Random thoughts about random subjects… From science to literature and between manga and watercolours, passing by data science and rugby; including film, physics and fiction, programming, pictures and puns.
As you know I am writing a new book. This time it is a book about statistics and data visualisation using Python as the main language to analyse data. It was thinking that I was a bit behind with my plan for the book, but I managed to surprise myself by being bang on time completing the first chapter.
This is the introductory chapter where we cover some background on the importance of statistics, a bit of history and the personalities behind some concepts widely used in stats and data visualisation. We then cover some background in formulating questions to be answered with data and how to communicate our results.
This year, 2020, the word Nightingale has acquired new connotations. It is no longer just a word to refer to a passerine bird with beautiful and powerful birdsong, it is the name that NHS England has given to the temporary hospitals set up for the COVID-19 pandemic. In normal circumstances it is indeed a very good name to use for a hospital, but given the circumstances, it becomes more poignant. It is even more so considering the fact that this year, 2020, is the bicentenary go Florence Nightingale’s birth.
Florence Nightingale was born on 12th May, 1820 in Florence, Italy (hence the name!) and became a social reformer, statistician, and the founder of modern nursing. She became the first woman to be elected to be a Fellow of the Royal Society in 1874.
With the power of data, Nightingale was able to save lives and change policy. Her analysis of data from the Crimean War was compelling and persuasive in its simplicity. It allowed her and her team to pay attention to time – tracking admissions to hospital and crucially deaths – on a month by month basis. We must remember that the power of statistical tests as we know today were not established tools and the work horse of statistics, regression, was decades in the future. The data analysis presented in columns and rows as supported by powerful graphics that many of us admire today.
In 2014 had an opportunity to admire her Nightingale Roses, or to use its formal name polar area charts, in the exhibition Science is Beautiful at the British Library.
Florence Nightingale’s “rose diagram”, showing the Causes of Mortality in the Army in the East, 1858. Photograph: /British Library
These and other charts were used in the report that she later published in 1858 under the title “Notes in Matters Affecting the Health, Efficiency, and Hospital Administration of the British Army”. The report included charts of deaths by barometric pressure and temperature, showing that deaths were higher in hotter months compared to cooler ones. In polar charts shown above Nightingale presents the decrease in death rates that have been achieved. Let’s read it from her own hand; here is the note the accompanying the chart above:
The areas of the blue, red & black wedges are each measured from the centre as the common vortex.
The blue wedges measured from the centre of the circle represent area for area the deaths from Preventible or Mitigable Zymotic diseases, the red wedged measured from the centre the deaths from wounds, & the black wedged measured from the centre the deaths from all other causes.
The black line across the read triangle in Nov. 1854 marks the boundary of the deaths from all other caused during the month.
In October 1854, & April 1855, the black area coincides with the red, in January & February 1855, the blue area coincides with the black.
The entire areas may be compared bu following the blue, the read & the black lines enclosing them.
Nightingale recognised that soldiers were dying from other causes: malnutrition, poor sanitation, and lack of activity. Her aim was to improve the conditions of wounded soldiers and improve their chances of survival. This was evidence that later helped put focus on the importance of patient welfare.
Once the war was over, Florence Nightingale returned home but her quest did not finish there. She continued her work to improve conditions in hospitals. She became a star in her own time and with time the legend of “The Lady with Lamp” solidified in the national and international consciousness. You may have heard of there in the 1857 poem by Henry Wadsworth Longfellow called “Santa Filomena”:
Lo! in that house of misery A lady with a lamp I see Pass through the glimmering gloom, And flit from room to room
Today, Nightigale’s lamp continues bringing hope to her patients. Not just for those working and being treated in the NHS Nightingale hospitals, but also to to all of us through the metaphorical light of rational optimism. Let there be light.
Did you use R, Python (along with their packages), both, or other tools for Analytics, Data Science, Machine Learning work in 2016 and 2017?
Python did not quite “swallow” R, but the results, based on 954 voters, show that in 2017 Python ecosystem overtook R as the leading platform for Analytics, Data Science, Machine Learning.
