Hello, my name is Stephen Paff. I am a data scientist and an ethnographer. The goal of this blog is to explore the integration of data science and ethnography as an exciting and innovative way to understand people, whether consumers, users, fellow employees, or anyone else.
I want to think publicly. Ideas worth having develop in
conversation, and through this blog, I hope to present my integrative vision so
that others can potentially use it to develop their own visions and in turn
help shape mine.
Please Note: Because my blog straddles two technical areas, I will split my posts based on how in-depth they go into each technical expertise. Many posts I will write for a general audience. I will write some posts, though, for data scientists discussing technical matters within that field, and other posts will focus on technical topics withn ethnography for anthropologists and other ethnographers. At the top of each post, I will provide the following disclaimers:
Data science’s popularity has grown in the last few years, and many have confused it with its older, more familiar relative: statistics. As someone who has worked both as a data scientist and as a statistician, I frequently encounter such confusion. This post seeks to clarify some of the key differences between them.
Before I get into their differences, though, let’s define them. Statistics as a discipline refers to the mathematical processes of collecting, organizing, analyzing, and communicating data. Within statistics, I generally define “traditional” statistics as the the statistical processes taught in introductory statistics courses like basic descriptive statistics, hypothesis testing, confidence intervals, and so on: generally what people outside of statistics, especially in the business world, think of when they hear the word “statistics.”
Data science in its most broad sense is the multi-disciplinary science of organizing, processing, and analyzing computational data to solve problems. Although they are similar, data science differs from both statistics and “traditional” statistics:
Difference
Statistics
Data Science
#1
Field of Mathematics
Interdisciplinary
#2
Sampled Data
Comprehensive Data
#3
Confirming Hypothesis
Exploratory Hypotheses
Difference
#1: Data Science Is More than a Field of Mathematics
Statistics is a field of mathematics; whereas, data science refers to more than just math. At its simplest, data science centers around the use of computational data to solve problems,[i] which means it includes the mathematics/statistics needed to break down the computational data but also the computer science and engineering thinking necessary to code those algorithms efficiently and effectively, and the business, policy, or other subject-specific “smarts” to develop strategic decision-making based on that analysis.
Thus, statistics forms a crucial component of data science, but data science includes more than just statistics. Statistics, as a field of mathematics, just includes the mathematical processes of analyzing and interpreting data; whereas, data science also includes the algorithmic problem-solving to do the analysis computationally and the art of utilizing that analysis to make decisions to meet the practical needs in the context. Statistics clearly forms a crucial part of the process of data science, but data science generally refers to the entire process of analyzing computational data. On a practical level, many data scientists do not come from a pure statistics background but from a computer science or engineering, leveraging their coding expertise to develop efficient algorithmic systems.
Difference
#2: Comprehensive vs Sample Data
In statistical studies, researchers are often unable to analyze the entire population, that is the whole group they are analyzing, so instead they create a smaller, more manageable sample of individuals that they hope represents the population as a whole. Data science projects, however, often involves analyzing big, summative data, encapsulating the entire population.
The tools of traditional statistics work well for scientific studies, where one must go out and collect data on the topic in question. Because this is generally very expensive and time-consuming, researchers can only collect data on a subset of the wider population most of the time.
Recent developments in computation, including the ability to gather, store, transfer, and process greater computational data, have expanded the type of quantitative research now possible, and data science has developed to address these new types of research. Instead of gathering a carefully chosen sample of the population based on a heavily scrutinized set of variables, many data science projects require finding meaningful insights from the myriads of data already collected about the entire population.
Difference
#3: Exploratory vs Confirming
Data scientists often seek to build models that do something with the data; whereas, statisticians through their analysis seek to learn something from the data. Data scientists thus often assess their machine learning models based on how effectively they perform a given task, like how well it optimizes a variable, determines the best course of action, correctly identifies features of an image, provides a good recommendation for the user, and so on. To do this, data scientists often compare the effectiveness or accuracy of the many models based on a chosen performance metric(s).
In traditional statistics, the questions often center around using data to understand the research topic based on the findings from a sample. Questions then center around what the sample can say about the wider population and how likely its results would represent or apply to that wider population.
In contrast, machine learning models generally do not seek to explain the research topic but to do something, which can lead to very different research strategy. Data scientists generally try to determine/produce the algorithm with the best performance (given whatever criteria they use to assess how a performance is “better”), testing many models in the process. Statisticians often employ a single model they think represents the context accurately and then draw conclusions based on it.
Thus, data science is often a form of exploratory analysis, experimenting with several models to determine the best one for a task, and statistics confirmatory analysis, seeking to confirm how reasonable it is to conclude a given hypothesis or hypotheses to be true for the wider population.
A lot of scientific research has been theory confirming: a scientist has a model or theory of the world; they design and conduct an experiment to assess this model; then use hypothesis testing to confirm or negate that model based on the results of the experiment. With changes in data availability and computing, the value of exploratory analysis, data mining, and using data to generate hypotheses has increased dramatically (Carmichael 126).
Data science as a discipline has been at the
forefront of utilizing increased computing abilities to conduct exploratory work.
Conclusion
A data scientist friend of mine once quipped to me that data science simply is applied computational statistics (c.f. this). There is some truth in this: the mathematics of data science work falls within statistics, since it involves collecting, analyzing, and communicating data, and, with its emphasis and utilization of computational data, would definitely be a part of computational statistics. The mathematics of data science is also very clearly applied: geared towards solving practical problems/needs. Hence, data science and statistics interrelate.
They differ, however, both in their formal definitions and practical understandings. Modern computation and big data technologies have had a major influence on data science. Within statistics, computational statistics also seeks to leverage these resources, but what has become “traditional” statistics does not (yet) incorporate these. I suspect in the next few years or decades, developments in modern computing, data science, and computational statistics will reshape what people consider “traditional” or “standard” statistics to be a bit closer to the data science of today.
For more details, see the following useful resources:
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.
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:
Market Research: When trying to understand customers and/or users in-depth
Product Design: When trying to design or modify a product by seeing how people use it in action
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:
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.
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 unsupervisedlearning. 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:
Autonomy: To teach computers to do a task without the direct aid/intervention of humans (e.g. autonomous vehicles)
Fluctuation: Help machines adjust when the requirements or data change over time
Intuitive Processing: Conduct (or assist in) tasks humans do naturally but are unable to explain how computationally/algorithmically (e.g. image recognition)
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.
