Author: Stephen Paff

The Job Hunt (Part 5): My Own Self-Reflection

In this article, I complete the occupational self-reflection I described in Part 4 as an example of what such a reflection might look like. Your story won’t be mine, so feel free to craft the reflection to fit your needs. This is just one sample of what it could look like.

Activity Overview:

Step 1List out five activities that you have done in the last few years that inspire you.
Step 2List out five activities that you have done in the last few years that have drained/frustrated you.
Step 3For each activity on both lists, then write out what about it inspired you and what about it drained you.
Step 4Look through both sets of lists for common features.
Step 5Synthesize these common features into a one- to two-sentence story.
Step 6Tell this story to others who are close to you and practice it when appropriate with others.

Step 1: List out five activities that inspire you.

  1. Show Rate Predictor at BronxCare
  2. Master’s Practicum
  3. Ethno-Data Blog
  4. Writing a Sitcom
  5. Networking

Descriptions:

(Note: You wouldn’t likely need to explain what each activity is to yourself, since, well, you are the person who did them. Just listing them is fine. But I am describing my activities to help give you, reader, context since otherwise, you won’t likely know what I did.)

  1. Show Rate Predictor at BronxCare: This was a major project I worked on at BronxCare. I worked with a clinic to build a machine learning show rate predictor that both calculated the probability an upcoming appointment would occur and estimated the number of appointments to expect for every doctors’ shifts. I started by conducting ethnographic user research into the problem to figure out exactly what they need, built two machine learning algorithms to provide the most useful information, and worked with a team to develop the app to communicate that findings to schedulers in real-time as they schedule.
  2. Master’s Practicum: The University of Memphis’s Anthropology Department required a practicum project, a 300-hour or more project with an organization for the master’s degree and documenting a detailed research report on the work. I did my practicum with Indicia Consulting in the summer of 2018. Here is my full report for your reference.
  3. Ethno-Data Blog: I am referring to this blog you are reading. So, if you are reading this, you have found it and know what it is.
  4. Writing a Sitcom: I have been writing an animated sitcom, which a few of my artistic friends and I are planning on developing.
  5. Networking: By this, I am thinking of the general activity of networking with other people to learn about their work and find connections with them. I do this particularly vigorously whenever I am looking for a job.

Now, yes, these are a range of projects: including a work project, a personal project, a school project, and finally a nebulous, informal activity like networking. This is by design, since encompassing a wide variety of different types of activities allows me to think about different facets of my life. Feel free to choose among any type of activities that would be most helpful for you.  

Step 2: List out five activities that you have done in the last few years that have drained/frustrated you.

  1. Comprehensive Exams
  2. UX Research Consulting Project for Thriving Cities Group
  3. Data Pulling at BronxCare
  4. Retention Research Project with ServiceMaster
  5. Secondary Math Teaching

More detailed explanations for your reference:

  1. Comprehensive Exams: The University of Anthropology required a written comprehensive exam to graduate with a master’s degree. One must write a four in-class essays over the series of two days and then defend your answers to your committee.
  2. UX Research Consulting Project for Thriving Cities Group: Thriving Cities Group was seeking to build an app to help non-profits coordinate with potential funders and chose Memphis, Tennessee as the starting place to launch their beta-version. They hired me as a UX researcher in preparation for this launch. I really enjoyed the UX research I did, but I also learned a lot about what I need when conducting research as a consultant with organizations.
  3. Data Pulling at BronxCare: As a data scientist who specialized in developing machine learning and statistical models for data, my role as a data scientist at BronxCare typically did not require simple data queries to pull data for a project. But, occasionally it would. These would generally be one-time SQL queries into the database system (since another team would generally conduct repeating queries). This was my least favorite aspect of my job there.
  4. Retention Research Project with ServiceMaster: As a data scientist, I worked on a year-long project building a model to predict and understand retention. I appreciated the idea of the project and the nature of the work, but its implementations had some issues: the abrupt departure of my manager who had commissioned the project, the lack of managerial buy-in for the project above him, issues collecting the necessary data, and departmental politics. These led to the project being overall frustrating, although also a learning experience on aspects of organizational research projects frustrate me.
  5. Secondary Math Teaching: I taught secondary math in both Gary, IN and Chicago, IL for a few years before deciding to move into data science. Through this, I learned middle school and high school teaching is not a good fit for me.

Notice again that this list covers a variety of different types of projects from different contexts and times in my life. Feel free to do the same.

Step 3: For each activity on both lists, then write out what about it inspired you and what about it drained you.

Here are what aspects of each list energized me and frustrated me.

Note: Yes, I wrote both what energized me and what frustrated me for both lists. Even the most life-giving activities have aspects about them that frustrates you and the most frustrating activities have positive aspects. You learn a lot about thinking about the ugly in the good and the good in the ugly.

Energizing Activities

Energizing ActivitiesWhat Energized MeWhat Frustrated
Show Rate Predictor at BronxCareTrying to break down and solve a complex problem

Figuring out the optimal machine learning models to useDesigning the software’s architecture and design

Presenting/selling it to those in the clinic and receiving their buy-in/support		Bureaucratic red tape to get the proper software access to build the app

Internal politics within the organization in getting the project off the ground
Master’s PracticumThe work itself: solving the problem and completing the task with Indicia

Researching and seeking to rethink the historic relationship between anthropology and data science (my key intervention in my report)

Presenting my work at conferences and the recognition through the various awards I received for innovative research		Committee members seemingly unwilling to entertain the thought of rethinking the discipline and its relationship to data science

Committee members trying to cast my work as not anthropology because it did not fit their pre-assigned mold of what anthropology is
Ethno-Data BlogResearching and developing interesting ideas, analysis, content, and explanations

Writing blog postsEspecially writing this Job Hunt mini-series

People coming to me saying they appreciated my blog and/or to ask questions about it		Using the WordPress software interface, particularly difficult is  transferring articles from Word Documents in which I originally write to WordPress

Rushing to meet posting deadlines
Writing a SitcomDeveloping the characters and their back stories

Developing the overarching story structure for the show, outlining plots of specific episodes, and developing specific scenes

Meeting with other artists to build a teamFeeling accomplished after finishing a draftSeeing other people excited about the idea		Editing: constantly polishing over the dialogue to make it shine (necessary but can be agonizing)

Trelby’s interface (the screenwriting software I use)
NetworkingReaching out to and connecting with people

Learning about other people and their work/experiences

Thinking through ways to work together to solve problems		Its cyclic nature: constantly repeating the same cycle of reaching out to new people after concluding with current connection