While in 2016 Python was in 2nd place (“Mainly Python” had 34% share vs 42% for “Mainly R”), in 2017 Python had 41% vs 36% for R.
The share of KDnuggets readers who used both R and Python in significant ways also increased from 8.5% to 12% in 2017, while the share who mainly used other tools dropped from 16% to 11%.
Fig. 1: Share of Python, R, Both, or Other platforms usage for Analytics, Data Science, Machine Learning, 2016 vs 2017
Next, we examine the transitions between the different platforms.
Fig. 2: Analytics, Data Science, Machine Learning Platforms
Transitions between R, Python, Both, and Other from 2016 to 2017
This chart looks complicated, but we see two key aspects, and Python wins on both:
Loyalty: Python users are more loyal, with 91% of 2016 Python users staying with Python. Only 74% of R users stayed, and 60% of other platforms users did.
Switching: Only 5% of Python users moved to R, while twice as many – 10% of R users moved to Python. Among those who used both in 2016, only 49% kept using both, 38% moved to Python, and 11% moved to R.
Net we look at trends across multiple years.
In our 2015 Poll on R vs Python we did not offer an option for “Both Python and R”, so to compare trends across 4 years, we replace the shares of Python and R in 2016 and 2017 by
Python* = (Python share) + 50% of (Both Python and R)
R* = (R share) + 50% of (Both Python and R)
We see that share of R usage is slowly declining (from about 50% in 2015 to 36% in 2017), while Python share is steadily growing – from 23% in 2014 to 47% in 2017. The share of other platforms is also steadily declining.
Fig. 3: Python vs R vs Other platforms for Analytics, Data Science, and Machine Learning, 2014-17
Finally, we look at trends and patterns by region. The regional participation was:
US/Canada, 40%
Europe, 35%
Asia, 12.5%
Latin America, 6.2%
Africa/Middle East, 3.6%
Australia/NZ, 3.1%
To simplify the chart we split “Both” votes among R and Python, as above, and also combine 4 regions with smaller participation of Asia, AU/NZ, Latin America, and Africa/Middle East into one “Rest” region.
Fig. 4: Python* vs R* vs Rest by Region, 2016 vs 2017
We observe the same pattern across all regions:
increase in Python share, by 8-10%
decline in R share, by about 2-4%
decline in other platforms, by 5-7%
The future looks bright for Python users, but we expect that R and other platforms will retain some share in the foreseeable future because of their large embedded base.
Very often the question about what programming language is best for data science work. The answer may depend on who you ask, there are many options out there and they all have their advantages and disadvantages. Here are some thoughts from Peter Gleeson on this matter:
While there is no correct answer, there are several things to take into consideration. Your success as a data scientist will depend on many points, including:
Specificity
When it comes to advanced data science, you will only get so far reinventing the wheel each time. Learn to master the various packages and modules offered in your chosen language. The extent to which this is possible depends on what domain-specific packages are available to you in the first place!
Generality
A top data scientist will have good all-round programming skills as well as the ability to crunch numbers. Much of the day-to-day work in data science revolves around sourcing and processing raw data or ‘data cleaning’. For this, no amount of fancy machine learning packages are going to help.
Productivity
In the often fast-paced world of commercial data science, there is much to be said for getting the job done quickly. However, this is what enables technical debt to creep in — and only with sensible practices can this be minimized.
Performance
In some cases it is vital to optimize the performance of your code, especially when dealing with large volumes of mission-critical data. Compiled languages are typically much faster than interpreted ones; likewise statically typed languages are considerably more fail-proof than dynamically typed. The obvious trade-off is against productivity.
To some extent, these can be seen as a pair of axes (Generality-Specificity, Performance-Productivity). Each of the languages below fall somewhere on these spectra.
With these core principles in mind, let’s take a look at some of the more popular languages used in data science. What follows is a combination of research and personal experience of myself, friends and colleagues — but it is by no means definitive! In approximately order of popularity, here goes:
R
What you need to know
Released in 1995 as a direct descendant of the older S programming language, R has since gone from strength to strength. Written in C, Fortran and itself, the project is currently supported by the R Foundation for Statistical Computing.
