In this second part of my interview with Randy Au, he discusses the techniques he used to teach himself to code and his approach to programming and data science as a social scientist.
Prior to joining Google, he spent a decade as a mixture of a data analyst, data scientist, and data engineer at various startups in New York City and before that, studied Communications. In his newsletter, he discusses data science topics like data collection and data quality from a social science perspective. Outside of work he often engages in far too many hobbies, taken to absurd lengths.
Click here to learn more about the Interview Series this is a part of.
Randy Au, a Quantitative UX Researcher at Google, explains how he leverages his backgrounds in communication, statistics, and programming as a quantitative UX researcher in Google Cloud to analyze and improve Cloud Storage products.
Prior to joining Google, he spent a decade as a mixture of a data analyst, data scientist, and data engineer at various startups in New York City and before that, studied Communications. In his newsletter, he discusses data science topics like data collection and data quality from a social science perspective. Outside of work he often engages in far too many hobbies, taken to an absurd lengths.
Click here to learn more about the Interview Series.
Here is the fourth and final part of my interview with Gideon Singer, Director of Spacial Data Science at Litterati, for my Interview Series. He describes the strategies he uses to collect data as a garbologist and data scientist.
Gideon Singer is an applied anthropologist in the business of exploring societies through the waste, litter, rubbish, and other detritus they leave behind. As a self-proclaimed digital garbologist, his work juxtaposes digital ethnography with archaeology and spatial data science.
Here is the third part of my interview with Gideon Singer, Director of Spacial Data Science at Litterati, for my Interview Series. He discusses how the interconnections he has found between data science and garbology.
Gideon Singer is an applied anthropologist in the business of exploring societies through the waste, litter, rubbish, and other detritus they leave behind. As a self-proclaimed digital garbologist, his work juxtaposes digital ethnography with archaeology and spatial data science.
Here is the second part of my interview with Gideon Singer, Director of Spacial Data Science at Litterati, for my Interview Series. He describes garbology is and what kind of work he does as a data scientist garbologist.
Gideon Singer is an applied anthropologist in the business of exploring societies through the waste, litter, rubbish, and other detritus they leave behind. As a self-proclaimed digital garbologist, his work juxtaposes digital ethnography with archaeology and spatial data science.
I interviewed Gideon Singer, Director of Spacial Data Science at Litterati, for my Interview Series. He discusses his mission to combine garbology, anthropology, and data science to better understand humanity and the trash we leave behind. In this first part, he describes the connections he has found between these various fields.
Gideon Singer is an applied anthropologist in the business of exploring societies through the waste, litter, rubbish, and other detritus they leave behind. As a self-proclaimed digital garbologist, his work juxtaposes digital ethnography with archaeology and spatial data science.
I suspect everyone has seen a bad graph, a mess of bars, lines, pie slices, or what have you that you dreaded having to look at. Maybe you have even made one, which you look at today and wonder what on earth you were thinking.
These graphs violate the most basic graph-making rule in data visualization:
A graph is like a sentence, expressing one idea.
This rule applies to all uses of graphs, whether you are a data scientist, data analyst, statistician, or just making graphs for your friends for fun.
In grade school, your grammar teachers likely explained that a sentence, at its most basic, expresses on thought or idea. Graphs are visual sentences: they should state one and only one thought or idea about the data.
When you look at a graph, you should be able to say, in one sentence, what the graph is saying: such as “Group A is greater than Group B,” or “Y at first improved but is now declining.” If you cannot, then you have yourself a run-on graph.
For example, the above graph is trying to say too many statements: trying to depict the immigration patterns of twenty-two different countries over the course of nearly a century. There are likely useful statements in this data, but the representation as one graph prevents a viewer/reader from being able to easily decipher them.
Likewise, this graph shows way too many lens sizes to meaningfully express a single, coherent idea, leaving the reader/viewer struggling to determine which fields to focus on.
Potential Objection #1: But I have more to say about the data than a single statement.
Great! Then provide more than one graph. Say everything you need to say about the data; just use one graph for each of your statements.
Don’t fall into the One-Graph-to-Rule-Them-All Fallacy: trying to use one graph to express all your statements about the data that ends up a visual mess of incomprehensibility. Create multiple easy-to-read graphs where each graph demonstrates one of your points at a time. Condensing everything into one graph just prevents your viewers from determining what you have to say at all.
One-Graph-to-Rule-Them-All Fallacy: Trying to use one graph to express all your thoughts about the data that ends up a visual mess of incomprehensibility
Instead, use one graph for each of your points
Potential
Objection #2: I want the viewers to interpret the findings for themselves, not
just impart my own ideas/conclusions.
Fair point. When presenting/communicating data, there is a time for showing your own insights and a time to open-endedly display the information for your viewers/readers to interpret for themselves. Graphs are tools for the former, and for the latter, use tables. Tables, among other potential uses, convey a wide scope of information for the reader/viewer to interpret on their own.
Remember that first example above about U.S. immigration from various parts of Europe? A table (see below) would convey that information much more easily and allow readers to track whatever places, patterns, or questions they would to learn about. Are you in a situation where you would like to report a large amount of information that your readers can use for their own purposes? Then tables are a much better starting point than graphs.
Some situations require that I lean towards sharing my insights/analysis and others towards encouraging my readers/viewers to form their own conclusions, but since most situations require a combination of the two, I generally combine graphs and tables. I try, when I can, to put smaller tables in the document or slides themselves and, when I cannot, include full tables in an Appendix.
Potential Objection #3: My main idea/point has multiple subpoints.
Many sentences have multiple subpoints needed to express the single idea as well, which does not prevent the sentence structure from meaningfully capturing those ideas. The fancy grammar word for such a subpoint is a claus. Even though some sentences are simple and straightforward with only one subject and predicate, many (like this very sentence) require multiple sets of subjects and predicates to express its thought.
Likewise, some graphical ideas require multiple subordinate or compounded subpoints, and there are types of graphs that allow this. Consider Joint Plots, like the one below. To present the relationships and combined distribution between the two variables adequately, they also display each variable’s individual distributions above and to the right. That way, the viewer can see how both distributions might be influencing the combined distribution. Thus, it displays each variable’s distribution on the side like a subordinate clause.
The darker colors in this graph signify a higher density of data points, showing the combined joint distribution of the variables.
These are advanced graphs to make, since like with multi-part sentences, one must present the subpoints carefully to make clear what the main point is. Multi-part sentences, likewise, require carefulness in how to organize multiple clauses cohesively. I intend to write a post later describing how to develop these multi-part graphs in more detail.
The general rule still applies for these more complicated graphs:
Can you summarize what the graph is saying in one coherent sentence?
If you cannot, do not use/show that graph. Our brains are very good at intuiting whether a sentence carries one thought, so use this to determine whether your graph is effective.
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.