Monotony of reaching out: generally having the repeat the same set of tasks with minor yet hard to automate tweaks

Frustrating Activities

Frustrating ActivitiesWhat Energized MeWhat Frustrated
Comprehensive ExamsResearching and developing the ideas to write about

Writing out my prepared essays

Wanting to talk about my intellectual journey and what anthropologists should learn from other disciplines and ways of thinking in order to think through how to engage with others in the world		Some of my committee members tried to force how I would think about things in the prompt by including what seemed like false premises into the questions themselves

Some of committee members seemed to want me to parrot why anthropology is great and superior to all other disciplines

Already dealing with burnout at the start of the assignment
UX Research Consulting Project for Thriving Cities GroupHearing about people’s work at various non-profits around Memphis and their stories

Analyzing issues with the app

Working with engineers to come up with innovative solutions to the problems users brought up		The team seemed to be trying to find a problem that matched their “solution” they have already built instead of seeking to understand what issues/needs people have and then crafting a solution to those needs

Top-down and forward-moving marching orders, which, in this instance, ignored the app’s severe issues with users on the ground
Data Pulling at BronxCareFiguring out how to make Python scripts to automate these processes

Building and analyzing models on the data after getting it together		Tedious

Unnecessarily time-consuming yet trivial and unstimulating intellectually

Data was stored in inconsistent and frustrating ways to access later
Retention Research Project with ServiceMasterPlanning and scoping out how to do the project

The ethnographic portion of the project where interviewed and observed customer service representatives to learn about their experiences/expertise in communicating with customers

Networking and collaborating with people from other teams to obtain data needed for the project and to learn about their own related projects

Felt like the work connected well with the goals and bottom-line of both organization as a whole and my team specifically		When developing the research questions for the project, I felt like my supervisor could only think of limited, close-ended questions, instead of thinking open-endedly about the project. This seemed necessary given the complexity of the issue at hand.

The research questions I was given for the project overly reductionist and simplisticThe data was half-hazard and messy, which made analysis with it difficult.

Frustrating inter-departmental politics to access and use of the data I got
Secondary Math TeachingPublic speaking

One-on-one teaching and mentoring

Helping students develop the critical thinking skills to solve complex problems themselves		Managing childish/immature student behavior

The tedium of grading

Teachers did not have enough time to collaborate in the school.

Step 4: Look through both sets of lists for common features.

Looking through them, I found the following common features within both columns:

What Energized MeWhat Frustrated
Talking with people and learning about what they are doing, whether through interviews, ethnographic research, or another means.

Networking and collaborating with others on a project

Analyzing and developing strategies to solve complex problems (e.g. whether the problem is data science-problem, mathematical/statistical, social or “people” problem, and so on.)

Communicating solutions to others (both conversationally and through public speaking) Writing creatively		Monotony, tedium, and rote work

Having to follow a seemingly unproductive and non-innovative procedure

Not having an innovative strategy I have been developing be understood or appreciated by those I am working with, especially my supervisor
Feeling my innovative ideas were shut down or attempted be shut down due to close-mindedness and the inability to think outside of the “conventional” ways of doing things

Step 5: Synthesize these common features into a one- to two-sentence story.

I did this synthesis twice, first writing a very large and cumbersome one-sentence version and then shortening it to make it more manageable.

Synthesis #1 – Large Cumbersome One-Sentence Version:

I am passionate about developing innovative ways to solve complex problems with others in situations for which the conventional approaches do not work, but I become frustrated when those with whom I am working (especially supervisors) do not understand or appreciate that innovation and try to enforce conventional ways of thinking that they have always employed.

Synthesis #2 – Final, Shortened Version:

My passion in a job is to develop innovative ways to solve complex problems, but I become frustrated when those with do not understand/appreciate that innovation and try to enforce conventional approaches.

Step 6: Tell this story to others who are close to you and practice it when appropriate with others.

I cannot easily show this stage in a written article like this, but suffice to say that it is extremely important to show and practice this in front of trusted family and friends. You learn a lot from their feedback.

Photo Credit #1: Pexels at https://pixabay.com/photos/fountain-pen-note-notebook-page-1851096/

Photo Credit #2: StockSnap at https://pixabay.com/photos/people-girls-women-students-2557396/

Photo Credit #3: Allan Rotgers at https://www.flickr.com/photos/122662432@N04/13740073235/in/photostream/

Photo Credit #4: kaboompics at https://pixabay.com/photos/young-woman-girl-lady-female-work-791849/

Photo Credit #5: Kelly Sikkema at https://unsplash.com/photos/-1_RZL8BGBM

Photo Credit #6: 742680 at https://pixabay.com/photos/chalkboard-story-blogging-believe-620316/

Photo Credit #7: Christina at https://unsplash.com/photos/LQ1t-8Ms5PY

Data Science and the Myth of the “Math Person”

woman holding books

“Data science is doable,” a fellow attendee of the EPIC’s 2018 conference in Honolulu would exclaim like a mantra. The conference was for business ethnographers and UX researchers interested in understanding and integrating data science and machine learning into their research. She was specifically trying to address a tendency she has noticed– which I have seen as well: qualitative researchers and other so-called “non-math people” frequently believe that data science is far too technical for them. This seems ultimately rooted in cultural myths about math and math-related fields like computer science, engineering, and now data science, and in a similar vein as her statement, my goal in this essay is to discuss these attitudes and show that data science, like math, is relatable and doable if you treat it as such.

The “Math Person”

In the United States, many possess an implied image of a “math person:” a person supposedly naturally gifted at mathematics. And many who do not see themselves as fitting that image simply decry that math simply isn’t for them. The idea that some people are inherently able and unable to do math is false, however, and prevents people from trying to become good at the discipline, even if they might enjoy and/or excel at it.

Most skills in life, including mathematical skills, are like muscles: you do not innately possess or lack that skill, but rather your skill develops as you practice and refine that activity. Anybody can develop a skill if they practice it enough.  

Scholars in anthropology, sociology, psychology, and education have documented how math is implicitly and explicitly portrayed as something some people can do and some cannot do, especially in math classes in grade school. Starting in early childhood, we implicitly and sometimes explicitly learn the idea that some people are naturally gifted at math but for others, math is simply not their thing. Some internalize that they are gifted at math and thus take the time to practice enough to develop and refine their mathematical skills; while others internalize that they cannot do math and thus their mathematical abilities become stagnant. But this is simply not true.