License
Free!
Pros
Excellent range of high-quality, domain specific and open source packages. R has a package for almost every quantitative and statistical application imaginable. This includes neural networks, non-linear regression, phylogenetics, advanced plotting and many, many others.
The base installation comes with very comprehensive, in-built statistical functions and methods. R also handles matrix algebra particularly well.
Data visualization is a key strength with the use of libraries such as ggplot2.
Domain specificity. R is fantastic for statistics and data science purposes. But less so for general purpose programming.
Quirks. R has a few unusual features that might catch out programmers experienced with other languages. For instance: indexing from 1, using multiple assignment operators, unconventional data structures.
Verdict — “brilliant at what it’s designed for”
R is a powerful language that excels at a huge variety of statistical and data visualization applications, and being open source allows for a very active community of contributors. Its recent growth in popularity is a testament to how effective it is at what it does.
Python
What you need to know
Guido van Rossum introduced Python back in 1991. It has since become an extremely popular general purpose language, and is widely used within the data science community. The major versions are currently 3.6 and 2.7.
License
Free!
Pros
Python is a very popular, mainstream general purpose programming language. It has an extensive range of purpose-built modules and community support. Many online services provide a Python API.
Python is an easy language to learn. The low barrier to entry makes it an ideal first language for those new to programming.
Packages such as pandas, scikit-learn and Tensorflow make Python a solid option for advanced machine learning applications.
Cons
Type safety: Python is a dynamically typed language, which means you must show due care. Type errors (such as passing a String as an argument to a method which expects an Integer) are to be expected from time-to-time.
For specific statistical and data analysis purposes, R’s vast range of packages gives it a slight edge over Python. For general purpose languages, there are faster and safer alternatives to Python.
Verdict — “excellent all-rounder”
Python is a very good choice of language for data science, and not just at entry-level. Much of the data science process revolves around the ETL process (extraction-transformation-loading). This makes Python’s generality ideally suited. Libraries such as Google’s Tensorflow make Python a very exciting language to work in for machine learning.
SQL
What you need to know
SQL (‘Structured Query Language’) defines, manages and queries relational databases. The language appeared by 1974 and has since undergone many implementations, but the core principles remain the same.
License
Varies — some implementations are free, others proprietary
Pros
Very efficient at querying, updating and manipulating relational databases.
Declarative syntax makes SQL an often very readable language . There’s no ambiguity about what
SELECT name FROM users WHERE age > 18
is supposed to do!
SQL is very used across a range of applications, making it a very useful language to be familiar with. Modules such as SQLAlchemy make integrating SQL with other languages straightforward.
Cons
SQL’s analytical capabilities are rather limited — beyond aggregating and summing, counting and averaging data, your options are limited.
For programmers coming from an imperative background, SQL’s declarative syntax can present a learning curve.
There are many different implementations of SQL such as PostgreSQL, SQLite, MariaDB . They are all different enough to make inter-operability something of a headache.
Verdict — “timeless and efficient”
SQL is more useful as a data processing language than as an advanced analytical tool. Yet so much of the data science process hinges upon ETL, and SQL’s longevity and efficiency are proof that it is a very useful language for the modern data scientist to know.
Java
What you need to know
Java is an extremely popular, general purpose language which runs on the (JVM) Java Virtual Machine. It’s an abstract computing system that enables seamless portability between platforms. Currently supported by Oracle Corporation.
License
Version 8 — Free! Legacy versions, proprietary.
Pros
Ubiquity . Many modern systems and applications are built upon a Java back-end. The ability to integrate data science methods directly into the existing codebase is a powerful one to have.
Strongly typed. Java is no-nonsense when it comes to ensuring type safety. For mission-critical big data applications, this is invaluable.
Java is a high-performance, general purpose, compiled language . This makes it suitable for writing efficient ETL production code and computationally intensive machine learning algorithms.
