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How to create inspiring data

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morning everyone sorry doing crane during a conference here good of yes so my name is what the just a moment for um and I thought instead of I just uh telling what what I do I thought maybe it's a nice idea if I to if I try to find out what people think that I do so I went to my Twitter account and the quite I have quite a following the by now and be quite a few people of at me to leaves and these lists on the names slaves and so I extract them some of the words people have been using to name list and then I just looked at the the frequency of those words and to I am very happy to announce that 1 3rd of the the words that have been used in in those places data visualization was actually what I do as a matter of words that are related to to what I do is data of data science sometimes designed graphics data-driven journalism but then I can hear you thinking why is this guy and states because this is a Python Conference of thoughts in 0 . 4 per cent the but also at it's your list with Python was in the name because that cell other reason to year I
think of but actually I use Python for all of my projects idea I use it to prepare my datasets before I actually create visualisations I'm actually Python use I don't sweet a lot about it so that probably explains the so let's get started so 1st of all let's just take the time and think about the
word archive and come up with some associations like emotions flow 1 will kind of feelings do you have a word archive good wants something 1 concerned old industry yeah I
think that's probably 1 of the most common things where people more people think of of your so your URI which is also kind of place where you put your e-mails where you them when you don't want to read a many more energy stored and just in case of but the other also these physical archives and I think most people think well these are kind of old dusty a location where people store groups and it's not it's maybe maybe a little bit cold because of the temperature control so not the most funny place to be problem but that's not true for every archive and I want to talk about 1 archive and dictionary which is the state archive for Dutch
architecture and urban planning I and this is quite a bit of an interesting archive because it's contains about 150 years of Dutch architecture and in this archive is maintained by a museum called the new Institute in Rotterdam and it's actually not an archive it's a collection of archived which is when an architecture or architects the Arctic organization decides well we have an archive and we want people want to have been maintained and they transfer to the to the new institutes and last year uh the Dutch national government also assigned the status of national heritage to to this archive so it really is an important and valuable archive which is is kind of a small piece of the collective memory of the Dutch arm in inside is archived there are a lot of photographs of sketches and models of group's reports all kinds of documents so it's really big archive it's the biggest architecture archive in the Netherlands and even 1 of the biggest in the world and the new institute has been quite a bit of effort in making the archive accessible online so you can go to the website and then you you will visit this page you will and end up in this page and you can enter research word a key at the top of your of your screen and then all the archives that match a keyword will push open result list and on the left you see some facets so it's a very common way of searching and if you click on 1 of the archive to get a detailed pages specifically for the archive and there are all the documents that you can while some of them you can look at because they have some scans of once you can model easy to make a reservation uh so that you can go to the archive and have a look at so and this way of searching is really common um this is a Dutch of house search website and on the right is are searching website and I'll just out of them and you basically you do the same thing you say you know what you're looking for this is that I can have I can pay this this amount of more pitch um I'm looking for a house with 3 rooms and a water results and then you can narrow down the search space in same goes for car I want diesel corots should be station from this year and you also narrowed down this search and it's very effective way of of the searching through a large collection of data but there's 1 big assumption here and that is that you know what you're looking for and that's not always the case um my wife and IRA almost in stages for looking for a new house because our has become 1 of the 2 small have but 1 of the things that I personally in is the house that the mind what are the chances that I find it in the location where I would like to should I look somewhere else or should I look for a different type of house or something like that and same goes for car if if and how unique is my search what are some alternatives that are easier to find those kind of questions and also the the more general sense of what does a dataset look like it that's really hard to do this with a search interface like this and 1 of the ways that you can gender excess data answers from data and get a better sense of the overall the contents of the data set is by using data visualization the and when I think of data visualization I think of something like this when you look at this picture you see a lot of people walking and a lot of things going on and if you would want to make sense of it it's a little bit difficult but but there's a Dutch photographer and this photographer travels the world and he goes to cities and there he takes pictures of people and he publishes them on this website in his books but he doesn't like this and I think this is really amazing because uh the groups them by the way people dress and suddenly people start to become part of a group that they were not aware of and this is exactly the same thing what data visualization design and Dutch you look at the dataset you try to come up with a designed it shows some structure and some some outliers and then that allows you to see some patterns in the data dataset by every data visualization and got can uh contains 3 components and you might you might label them with 3 questions what why and how and what is the data that you're looking at uh what is represented to why is why is the user using a data visualization what should you get out of its what our question to the short answer with a visualization and the how is the visual design and the interactions and these are also the areas where it can go wrong if you have the wrong dataset and visualizations is wrong if you the wrong questions It's not right and also if you have the right questions right data but you're not shown it correctly it's also the wrong and these are also the areas where you can improve things if you improve the quality of the data if you ask better questions or if you make a better design and visualization so this is part of every visualization so let's talk about the concept of for the architecture so it the new institute approach me and ask me why we have this big archive and we would like to know what does it look like that was the question and the it is actually a very good question distorted visualization process because the when