Anyone can learn and do math if he or she practices math and cultivates mathematical thinking. If you do not cultivate your math muscle, then well it will become underdeveloped and, then, yes, math becomes harder to do. Thus, as a cruel irony someone internalizing that he or she cannot do math can turn into a self-fulfilling prophecy: he or she gives up on developing mathematical skills, which leads to its further underdevelopment.

Similarly, we cultivate another false myth that people skilled in mathematics (or math-related fields like computer science, engineering, and data science) in general do not possess strong social and interpersonal communication skills. The root for this stereotype lies in how we think of mathematical and logical thinking than actual characteristics of mathematicians, computer scientists, or engineers. Social scientists who have studied the social skills of mathematicians, computer scientists, and engineers have found no discernable difference in social and interpersonal communication skills with the rest of the world.  

Quantitative and Qualitative Specialties

Anyone can learn and do math if he or she practices math and cultivates mathematical thinking.

The belief that some people are just inherently good at math and that such people do not possess strong social and interpersonal communication skills contributes to the division between quantitative and qualitative social research, in both academic and professional contexts. These attitudes help cultivate the false idea that quantitative research and qualitative research are distinct skill sets for different types of people: that supposedly quantitative research can only be done “math people” and qualitative research by “people people.” They suddenly become separate specialties, even though social research by its very nature involves both. Such a split unnecessarily stifles authentic and holistic understanding of people and society.

In professional and business research contexts, both qualitative and quantitative researchers should work with each other and eventually through that process, slowly learn each other’s skills. If done well, this would incentivize researchers to cultivate both mathematical/quantitative, and interpersonal/qualitative research skills.

It would reward professional researchers who develop both skillsets and leverage them in their research, instead of encouraging researchers to specialize in one or the other. It could also encourage universities to require in-depth training of both to train their students to become future workers, instead of requiring that students choose among disciplines that promote one track over the other.

Working together is only the first step, however, whose success hinges on whether it ultimately leads to the integration of these supposedly separate skillsets. Frequently, when qualitative and quantitative research teams work together, they work mostly independently – qualitative researchers on the qualitative aspect of the project and quantitative researchers on the quantitative aspects of the project – thus reinforcing the supposed distinction between them. Instead, such collaboration should involve qualitative researchers developing quantitative research skills by practicing such methods and quantitative researchers similarly developing qualitative skills.

Conclusion

Anyone can develop mathematics and data science skills if they practice at it. The same goes with the interpersonal skills necessary for ethnographic and other qualitative research. Depicting them as separate specialties – even if they come together to do each of their specialized parts in a single research projects – functions stifles their integration as a singular set of tools for an individual and reinforces the false myths we have been teaching ourselves that data science is for math, programming, or engineering people and that ethnography is for “people people.” This separation stifles holistic and authentic social research, which inevitably involves qualitative and quantitative approaches.

Photo credit #1: Andrea Piacquadio at https://www.pexels.com/photo/woman-holding-books-3768126/

Photo credit #2: Antoine Dautry at https://unsplash.com/photos/_zsL306fDck

Photo credit #3: Mike Lawrence at https://www.flickr.com/photos/157270154@N05/28172146158/ and http://www.creditdebitpro.com/

Photo credit #4: Ryan Jacobson at https://unsplash.com/photos/rOYhgmDIOg8

When Is Machine Learning Useful?

In a past blog post, I defined and described what machine learning is. I briefly highlighted four instances where machine learning algorithms are useful. This is what I wrote:

  1. Autonomy: To teach computers to do a task without the direct aid/intervention of humans (e.g. autonomous vehicles)
  2. Fluctuation: Help machines adjust when the requirements and data change over time
  3. Intuitive Processing: Conduct or assist in tasks humans do but are unable to explain how computationally/algorithmically (e.g. image recognition)
  4. Big Data: Breaking down data that is too large to handle otherwise

The goal of this blog post is to explain each in more detail.

Case #1: Autonomy

Car, Automobile, 3D, Self-Driving

The first major use of machine learning centers around teaching computers to do a task or tasks without the direct aid or intervention of humans. Self-driving vehicles are a high-profile example of this: teaching a vehicle to drive (scanning the road and determining how to respond to what is around it) without the aid of or with minimal direct oversight from a human driver.

There are two types basic types of tasks that machine learning systems might perform autonomously:

  1. Tasks humans frequently perform
  2. Tasks humans are unable to perform.

Self-driving cars exemplify the former: humans drive cars, but self-driving cars would perform all or part of the driving process. Another example would be chatbots and virtual assistants like Alexa, Cortana, and Ok Google, which seek to converse with users independently. Such tasks might completely or partially complete the human activity: for example, some customer service chatbots are designed to determine the customer’s issue but then to transfer to a human when the issue has a certain complexity.

Humans have also sought to build autonomous machine learning algorithms to perform tasks that humans are unable to perform. Unlike self-driving cars, which conduct an activity many people do, people might also design a self-driving rover or submarine to drive and operate in a world that humans have so far been unable to inhabit, like other planets in our Solar System or the deep ocean. Search engines are another example: Google uses machine learning to help refine search results, which involves analyzing a massive amount of web data beyond what a human could normally do.

Case #2: Fluctuating Data

Business, Success, Curve, Hand, Draw, Present, Trend

Machine learning is also powerful tool for making sense of and incorporating fluctuating data. Unlike other types of models with fixed processes for how it predicts its values, machine learning models can learn from current patterns and adjust both if the patterns fluctuate overtime or if new use cases arise. This can be especially helpful when trying to forecast the future, allowing the model to decipher new trends if and when they emerge. For example, when predicting stock prices, machine learning algorithms can learn from new data and pick up changing trends to make the model better at predicting the future.

Of course, humans are notorious for changing overtime, so fluctuation is often helpful in models that seek to understand human preferences and behavior. For example, user recommendations – like Netflix’s, Hulu’s, or YouTube’s video recommendation systems – adjust based on the usage overtime, enabling them to respond to individual and/or collective changes in interests.

Case #3: Intuitive Processing

Flat, Recognition, Facial, Face, Woman, System

Data scientist frequently develop machine learning algorithms to teach computers how to do processes that humans do naturally but for which we are unable to fully explain how computationally. For example, popular applications of machine learning center around replicating some aspect of sensory perception: image recognition, sound or speech recognition, etc. These replicate the process of inputting sensory information (e.g. sight and sound) and processing, classifying, and otherwise making sense of that information. Language processing, like chatbots, form another example of this. In these contexts, machine learning algorithms learn a process that humans can do intuitively (see or hear stimuli and understand language) but are unable to fully explain how or why.

