and to fall in love with each one as if it’s the one. Often, when coming up with a solution, we set limits to our ideas by labeling them “impossible” or “unfeasible.” At the Innovation Hub, we learn to open up our minds and embrace out-of-reach and opposing ideas with open arms. We are encouraged to take on a different perspective, to let go of preconceived notions and biases that we hold against our ideas and those of others. It is only when we begin to challenge our brains to break free from the box, and put the power back into ideas, that we are able to implement real and impactful change.