Cons
For ad-hoc analyses and more dedicated statistical applications, Java’s verbosity makes it an unlikely first choice. Dynamically typed scripting languages such as R and Python lend themselves to much greater productivity.
Compared to domain-specific languages like R, there aren’t a great number of libraries available for advanced statistical methods in Java.
Verdict — “a serious contender for data science”
There is a lot to be said for learning Java as a first choice data science language. Many companies will appreciate the ability to seamlessly integrate data science production code directly into their existing codebase, and you will find Java’s performance and and type safety are real advantages. However, you’ll be without the range of stats-specific packages available to other languages. That said, definitely one to consider — especially if you already know one of R and/or Python.
Scala
What you need to know
Developed by Martin Odersky and released in 2004, Scala is a language which runs on the JVM. It is a multi-paradigm language, enabling both object-oriented and functional approaches. Cluster computing framework Apache Spark is written in Scala.
License
Free!
Pros
Scala + Spark = High performance cluster computing. Scala is an ideal choice of language for those working with high-volume data sets.
Multi-paradigmatic: Scala programmers can have the best of both worlds. Both object-oriented and functional programming paradigms available to them.
Scala is compiled to Java bytecode and runs on a JVM. This allows inter-operability with the Java language itself, making Scala a very powerful general purpose language, while also being well-suited for data science.
Cons
Scala is not a straightforward language to get up and running with if you’re just starting out. Your best bet is to download sbt and set up an IDE such as Eclipse or IntelliJ with a specific Scala plug-in.
The syntax and type system are often described as complex. This makes for a steep learning curve for those coming from dynamic languages such as Python.
Verdict — “perfect, for suitably big data”
When it comes to using cluster computing to work with Big Data, then Scala + Spark are fantastic solutions. If you have experience with Java and other statically typed languages, you’ll appreciate these features of Scala too. Yet if your application doesn’t deal with the volumes of data that justify the added complexity of Scala, you will likely find your productivity being much higher using other languages such as R or Python.
Julia
What you need to know
Released just over 5 years ago, Julia has made an impression in the world of numerical computing. Its profile was raised thanks to early adoption by several major organizationsincluding many in the finance industry.
License
Free!
Pros
Julia is a JIT (‘just-in-time’) compiled language, which lets it offer good performance. It also offers the simplicity, dynamic-typing and scripting capabilities of an interpreted language like Python.
Julia was purpose-designed for numerical analysis. It is capable of general purpose programming as well.
Readability. Many users of the language cite this as a key advantage
Cons
Maturity. As a new language, some Julia users have experienced instability when using packages. But the core language itself is reportedly stable enough for production use.
Limited packages are another consequence of the language’s youthfulness and small development community. Unlike long-established R and Python, Julia doesn’t have the choice of packages (yet).
Verdict — “one for the future”
The main issue with Julia is one that cannot be blamed for. As a recently developed language, it isn’t as mature or production-ready as its main alternatives Python and R. But, if you are willing to be patient, there’s every reason to pay close attention as the language evolves in the coming years.
MATLAB
What you need to know
MATLAB is an established numerical computing language used throughout academia and industry. It is developed and licensed by MathWorks, a company established in 1984 to commercialize the software.
License
Proprietary — pricing varies depending on your use case
Pros
Designed for numerical computing. MATLAB is well-suited for quantitative applications with sophisticated mathematical requirements such as signal processing, Fourier transforms, matrix algebra and image processing.
Data Visualization. MATLAB has some great inbuilt plotting capabilities.
MATLAB is often taught as part of many undergraduate courses in quantitative subjects such as Physics, Engineering and Applied Mathematics. As a consequence, it is widely used within these fields.
Cons
Proprietary licence. Depending on your use-case (academic, personal or enterprise) you may have to fork out for a pricey licence. There are free alternatives available such as Octave. This is something you should give real consideration to.
MATLAB isn’t an obvious choice for general-purpose programming.