Many early forms of machine learning arose out of neurological models of how human brains work. The initial intention of neural nets, for instance, were to model our neurological decision-making process or processes. Now, much contemporary neurological scholarship since has disproven the accuracy of neural nets in representing how our brains and minds work.[i] But, whether they represent how human minds work at all, neural networks have provided a powerful technique for computers to use to process and classify information and make decisions. Likewise, many machine learning algorithms replicate some activity humans do naturally, even if the way they conduct that human task has little to do with how humans would.

Case #4: Big Data

Technology, 5G, Aerial, Abstract Background

Machine learning is a powerful tool when analyzing data that is too large to break down through conventional computational techniques. Recent computer technologies have increased the possibility of data collection, storage, and processing, a major driver in big data. Machine learning has arisen as a major, if not the major, means of analyzing this big data.

Machine learning algorithms can manage a dizzying array of variables and use them to find insightful patterns (like lasso regression for linear modeling). Many big data cases involve hundreds, thousands, and maybe even tens or hundreds of thousands of input variables, and many machine learning techniques (like best subsets selection, stepwise selection, and lasso regression) process the myriads of variables in big data and determine the best ones to use. 

Recent developments computing provides the incredible processing power necessary to do such work (and debatably, machine learning is currently helping to push computational power and provide a demand for greater computational abilities). Hand-calculations and computers several decades ago were often unable to handle the calculations necessary to analyze large information: demonstrated, for example, by the fact that computer scientists invented the now popular neural networks many decades ago, but they did not gain popularity as a method until recent computer processing made them easy and worthwhile to run.

Tractors and other large-scale agricultural techniques coincided historically with the enlargement of farm property sizes, where the such machinery not only allowed farmers to manage large tracks of land but also incentivized larger farms economically. Likewise, machine learning algorithms provide the main technological means to analyze big data, both enabling and in turn incentivized by rise of big data in the professional world.

Conclusion

Here I have described four major uses of machine learning algorithms. Machine learning has become popular in many industries because of at least one of these functionalities, but of course, they are not the only potential current uses. In addition, as we develop machine learning tools, we are constantly inventing more. Given machine learning’s newness compared to many other century-old technologies, time will tell all the ways humans utilize it.

Photo credit #1: Mike MacKenzie at https://www.flickr.com/photos/mikemacmarketing/30212411048/

Photo credit #2: julientromeur at https://pixabay.com/illustrations/car-automobile-3d-self-driving-4343635/

Photo credit #3: geralt at https://pixabay.com/illustrations/business-success-curve-hand-draw-1989130/

Photo credit #4: geralt at https://pixabay.com/illustrations/flat-recognition-facial-face-woman-3252983/

Photo credit #5: mohamed_hassan at https://pixabay.com/illustrations/technology-5g-aerial-4816658/

[i] See Richard, Nagyfi. The differences between Artificial and Biological Neural Networks. 4 September 2018. https://towardsdatascience.com/the-differences-between-artificial-and-biological-neural-networks-a8b46db828b7; and Tcheang, Lili. Are Artificial Neural Networks like the Human Brain? And does it matter? 7 November 2018. https://medium.com/digital-catapult/are-artificial-neural-networks-like-the-human-brain-and-does-it-matter-3add0f029273.

Four Lessons in Time Management: What Graduate School Taught Me about Time Management

three round analog clocks and round gray mats

I am a Type-A personality who likes to do a variety of different activities yet cannot help but give each of them my all. Through this, I have learned a ton about time management. In particular, from 2017 to 2019, I was in graduate school at the University of Memphis while working as both a data scientist and a user researcher. I was easily working 70-90 hours a week.

Necessity is often the best teacher, and during this trial by fire, I figured out how to manage my time efficiently and effectively. Here are four personal lessons I learned for how to manage time well:

Lesson #1 Rest Effectively
Lesson #2 Work in Short-Term Sprints
Lesson #3 Complete Tasks during the Optimal Time of Day
Lesson #4 Rotating between Types of Tasks to Replenish Myself

Lesson #1: Rest Effectively

Developing an effective personal rhythm in which I had time to both work and relax throughout the day was necessary to ensure that I could work productively.

When many people think about time management (or at least when I do), they often focus on strategies/techniques to be productive during work time. Managing one’s time while working is definitely important, but I have found that resting and recuperating effectively is by far the most important single practice to cultivate to work productively.

Developing an effective personal rhythm in which I had time to both work and relax throughout the day was necessary to ensure that I could work effectively.

woman doing yoga meditation on brown parquet flooring

Several different activities help me relax: taking walks, exercising, hanging out with friends and colleagues, reading, watching videos, etc. People have a variety of ways to relax, so maybe some of those are great for you, and maybe you do something else entirely.

Generally, to relax I chose an activity that contrasted and complemented the work I had just been doing. For example, if my work was interviewing people – which I did frequently as a user researcher – then I would unwind with quiet, solitary tasks like walking or reading, but if my work was solitary like programming or writing a paper, I might unwind by socializing with others. Relaxing with a different type of activity as my work would allow me to rest and rejuvenate from the specific strains of that work activity.

I have seen a tendency in some of U.S. work/business culture to constantly push to do more. The goal is usually productivity – that is to get more done – and it makes sense to think that doing more will, well, lead to getting more things done.

That is true to a point, though, or at least to me. There comes a point when trying to do more actually prevents me from getting more done. Instead, taking enough time to rest and recuperate unwinds my mind so that when I am working, I am ready to go. This leads to greater productivity across all counts:

  1. Quantitatively: I can complete a greater number of tasks
  2. Qualitatively: The tasks I complete are of better quality
  3. Efficiency: It takes me a lot less time to complete the same task

I think the idea that doing more work leads to greater productivity is a major false myth in the modern U.S. workforce. Instead, it leads to overwork, stress, and inefficiency, stifling genuine productivity.

Self-care through incorporating rest into my work rhythm has not only been necessary for my mental health but also to be a productive worker. In discussions around self-care, I have often a juxtaposition between being more productive and taking care of oneself, but those two concerns reinforce each other not contradict each other. Overworking without taking enough time to recuperate prevents me from being an effective and productive human worker. Instead, the question is how to cultivate life-giving and rejuvenating practices and disciplines so that I can become productive and maintain so.