you get when you start a date was a visualization process sometimes people think that I would want to think of a design and you build it were designed and implemented and that's but that's not the case you some I describe what I do as finding a visual representation of data set that works for particular situation you always have to discover what works and what doesn't work so this question is really good because it gives you direction but at the same time it doesn't tell me what it should look like but at the same time it was still a little too abstract for me so I came up with 2 sub-questions and 1 is what does the contents of the archive look like and more specifically ATA archived or similar if you look at the contents and the other 1 is what is the structure of the archive look like am now let's look at have look at the data of the architecture archive that if you look at the website this is the detail page of 1 of the archives then or several components that are already visible that can be used as a dataset so 1st there's a title it as an identifier title and then after the slash you see the type of archive and there's some other metadata France in the period the archive is about or the science and just for information the physical size of the archive there there are archived that our over 250 meters long so that's a really big archive and that and then this was really the part that was most interesting to me because this is a tree structure similar to the folders and files on your hard disk and also all these all these elements in industry structures have labels and that's what I could use for understanding the contents of of the that this is a Dutch sentence but so this is an example of the of a title that was that was there and what I wanted to do was extract some some kind of informative words so I did some natural language processing on this data and I want to extract the nouns and verbs and morphology so the combination of words I wanted to extract them uh get rid of punctuation get rid of numbers and things like that so here's another 1 I also wanted to uh you know if something was a person like from how does a person in the middle on such a that's Mediterranean so that's the that's a location and so these are all the kind of things that I wanted to extract so that I could get a better sense of what is this archive about of the only thing that we can have before I had to deliver the project I came across this 1 but I've been using the Python Python package school patterns which you may know for language processing but the thing is that we have many of these language processing and tools are very good at English and sometimes they also support other languages and they are reasonably support so pattern worked really OK but I had to do a lot of work in order to make it right and then I came across this and is was developed by 2 Dutch universities specifically for Dutch so this worked extremely well the only thing is that the the original language you are processing if that's not really good come grammatical touched and the result is also not very good and I was also quite often the case because those titles were just descriptions and sometimes it which is cover 1 cover to cover 3 so that doesn't really tell you something so there was still a bit of a challenge i'm so for the visual design I would like to give a live demo of the result and I cannot see screen so I have to I switch
to during what would so this is
the year of the the end result this is the 1st part is the content of the archive and what you see right here is that these are the archives and there are clustered by similarity based on the content and they also colored and positions based on the if you hover over an archive you can see the name of the archive on top and below that you see the 3 most frequent words um on the left and on the right you see to list of words and um those morphologies I mentioned the combination of words that's what I'm showing here because I was interested in uh there's for instance the word building occurred many times but there's a hotel building the Congress building there's all kinds of buildings I wanted to uh get a better sense of all those types of building so on the left you see a list of words that and parts of words where that's or the beginnings of bigger words and on the right is the forms word or the endings of word so here you see that for instance it's on his in Dutch of phrases that project so you have project documentation or project correspondence or something like that then if you click on it you can see where in which an archive these words occur the same goes
for for the ones where the words are ending on an so a part of the work and you can see where the other part of the visualisation was about a year the structure of the archives and once you click on a visualization you can see what the structure of the visualization looks like and this is 1 of the visualizations and over here you see it's actually a just a line chart where you can select different archives and it's based on the number of nodes in the network so here the bigger archives and personally I think this is very interesting because there is some kind of signature image of each archive which are prices his own unique appearance it in which the next 1 and nice as I mentioned briefly the um I
knew when I started visualization project I need to get a sense of of of the dataset so I usually try to visualize data really soon in the process and here some sketches of the visualization that I did just to get a sense of is this big data set we use a lot of variety and things like that and what were artificially because I also have go in the back of my mind the idea that I need to communicate it and I needs to look nice so it's not just about what's the what's the correct or or something like that or most effective and what what's also nice to look at so here are some different ways to look at maybe the same archive using different algorithms to to show network and it was just yeah trying out what works and what doesn't work what looks nice um so these could all be the same archive looking completely different and this 1 for example I thought I did this is not really working you see 1 archive which has a lot of nodes at 1 level deep but um there's a small exception there uh and so I thought this it was not the best solution so what you saw what the end result of but also the clustering was kind of exploratory process because there's so many parameters you can play with uh the strength of the attraction and repulsion of the nodes in the network the size of the node so here everything is shown and once you have
1 big cluster on the right here is a um Tonia a clustering the center and 1 some the on the outside are not connected to that which is also not very good uh here everything is 1 big blob in the center but also this bilingual how do you should show the different uh knows you just use transparency
