As a data scientist and ethnographer, I have worked on many types of research projects. In professional and business settings, I am excited by the enormous growth in both data science and ethnography but have been frustrated by how, despite recent developments that make them more similar, their respective teams seem to be growing apart and competitively against each other.
Within academia, quantitative and qualitative research methods have developed historically as distinct and competing approaches as if one has to choose which direction to take when doing research: departments or individual researchers specialize in one or the other and fight over scarce research funding. One major justification for this division has been the perception that quantitative approaches tend to be prescriptive and top-down compared with qualitative approaches which tend to be to descriptive and bottom-up. That many professional research contexts have inherited this division is unfortunate.
Recent developments in data science draw parallels with qualitative research and if anything, could be a starting point for collaborative intermingling. What has developed as “traditional” statistics taught in introductory statistics courses is generally top-down, assuming that data follows a prescribed, ideal model and asking regimented questions based on that ideal model. Within the development of machine learning been a shift towards models uniquely tailored to the data and context in question, developed and refined iteratively.[i] These trends may show signs of breaking down the top-down nature of traditional statistics work.
If there was ever a time to integrate quantitative data science and qualitative ethnographic research, it is now. In the increasingly important “data economy,” understanding users/consumers is vital to developing strategic business practices. In the business world, both socially-oriented data scientists and ethnographers are experts in understanding users/consumers, but separating them into competing groups only prevents true synthesis of their insights. Integrating the two should not just include combining the respective research teams and their projects but also encouraging researchers to develop expertise in both instead of simply specializing in one or the other. New creative energy could burst forth when we no longer treat these as distinct methodologies or specialties.
[i] Nafus, D., & Knox, H. (2018). Ethnography for a Data-Saturated World. Manchester: Manchester University Press, 11-12.
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:
Newcomers to data science or artificial intelligence frequently ask me the best programming language to learn to build machine learning algorithms. Thus, I wrote this article as a reference for anyone who wants to know the answer to that question. These are what I consider the three most important languages, ranked in terms of usefulness based on both overall popularity within the data science community and my own personal experiences:
Best Programming Languages for Machine Learning:
#1 Choice: Python
#2 Choice: R
#3 Choice: Java
#4 Choice: C/C++
#1 Programming Language: Python
Python is the most popular language to use for machine learning and for three good reasons.
First, it’s package-based style allows you to utilize efficient machine learning and statistical packages that others have made, preventing you from having to constantly reinvent the wheel for common problems. Many if not most of the best packages (like NumPy, pandas, scikit learn, etc.) are in Python. This almost allows you to “cheat” when programing machine learning algorithms.
Second, Python is a powerful and flexible all-purpose language, so if you are building a machine learning algorithm to do something, then you can easily build the code for the other overall product or system in which you will use the algorithm without having to switch languages or softwares. It supports object-oriented, functional, and procedure-oriented programming styles, giving the programmer flexibility in how to code, allowing you to use whatever style or combination of various styles you like best or fits the specific context.
Third, unlike a language like Java or C++, Python does not require elaborate setup to program a single line of code. Even though you can easily build the coding infrastructure if you need to, if you only need to run a simple command or test, you can start immediately.
When I program in Python, I personally love using Jupyter Notebook, since its interface allows me to both code and to easily show my code and findings as a report or document. Another data scientist can simultaneously read and analyze my code and its output at the same time. I personally wish more data scientists published their papers and reports in Jupyter Notebook or other notebooks like it because of this.
If you have time to learn a single programming language for machine learning, I would strongly recommend it be Python. The next three languages, R, Java, and C++, do not match its ease and popularity within data science.
#2 Programming Language: R
R is a popular language for statisticians, a programming language that is specifically tailored for advanced statistical analysis. It includes many well-developed packages for machine learning but is not as popular with data scientists as Python. For example, in Towards Data Science’s survey, 57% of data scientists reported using Python, with 33% prioritizing it, and only 31% reported using R, with 17% prioritizing it. This seems to show that R is a complementary, not primary language for data science and machine learning. Most R packages have their equivalent in Python (and to some extent the other way around). Unlike Python, which is an all-purpose language, able to do other wonders other than analyzing data and developing machine learning algorithms, R is specifically tailored to statistics and data analysis, not able to do much beyond that. Saying this, though, R programmers are increasingly developing more and more packages for it, allowing it to do more and more.
#3 Programming Language: Java
Java was once the most popular language around, but Python has dethroned it in the last few years. As an avid Java programmer who programs in Java for fun, it breaks my heart to put it so far down the list, but Python is clearly a better language for data science and machine learning. If you are working in an organization or other context that still uses Java for part or all of its software infrastructure, then you may be stuck using it, but most recent developments, particularly in machine learning, have occurred in Python and in R (and a few other languages). Thus, if you use Java, you’ll frequently find yourself having to unnecessarily reinventing the wheel.
Plus, one major con of Java is that conducting quick, on-the-go analysis is not possible, since one must write a whole coding system before one can do a single line of code. Java can be popular in certain contexts, where the surrounding applications/software that utilize the machine learning algorithms are in Java, common in finance, front-end development, and companies that have been using Java-based software.
#4 Programming Languages: C/C++
The same Towards Data Science survey I mentioned above lists C/C++ as the second most popular data science and machine learning language after Python. Java follows them closely, yet I included Java and not C/C++ as third because I personally find Java to be a better overall language than C or C++. In C or C++, you may frequently find yourself reinventing the wheel – having to develop machine learning algorithms that others have already built in Python – but in some backend systems that have been built C or C++ like in engineering and electronics, you do not have much of an option. C++ has a similar problem with Java as well: lacking the ability to do quick on-the-go coding without having to build a whole infrastructure.
Conclusion
For a beginner to the data science scene, learning a single programming is the most helpful way to enter the field. Use learning a programming language to assess whether data science is for you: if you struggle and do not like programming, then developing machine learning algorithms for a living is probably not a good fit for you.
Many groups are trying to develop softwares that enable machine learning without having to program: DataRobot, Auto-WEKA, RapidMiner, BigML, and AutoML, among many others. The pros and cons and successes and failures of these softwares warrants a separate blog post to itself (one I intend to write eventually). As of now though, these have not replaced programming languages in either practical ability to develop complex machine learning algorithms and in demonstrating that you have the technical computational/programming skills for the field.
For a beginner to the data science scene, learning a single programming is the most helpful way to enter the field. Use learning a programming language to assess whether data science is for you: if you struggle and do not like programming, then data science where you would be developing machine learning algorithms for a living is probably not a good fit for you. Depending on where you work or type of field/tasks you are doing, you might end up using the language(s) or software(s) your team works with so that you can easily work jointly on projects with them. For some areas of work or tasks might prefer certain packages and languages. If you demonstrate that you can already know a complex programming language like Python (or Java or C++), even if that is not the preferred language of their team, then you will likely demonstrate to any hiring manager that you can learn their specific language or software.
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:
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 and data change over time
Intuitive
Processing: Conduct or assist in tasks humans do but
are unable to explain how computationally/algorithmically (e.g. image
recognition)
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
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:
Tasks humans frequently perform
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
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
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
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.
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.