Lesson #2: Work in Short-Term Sprints

I developed a practice of completing tasks in twenty-five-minute chunks. I would set the timer for twenty-five-minutes and work intensely without stopping on the given task/project until the time was up. (My technique has some similarities with the Pomodoro Technique, but without as many rules or requirements.) I realized that twenty-five-minutes was how long I could mentally work continuously on a single task without thinking about something else or needing a break. After that time, I would start to get tired and inefficient, so giving myself a break would let me unwind and rejuvenate.

After one of these twenty-five-minute sprints, I would take a break of at least five minutes: walk around, watch an interesting video, go talk with a colleague or friend, whatever I needed to do to unwind. These breaks were the time my brain would need to process what I was doing and reenergize for the next task. Given that my day would be made up of several of these twenty-five-minute sprints, for the first one or two, I might take a five minute break, but a few more, I might take a longer break as I had done more to unwind from.

A crucial skill for this practice has been successfully breaking down the given project to complete in the timed chunks. For some projects, I would designate a short-term task or goal to complete in the twenty-five-minutes. With my course readings, for example, I generally had to submit a summary and analysis of the readings. Thus, my goal during each twenty-five-minute sprint would be to finish one article or chapter – both reading it and writing the summary and analysis. I would start by reading the most significant subsections, generally the introduction and conclusion, summarizing and analyzing it as I read. That generally took up half of my twenty-five-minutes, so in whatever remaining time I had left, I would read the remaining sections.

This provided enough time to get a sense for the reading’s argument and complete the assignment, even in the off-chance that I did not have time to finish reading the entire article. In only twenty-five-minutes, I would knock out a whole reading, including my summary and analysis: one less task to worry about. Spending twenty-five-minutes a day is not that much of a burden either. Doing this, I would complete all the readings for my courses within the first few weeks of the semester, opening time over the next several months when my other work would pick up.

aerial photography of mountain ridge

I could not split all activities into short-term tasks to complete in twenty-five-minutes, though. For those I could not, the trick was to estimate how much time an overall task would take. For example, if my supervisor gave me a month to complete a project, I would then calculate how many twenty-five-minute slots I would need per day given how many total hours I would likely need to spend on the project.

Data science projects are notoriously nonlinear, meaning that I could just about never break them down into sets of twenty-five-minute tasks, but rather almost always had to just figure out how much total time to budget like this. The various parts of a data science project – like data cleaning, building the model(s), and then improving/refining said model – could take widely different amount of times to complete and often fed into each other anyways. The first data science projects were always the hardest to determine how long they would take, but after doing many of them, I developed an intuitive sense of how much time to budget.

toddler's standing in front of beige concrete stair

The fear of a blank page and resulting procrastination were major issues I had to overcome when working on a project. At the beginning of the project, before I had broken down the task and determined the best strategy for how to complete it, focusing could be difficult. If I was not careful, the stress of the blank page or complete openness of the new project could cause me to become distracted and want to do something else instead. In more extreme cases, this could lead to procrastinating in getting started at all.

To get my ideas on paper, during the first twenty-five-minute sprint of a new task, I would look through all my materials and brainstorm how I would complete the task. Through this, I would develop an initial to do list of items that I could do in the ensuing sprints. Even though my to do list almost always changed overtime, this allowed me to get started. The most important caveat was to make sure I did that planning session when I was able to handle such an open-ended task (something I discuss in more detail in Lesson #3).

I also addressed my tendency to procrastinate by creating my own stricter deadlines for when a project was due. Extreme procrastination (like putting off starting or completing something until the last minute when you must rush to complete a task in the last several hours before its deadline) would destroy my productivity. Having to work in a mad rush would prevent me from having the balance between work and rest I discussed in Lesson #1 necessary to work productively. And when I have a lot of tasks, rushing last minute for one project would prevent me from working ahead on future projects, which would have then caused me to fall behind on them and create a vicious cycle of procrastination.

Thus, I would set my own deadline a week or two prior to a project’s actual deadline. For example, if I had four weeks to write an assignment, I would set my own deadline of three weeks for a presentable draft, and no matter what, I would meet this deadline. I would treat this like my actual deadline and never missed it. This presentable draft may not be perfect or amazing yet but something that in a pinch I would feel comfortable turning in: a solid B or B- quality version, not the A or A+ awesomeness my perfectionist self prefers. I might need to proofread once or twice to smooth out some kinks, but it has all the basic components of the task or assignment done. That way, if I became too busy with other projects to do that proofreading, it was good enough quality that I could still turn it in without editing in a pinch.

In the remaining week, I would then work out those minor issues, combing it a few more times to make it top quality, but if another, higher priority project or issue arose during that final week needing more of my attention than I anticipated, I could still have something to turn in. By making sure I stayed ahead with an adequate draft, I never had to worry about falling behind and rushing to finish as assignment last minute, and being a week or so ahead provided a cushion or shock absorber to handling any unforeseeable issues without falling behind. Through this, I never missed a single deadline despite working multiple jobs and being a full-time student.

Lesson #3: Complete Tasks during the Optimal Time of Day

I have found that certain types of activities are easier for me during certain times of the day. For example, being a morning person, I do my best work first thing in the morning. Thus, I would perform my most open-ended, creative, and strategic types of tasks – like brainstorming and breaking down a new project, solving an open-ended problem, and writing an essay or report – then. In the early afternoon, I would try to schedule any meetings and interviews (if that worked in the other people’s schedules as well of course), and in the late afternoon and evening, I would complete more menial, plug-and-chug aspects of a project that need less intense mental thought and more rote implementation of what I came up with that morning, like writing the code of an algorithm I had mapped out in the morning or proofreading a paper I already wrote. This would ensure that I would be fresh and efficient when doing the complex, open-ended tasks and not wasting my time and energy trying to force myself to complete such tasks during the times of the day when I am naturally tired, slower, and less efficient.

Lesson #4: Leveraging Different Types of Tasks to Replenish Myself

As both a data scientist and anthropologist, I have had to do a wide variety of tasks, using many different skills, ranging from talking and interviewing people to math proofs and programming to scholarly and non-fiction writing. This variety has been something I could use to replenish myself. Each of these activities is in of itself stimulating to me, but doing one of them exclusively for long periods of time would become draining after a while.

In agriculture, certain crops use up certain nutrients in the soil (like corn depletes nitrogen particularly strongly), so farmers will often rotate between crops to replenish the nutrients in the soil from the previous crop. Likewise, I found rotating between several different types of activities helpful for rejuvenating and replenishing my mind from the last activity.

