Working with ADHD, Pt. 2 – Data Science

Click here to read part 1 on product management with ADHD!

In the first part of Working with ADHD, I discussed how ADHD can be both a strength and a weakness in product management. Our heightened perception and empathy, ability to focus on the bigger picture, and flexibility to quickly switch contexts are valuable assets. However, our resistance to extreme structure, spreadsheets, and planning can be challenging. When it comes to data analytics and data science, this spreadsheet-phobia can be even more pronounced. The sheer volume of information can be overwhelming, and we are expected to extract meaningful insights and narratives from these data behemoths. Nevertheless, ADHD also brings unique strengths that can streamline the process from exploration to insight and enable us to work more efficiently with data.

Although I have not held a dedicated data role yet, I have interviewed for several and have worked in technical data capacities throughout my career. Additionally, I am pursuing a Master’s degree in data science, so I am no stranger to large-scale, collaborative data projects. This article is for anyone working with data in their daily duties, not just those with titles like data analyst, data scientist, or data engineer. I aim to show how leveraging ADHD strengths can help cover pitfalls in attention to detail and collaboration, enabling us to craft evidence-driven stories and strategies effectively.

Learning Data Science

The typical data analytics process consists of the following steps:

1. Identify and define the business problem
2. Identify relevant data sets and sources
3. Extract and process data
4. Explore and clean data
5. Analyze data
6. Visualize and present data
7. Operationalize findings

In introductory data analytics and science courses, students often start with the basics of coding and data extraction, like Numpy, Pandas, or Python. Increasingly, defining the business problem is becoming critical in nearly all aspects and industries of data analysis.

How does ADHD come into play? For me, the most disruptive moments occurred when diving straight into models, algorithms, or mathematics without much context. I often became confused, struggling to see how each component fit into the bigger picture. Additionally, I tend to immediately visualize code and its results in plain English as I read it, leading to mental parallel programming that causes headaches and confusion.

When I first started learning advanced mathematics and statistics, I felt perpetually behind despite my efforts. The sudden jump from algebra to calculus, coupled with the abstract nature of the concepts, was overwhelming. While this learning curve is common, my ADHD exacerbated my difficulties. Incorrect interpretations and applications of concepts on assignments and projects often led me astray, and I blamed myself for not grasping them as quickly as others. Through trial and error, I found ways to compensate for this difficulty and realized that my slow understanding stemmed from several feelings:

• Unfamiliar concepts caused my heart rate to increase and my thoughts to jumble.
• I distracted myself to avoid thinking through difficult thoughts, even during live classes.
• Distractions ranged from pulling out my phone to daydreaming to biting my nails.
• I jumped into assignments or projects without planning or structuring, driven by a sharp tinge of perfectionism.

To overcome these challenges, I learned to compartmentalize my thought processes. This did not come naturally and required months of training and self-awareness. I separated my ego from external behaviors and confronted negative reactions with logical reasoning. Additionally, I developed strategies to safeguard against distractions:

• I left my laptop at home or in the library and took handwritten notes, often giving my phone to a friend during lectures.
• I used different colored pens to categorize notes: black for regular notes, red for important callouts, green for example solutions, etc.
• I drank a lot of coffee to stay focused—though I'm not addicted, I promise!

Learning to absorb abstract mathematical concepts and code abstraction became easier. I hand-wrote code and equations and annotated each line to understand its purpose in the overall program. After every lecture, I reviewed my notes and wrote down the top five takeaways and one overall message.

In my Master’s program in data science at UC Berkeley, course content is delivered through pre-recorded videos, with live discussions during lectures. Initially skeptical of this format, I quickly appreciated the independence it provided. I could learn at my own pace and developed the motivation to finish watching the videos before live lectures. This freedom allowed my overstimulated brain to take necessary breaks and direct intense thinking where needed. Key takeaway: Reserving time and allowing yourself to regulate your emotions are critical prerequisites to succeeding in difficult tasks!

Data Analytics on the Job

Though I have not held a dedicated data role, I have applied various data analytics techniques in all my jobs. Despite "data-driven" becoming a buzzword, I believe ADHD has given me a heightened attention to explore and leverage data. This trait manifests in three main "superpowers."

First, my ability to quickly synthesize data sources stands out. I can project a clear image of the end result during the problem definition phase and swiftly peruse multiple datasets. I identify and investigate relevant tables within short bursts of productivity.

Second, stories and narratives easily materialize in my head. I can quickly picture a sequence of events and the reactions I hope to elicit from my audience. I pull the appropriate data, craft the right message, and finalize the visuals with ease.

Third, I can recall specific pieces of outside information on the spot. While my working memory is generally weak without documentation or medication, I remember small details. This is useful for bringing additional context, incorporating outside knowledge, and adding humor to presentations about potentially difficult insights.

These three powers lead to many opportunities in making my work more fun and tailored to my own personality, such as:

• Creating artificial deadlines to maintain focus. For instance, set up periodic review meetings with a co-worker or stakeholder for long-term projects. This adds just enough pressure to keep me on track leading up to these deadlines. (Hint: This method is GREAT for interviewing prepping / studying as well!)
• Injecting some humor into my code. I love adding silly elements that make me chuckle, like writing tests with puns or pop culture references. It keeps the work enjoyable and light-hearted.
• Leveraging my autonomy wisely. I have the freedom to choose my projects, which is both a blessing and a curse. Whenever possible, try to incorporate novel elements into your tasks to keep things interesting.
• For repetitive tasks, finding ways to automate them. Instead of doing the same thing repeatedly, identify one part of the process that could be automated and focus on optimizing it.

To fully leverage these ADHD superpowers, I recommend fellow ADHDers dedicate mental energy to context-building. Ruthlessly question the purpose and goal of a task to emotionally accept its meaning. Understand how the task impacts others. Developing this skill throughout my career has unlocked my ADHD superpowers and made me very detail-oriented and intentional in everything I do.

Conclusion

Working in data science with ADHD presents unique challenges, but it also offers distinct advantages. By harnessing our natural strengths and developing strategies to mitigate our weaknesses, we can excel in this field. Our ability to synthesize information quickly, create compelling narratives, and recall specific details can set us apart. It's essential to build context, understand the purpose of tasks, and appreciate their impact on others. Embracing these strategies can turn ADHD from a hurdle into a powerful asset, enabling us to thrive in the ever-evolving world of data science.