Veredict — “best for mathematically intensive applications”
MATLAB’s widespread use in a range of quantitative and numerical fields throughout industry and academia makes it a serious option for data science. The clear use-case would be when your application or day-to-day role requires intensive, advanced mathematical functionality; indeed, MATLAB was specifically designed for this.
Other Languages
There are other mainstream languages that may or may not be of interest to data scientists. This section provides a quick overview… with plenty of room for debate of course!
C++
C++ is not a common choice for data science, although it has lightning fast performance and widespread mainstream popularity. The simple reason may be a question of productivity versus performance.
“If you’re writing code to do some ad-hoc analysis that will probably only be run one time, would you rather spend 30 minutes writing a program that will run in 10 seconds, or 10 minutes writing a program that will run in 1 minute?”
The dude’s got a point. Yet for serious production-level performance, C++ would be an excellent choice for implementing machine learning algorithms optimized at a low-level.
Verdict — “not for day-to-day work, but if performance is critical…”
What kind of probability are people talking about when they say something is “highly likely” or has “almost no chance”? The chart below, created by Reddit user zonination, visualizes the responses of 46 other Reddit users to “What probability would you assign to the phase: <phrase>” for various statements of probability. Each set of responses has been converted to a kernel destiny estimate and presented as a joyplot using R.
Somewhat surprisingly, the results from the Redditors hew quite closely to a similar study of 23 NATO intelligence officers in 2007. In that study, the officers — who were accustomed to reading intelligence reports with assertions of likelihood — were giving a similar task with the same descriptions of probability. The results, here presented as a dotplot, are quite similar.
For details on the analysis of the Redditors, including the data and R code behind the joyplot chart, check out the Github repository linked below.
R has some good tools for importing data from spreadsheets, among them the readxl package for Excel and the googlesheets package for Google Sheets. But these only work well when the data in the spreadsheet are arranged as a rectangular table, and not overly encumbered with formatting or generated with formulas. As Jenny Bryan pointed out in her recent talk at the useR!2016 conference (and embedded below, or download PDF slides here), in practice few spreadsheets have “a clean little rectangle of data in the upper-left corner”, because most people use spreadsheets not just a file format for data retrieval, but also as a reporting/visualization/analysis tool.
Nonetheless, for a practicing data scientist, there’s a lot of useful data locked up in these messy spreadsheets that needs to be imported into R before we can begin analysis. As just one example given by Jenny in her talk, this spreadsheet was included as one of 15,000 spreadsheet attachments (one with 175 tabs!) in the Enron Corpus.
To make it easier to import data into R from messy spreadsheets like this, Jenny and co-author Richard G. FitzJohn created the jailbreakr package. The package is in its early stages, but it can already import Excel (xlsx format) and Google Sheets intro R as a new “linen” objects from which small sub-tables can easily be extracted as data frames. It can also print spreadsheets in a condensed text-based format with one character per cell — useful if you’re trying to figure out why an apparently simple spreadsheet isn’t importing as you expect. (Check out the “weekend getaway winner” story near the end of Jenny’s talk for a great example.)
The jailbreakr package isn’t yet on CRAN, but if you want to try it out you can download it from the Github repository (or even contribute!) at the link below.
Yesterday I had the pleasure to give a community talk at Campus London as part of the events organised by General Assembly London. The place was fully packed and I was quite pleased to see that the audience was very engaged as they asked questions, made comments and great remarks.
As expected, the audience was quite varied from students interested to break into the field, to seasoned analysts and startup entrepreneurs. The questions were all very pertinent and I hope that the answers provided were useful to all of them.
The talk was effectively an introduction to what data science is, the tools used and opportunities and challenged in the field. You can find a handout of the slides here.
Today I had the opportunity of running a #DataScience bootcamp in London. It was an all-day affair and although the attendees were engaged, I’m sure that by the end of the 6th hour they were quite tired.
The discussions ranged from what data science is, the skills required to become a data scientist and also to manage them. Finally we implemented some data analyses based on linear regression, all using R. I was very pleased to see some of the results.