If I had to do a series of very logical tasks like math or programming, I might replenish with a social task as my next activity like interviewing or meeting with people, or if I interviewed people for several hours, I would next break from that by doing something solitary like programming or writing. I would use these rotations strategically to rest from one activity while still practicing and developing other skill sets.

Conclusion

These are the lessons I learned for how to sustain myself while working 80-100-hour weeks. The first lesson was crucial: developing an effective rhythm between work and rest that enabled me to work productively, efficiently, and sustainably. The other three were my specific strategies for how I created that rhythm. I developed and refined them during intense, busy periods of my life in order to still produce high quality work while maintaining my sanity. Hopefully, they are helpful food for thought for anyone else trying to develop his or her own time-management strategies.

Photo credit #1: Karim MANJRA at https://unsplash.com/photos/dtSCKE9-8cI

Photo credit #2: Jared Rice at https://unsplash.com/photos/NTyBbu66_SI

Photo credit #3: Carl Heyerdahl at https://unsplash.com/photos/KE0nC8-58MQ

Photo credit #4: Allie Smith at https://unsplash.com/photos/eXGSBBczTAY

Photo credit #5: NeONBRAND at https://unsplash.com/photos/KYxXMTpTzek

Photo credit #6: Alex Siale at https://unsplash.com/photos/qH36EgNjPJY

Photo credit #7: Jukan Tateisi at https://unsplash.com/photos/bJhT_8nbUA0

Photo credit #8: Ksenia Makagonova at https://unsplash.com/photos/Vq-EUXyIVY4

Photo credit #9: Dawid Zawila at https://unsplash.com/photos/-G3rw6Y02D0

Photo credit #10: Dennis Jarvis at https://www.flickr.com/photos/archer10/3555040506/

Methodological Complementarianism: Being the Mix in Mixed Methods

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Photo by RF._.studio on Pexels.com

I wrote this essay for my midterm for a course I took on conducting program evaluation as an anthropologist taught by Dr. Michael Duke at the University of Memphis Anthropology Master’s program. In it, I synthesize Donna Mertens’s discussion of employing mixed methods research for program evaluation work in her book, Mixed Methods Design in Evaluation, as a way to present the need for what I call methodological complementarianism.

Methodological complementarianism involves complementing those on the team one is working with by advancing for the complementary perspectives that the team needs. When conducting transdisciplinary work as applied anthropologists, instead of explicitly or implicitly seeking to maintain a “pure” anthropological approach, I think we should have a greater willingness to produce something anew in that environment, even if it no longer fits the “pure” boundaries of proper anthropology or ethnography but rather some kind of hybrid emerging out of the needs of the situation. Methodological complementarianism is one practical way to do that I have been exploring.

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The Stages of Learning a New Data or Programming Skill

Many people have admirably sought to learn data science, data analytics, a programming language, or some other data or programming skill in order to develop themselves professionally and/or seek a new career path. Excitingly, learning such skills has become significantly easier to do online. But this online learning can also foster unrealistic understandings of what learning one of these skills entails, since it can remove prospective learners from the physical community of experts who help introduce prospective learners to the expectations of that field.

The goal of this article is to help rectify that by explaining the basic steps typically needed to develop a mastery of a new data or programming skill. This will hopefully help inform high-level expectations for learning the skill would entail but also help you choose the right courses or set of courses to ensure you develop all three stages.

By data skill, I mean any data field like data science, data analytics, or data engineering, or any specific skill or practice within a data field that someone might seek to learn, and by programming skill I mean the skills necessary to learn and code in a programming language.

These are the three basic learning stages to master any of these topics:

Stage 1: Grasp the basic concepts of the topic
Stage 2: Complete a guided project
Stage 3: Complete a self-directed project

Stage 1: Grasping Basic Concepts

Grasping basic concepts entails learning the relevant vocabulary, syntax, and key approaches. Often programs teach each concept distinctly, one at a time. For example, when learning a new programming language, you might learn the major commands and syntax rules, and for data science, you might learn about each of the most prominent machine learning models one at a time.

This is different from applying the concepts widely, and at this stage, you may not be able to handle mixing all the concepts together in a complex problem yet (that’s Stage 2). Programs often teach the material at this point sequentially (even though that can be difficult for nonlinear learners).

For example, W3Schools provides grounded Stage 1 teaching for most programming languages and data science skills. They provide sequential exercises working through the basic syntax components of a new language, ever so slightly increasing in complexity along the way.

Now, only performing the first stage does not entail a full mastery of topic. After practicing each piece one at a time, you must also transition into Stage 2 where you start to learn how to combine them when completing a more complex problem.

Stage 2: Guided Project

Here you practice putting all the pieces together through a guided project(s). This guided project is a model for how each of the components fit together in an actual project. I liken these to building a Lego kit: following step-by-step instructions to build a cool model (instead of building your own object from scratch, which is Stage 3). They hold your hand through its completion to illustrate what putting all of the isolated skills and concepts together during a complicated project would entail.

Stage 3: Independent Project

In the third stage, you bring everything have learned together to complete a project on your own. Unlike in Stage 2, when they held your hand, you now have the freedom to struggle, which is necessary to learn. You are developing the skills involved in forming and carrying out a project on your own.

At the same time, you are learning what it looks like to implement those skills “in the wild” of a real-life project. In the previous stages, instructors often coddle their students: providing cleaned and perfectly ready-to-do example problems that you might find in a textbook, necessary to learn the basic concepts. Like a Lego kit, the components of the project have been groomed to make what you are producing. In Stage 3, you often start to experience the types of messiness common in real-world projects, when you have to find the pieces you need and/or figure out how to make do with the ones what you have.

For example, among data science learners, this stage is when students first learn to deal with the complexities of finding the right data for their problem; determining the best questions for a given dataset; and/or cleaning inconsistent data. Beforehand, most examples probably had already cleaned data that matched the specific task they were built for.

A certain amount of trial by fire is often needed to learn how to develop your own project. Your instructor(s) might take a little more of a backseat role during this process, looking over what you have done, answering any questions you might have, and nudging you when necessary. In my experience, exploring strategies yourself is the best way to learn Stage 3. Hopefully, at the end of it all, you will produce a nifty project that you can show prospective employers or whoever else you might wish to impress.

Conclusion

These are the three most common stages to develop initial mastery of a new data or programming skill or field. Now, they are the skill levels generally necessary to learn the new skill, but there are plenty of further levels of learning after you complete these. For example, grasping basic data science concepts, completing a guided project, and learning how to conduct your own self-directed data science project would be enough to make you a new inductee into the data science community, but you would still be a newbie data scientist. It is only the tip of the iceberg for what you can learn and how you would grow as a data scientist.

Now, despite calling them stages, not everyone learns them in sequential order, especially given the variety of extenuating circumstances and learning styles. For example, some might complete all three stages for a specific subset of skills in the field they are learning, and then go back to Stage 1 for another subset. Most education programs will include all three stages, more or less in order.

Some education programs, however, might completely lack or provide insufficient resources for one or two stages. Assessing whether a program adequately includes all three can be an effective way to determine how good they are at teaching and whether they are worth your money and/or time. When choosing to learn a new skill, I would recommend a program or combination of programs that includes all three. If a program you want to do or are currently completing lacks one or two of these stages, you can try to find another (hopefully free) way to complete that stage yourself online. For example, online courses and tutorials very frequently fail to provide Stage 3 (and in some cases, Stage 2), so after you complete one, I would recommend finding a project to work on.

Finally, when you are encountering a difficulty learning, it might be because you need to go backwards to a previous stage. For example, when many learners move to Stage 2, they must periodically swing back into Stage 1 to review a few core concepts when they see those concepts applied in a new way. Similarly, when completing a project in Stage 3, there is nothing wrong with reviewing Stage 2 or even Stage 1 materials.  

Now, be careful because you can falsely attribute this. Learning anything can be frustrating. Sometimes the difficulties you are having are not rooted in the need to review or relearn past material, but you simply need to push through with the new material until you start to get it. In those cases, some students revert backwards into a set of material in which they can feel safe and confident instead of challenging themselves. Even in those cases, however, like rocking a car by going into reverse and then drive to get over a bump, quickly going backwards can help launch you forward over the hurdle. In such cases, what is most important is to know yourself – your learning tendencies and how you typically respond – and check in as much as you can with instructors and/or experts in the field who have been there and done that to help you determine the best ways to overcome whatever challenge you are having.  

Photo credit #1: Jukan Tateisi at https://unsplash.com/photos/bJhT_8nbUA0

Photo credit #2: qimono at https://pixabay.com/illustrations/cog-wheels-gear-wheel-machine-2125178/

Photo credit #3: Bonneval Sebastien at https://unsplash.com/photos/lG-6_ox_UXE

Photo credit #4: Holly Mandarich at https://unsplash.com/photos/UVyOfX3v0Ls

Photo credit #5: George Bakos at https://unsplash.com/photos/VDAzcZyjun8

What Is Ethnography: A Short Description for the Unsure

What is ethnography, and how has it been used in the professional world? This article is a quick and dirty crash course for someone who has never heard of (or knows little about) ethnography.

Anthropology at its most basic is the study of human cultures and societies. Cultural anthropologists generally seek to understand current cultures and societies by conducting ethnography.

In short, ethnography involves seeking to understand the lived experiences of a particular culture, setting, group, or other context by some combination of being with those in that context (called participant-observation), interviewing or talking with them, and analyzing what happens and what is produced in that context.

It is an umbrella term for a set of methods (including participant-observation, interviews, group interviews or focus groups, digital recording, etc.) employed with that goal, and most ethnographic projects use some subset of these methods given the needs of the specific project. In this sense, it is similar to other umbrella methodologies – like statistics – in that it encapsulates a wide array of different techniques depending on the context.

two woman chatting

One conducts ethnographic research to understand something about the lived experiences of a context. In the professional world, for example, ethnography is frequently useful in the following contexts:

  1. Market Research: When trying to understand customers and/or users in-depth
  2. Product Design: When trying to design or modify a product by seeing how people use it in action
  3. Organizational Communication and Development: When trying to understand a “people problem” within an organization.

In this article, I expound in more detail on situations where ethnographic research is useful in in professional settings.

Ethnographies are best understood through examples, so the table below include excellent example ethnographies and ethnographic researchers in various industries/fields:

Project Area
Computer Technology Development at Intel Market Research
Vacuum CMarket Research Examples Market Research
Psychiatric Wards in Healthcare Organizational Management
Self-Driving Cars at Nissan Artificial Intelligence
Training of Ethnography in Business Schools Education of Ethnography

These, of course, are not the only some situations where ethnography might be helpful. Ethnography is a powerful tool to develop a deep understanding of others’ experiences and to develop innovative and strategic insights.

Photo credit #1: Paolo Nicolello at https://unsplash.com/photos/hKVg7ldM5VU.

Photo credit #2: mentatdgt at https://www.pexels.com/photo/two-woman-chatting-1311518/.

What Is Data Science and Machine Learning? A Short Guide for the Unsure

 What is data science, and what is machine learning? This is a short overview for someone who has never heard of either.

What Is Data Science?

 In the abstract, data science is an interdisciplinary field that seeks to use algorithms to organize, process, and analyze data. It represents a shift towards using computer programing, specifically machine learning algorithms, and other, related computational tools to process and analyze data.

By 2008, companies starting using the term data scientists to refer to a growing group of professionals utilizing advanced computing to organize and analyze large datasets,[i] and thus from the get-go, the practical needs of professional contexts have shaped the field. Data science combines strands from computer science, mathematics (particularly statistics and linear algebra), engineering, the social sciences, and several other fields to address specific real-world data problems.

On a practical level, I consider a data scientist someone who helps develop machine learning algorithms to analyze data. Machine learning algorithms form the central techniques/tools around what constitutes data science. For me personally, if it does not involve machine learning, it is not data science.

What Is Machine Learning?

 Machine learning is a complex term: What to say that a machine “learns”? Overtime data scientists have provided many intricate definitions of machine learning, but its most basic, machine learning algorithms are algorithms that adapt/modify how their approach to a task based on new data/information overtime.

Herbert Simon provides a commonly used technical definition: “Learning denotes changes in the system that are adaptive in the sense that they enable the system to do the task or tasks drawn from the same population more efficiently and more effectively the next time.”[ii] As this definition implies, machine learning algorithms adapt by iteratively testing its performance against the same or similar data. Data scientists (and others) have developed several types of machine learning algorithms, including decision tree modeling, neural networks, logistic regression, collaborative filtering, support vector machines, cluster analysis, and reinforcement learning among others.

Data scientists generally split machine learning algorithms into two categories: supervised and unsupervised learning. Both involve training the algorithm to complete a given task but differ on how they test the algorithm’s performance. In supervised learning, the developer(s) provide a clear set of answers as a basis for whether the prediction is correct; while for unsupervised learning, whether the algorithm’s performance is much more open-ended. I liken the difference to be like the exams teachers gave us in school: some tests, like multiple choice exams, have clear, right and wrong answers or solutions, but other exams, like essays, are open-ended with qualitative means of determining goodness. Just like the nature of the curriculum determines the best type of exam, which type of learning to performs depends on the project context and nature of the data.

Here are four instances where machine learning algorithms are useful in these types of tasks:

  1. Autonomy: To teach computers to do a task without the direct aid/intervention of humans (e.g. autonomous vehicles)
  2. Fluctuation: Help machines adjust when the requirements or data change over time
  3. Intuitive Processing: Conduct (or assist in) tasks humans do naturally but are unable to explain how computationally/algorithmically (e.g. image recognition)
  4. Big Data: Breaking down data that is too large to handle otherwise

Machine learning algorithms have proven to be a very powerful set of tools. See this article for a more detailed discussion of when machine learning is useful.

[i] Berkeley School of Information. (2019). What is Data Science? Retrieved from https://datascience.berkeley.edu/about/what-is-data-science/.

[ii] Simon in Kononenko, I., & Kukar, M. (2007). Machine Learning and Data Mining. Elsevier: Philadelphia.

Photo credit #1: Frank V at https://unsplash.com/photos/zbLW0FG8XU8

Photo credit #2: Brett Jordan at https://unsplash.com/photos/HzOclMmYryc

Recently Published Article: “Anthropology by Data Science”

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I am pleased to announce that the Annals of Anthropological Practice has accepted my article “Anthropology by Data Science.” https://anthrosource.onlinelibrary.wiley.com/doi/10.1111/napa.12169. In it, I reflect on the relationship anthropologist have cultivated with data science as a discipline and the importance of integrating machine learning techniques into ethnographic practice.

Annals of Anthropological Practice is overseen by the National Association for the Practice of Anthropology (NAPA) within the American Anthropological Association. Thank you, NAPA, for publishing my article and thank you to all the unnamed editors and reviewers in the process.

Interdisciplinary Anthropology and Data Science Master’s Thesis: A Quick and Dirty Project Summary

This is a quick and dirty summary of my master’s practicum research project with Indicia Consulting over the summer of 2018. For anyone interested in more detail, here is a more detailed report, and here is the final report with Indicia. 

Background

My practicum was the sixth stage of a several year-long research project. The California Energy Commission commissioned this larger project to understand the potential relationship between individual energy consumption and technology usage. In stages one through five, we isolated certain clusters of behavior and attitudes around new technology adoption – which Indicia called cybersensitivity – and demonstrated that cybersensitivity tended to associate with a willingness to adopt energy-saving technology like smart meters.

This led to a key question: How can one identify cybersensivity among a broader population such as a community, county, or state? Answering this question was the main goal of my practicum project.

In the past stages of the research project, the team used ethnographic research to establish criteria for whether someone was a cybersensitive based on several hours of interviews and observations about their technology usage. These interviews and observations certainly helped the research team analyze behavioral and attitudinal patterns, determine what patterns were significant, and develop those into the concept of cybersensitivity, but they are too time- and resource-intensive to perform with an entire population. One generally does not have the ability to interview everyone in a community, county, or state. I sought to address this directly in my project.

TaskTimelineTask NameResearch TechniqueDescription
Task 1June 2015-Sept 2018General Project TasksAdministrative (N/A)Developed project scope and timeline, adjusting as the project unfolds
Task 2July 2015 – July 2016Documenting and analyzing emerging attitudes, emotions, experiences, habits, and practices around technology adoptionSurveyConducted survey research to observe patterns of attitudes and behaviors among cybersensitives/awares.
Task 3Sept 2016 – Dec 2016Identifying the attributes and characteristics and psychological drivers of cybersensitivesInterviews and Participant-ObservationConducted in-depth interviews and observations coding for psych factor, energy consumption attitudes and behaviors, and technological device purchasing/usage.
Task 4*Sept 2016 – July 2017Assessing cybersensitives’ valence with technologyStatistical AnalysisTested for statistically significant differences in demographics, behaviors, and beliefs/attitudes between cyber status groups
Task 5Aug 2017 – Dec 2018  Developing critical insights for supporting residential engagement in energy efficient behaviorsStatistical AnalysisAnalyzed utility data patterns of study participants, comparing it with the general population.
Task 6March 2018 – Aug 2018Recommending an alternative energy efficiency potential modelDecision Tree ModelingConstructed decision tree models to classify an individual’s cyber status

Project Goal

The overall goal for the project was to produce a scalable method to assess whether someone exhibits cybersensitivity based on data measurable across an entire population. In doing this, the project also helped address the following research needs:

  1. Created a method to further to scale across a larger population, assessing whether cybersensitives were more willing to adopt energy saving technologies across a community, county, or state
  2. Provided the infrastructure to determine how much promoting energy-saving campaigns targeting cybersensitives specifically would reduce energy consumption in California
  3. Helped the California Energy Commission determine the best means to reach cybersensitives for specific energy-saving campaigns

The Project

I used machine learning modeling to create a decision-making flow to isolate cybersensitives in a population. Random forests and decision trees produced the best models for Indicia’s needs: random forests in accuracy and robustness and decision trees in human decipherability. Through them, I created a programmable yet human-comprehensible framework to determine whether an individual is cybersensitive based on behaviors and other characteristics that an organization could be easily assess within a whole population. Thus, any energy organization could easily understand, replicate, and further develop the model since it was both easy for humans to read and encodable computationally. This way organizations could both use and refine it for their purposes.

Conclusion

This is a quick overview of my master’s practicum project. For more details on what modeling I did, how I did it, what results it produced, and how it fit within the wider needs of the multi-year research project, please see my full report.

I really appreciated the opportunity it posed to get my hands dirty integrating ethnography and data science to help address a real-world problem. This summary only scratches the surface of what Indicia did with the Californian Energy Commission to encourage sustainable energy usage societally. Hopefully, though, it will inspire you to integrate ethnography and data science to address whatever complex questions you face. It certainly did for me.

Thank you to Susan Mazur-Stommen and Haley Gilbert for your help in organizing and completing the project. I would like to thank my professorial committee at the University of Memphis – Dr. Keri Brondo, Dr. Ted Maclin, Dr. Deepak Venugopal, and Dr. Katherine Hicks – for their academic support as well.