and should I show the links between the nodes so that diversity connections and maybe combination and what if I show just the year uh or reduce have a fixed radius for every year every circle and also the do the natural language processing provided me with the list of verbs nouns adjectives and things like that also of persons and locations so 1 of the key ideas that I was maybe I should offer the user the option to use to dynamically cluster the network based on nouns or adjectives or something like that and this 1 France's based on persons and it it actually shows that the person is very much related to 1 archive and not to our which makes sense an archive is about 1 architect or architecture organization um so and even those connections may be coincidence because maybe 2 people of the same name so this is not even the same person so I decided not to do this in the end and this is just these are just a few examples and also the highlight color what should be white in the doesn't work I also had the idea of showing the but for the most frequent work per cluster but I decided not to because it to me it was kind of too much
of a reduction to a single word where there was really a lot of variety in the in the words that were used in the archives so I decided not to do this and also the line charges so at the bottom of the year after year the non rotating network so this is was another example when I
was looking for a visual representation of a of that's where it didn't interfere with the rotating network and didn't take up too much space because the rotating network was the main point of that view so so it's really a lot of experimenting and trying things out and and see what works and what doesn't work so what makes the data visualization interesting not there um research in social sciences that are trying to figure this out what makes something interesting and uh there are at least 2 challenges of people differ in what they may find interesting and what interesting now may not be interesting in the future but the researchers do some experiments and I would like to try this experiment with you and I've done a few times in the past and most of the time is it's successful but sometimes it's not so don't feel guilty if if the experiment so what I'm going to do is I'm gonna show you a visualization and what I want you to do is to this visualization on scale from 1 to 10 with 1 being not interesting at all and 10 extremely interesting for whatever reason and that's just so just give a number of interesting do you think
why you don't to you don't dimension it just for itself I think everybody has a number of again let me explain a little bit about this visualization this is visualization done by boris neuron is a professor of information visualization at the University of Potsdam in Germany and he created this visualization and it is part of the festival called poetry on the road um for several years he has been asked a creative visualization based on the actual poems of the festival and this visualization was used on the poster that was well they used to
announce the the festival what you see right here is that every circle every big circle is 1 problem and the bigger the so called the longer the poem um and then he came up with an idea of where you assign a number 2 letter so phase 1 we used to use these 3 etc. and for every word he some of those numbers and what each circle is actually a scale with 0 on top and then based on the sum of these numbers and the words the red dots are representations were so they're putting on a radial scale and then the size of the red circles are bigger if more words have the same number and then the the gray lines are used to connect the the following the original water order words in the original order of people so now that you know a little bit more about the how you could read this visualization who made it what it was used for and how would you rate the visualization now for who did go out quite a few through
did go down yeah also quite a few for it is they the same the it's of point I I didn't I really can't tell if there's a
majority or not the idea is that uh interestingness according to researchers has 2 main components the 1st 1 is novelty which is what we usually think of when something is interesting
because it's surprising it's new it's an unexpected but there's another component to it which is just as important was comprehensibility so you have to understand what you're looking at them the people does in some kind of diagram you can think of it like this but if something is very common and it's comprehensible then it's a live born you can think of bar charts for instance there is very effective everybody understands them their use all the time but at the same time they also orange and if something is incomprehensible but also the novel maybe visualization I just showed that showed you it can be very beautiful but you can think what am I looking at its looks nice but I don't know what it is but if it's common and if if it's incomprehensible then it's a failure and you can think of those as uh visualisations for instance a pie chart where segments don't add up to 100 % that's kind of manipulating but and what it's comprehensible and novel than it should be interesting let me tell you about another project I it's and so this is a project I did for European Space Agency then it's about this satellite and a satellite is called the pockets in a park as a satellite was in space of a few years ago but I don't know if it still is but at least the few years ago it made measurements about start and all the data collected was ended up in a in a large catalog that this both available digitally and in what in printed format where you have several volumes with lots of tables and diagrams and it was at that time the largest star catalog of the world and the and inside this catalog you can see diagrams like this where you can see locations of stars and the brightness of stars who whether start removing or not and things like for the direction of motion because they're all so and when
the European Space Agency approach me they wanted to they asked me if I could create a visualization that communicated what what is a star catalog what's going because the European bees Bayesian Caesar international government-funded organization and 1 of the uh 1 of the things that they also do is communicate to the outside world what they're doing so there for the group of people that work with education this is so stupid to explain to students what's the weather doing and uh Large Communication part organization and they also did a little bit of research what was already out there with regards
to interactive visualization of star catalogs and there wasn't very much of the time so there was 1 warning if you look for instance on google for emotion you don't find a lot of results and also on you tube you find a few animations and this is 1 of them where easy 1 constellation and it moves over 100 thousand year period and then it changes shape and that's but that's about it so I thought there was quite a bit of an opportunity there to create an interactive visualization so let me again and give you a demo yeah on so this is the starting screen of the star
catalog visualization and and what you see on-screen is some on the right you always have a short explanation of what you're actually looking at them here on top of the sea is some small suggestions on how you could interact with the visualization here in the lower left you have the bottom so the buttons to turn on star names or constellations in here at the bottom of the screen you have a few sections were were I will go through that that shows you several aspects of stock needs of those views as small uh control to interact with the visualization and this particular view is about the apparent magnitude and that's the brightness of the star you would see them from work and with this slide you can change the brightness of the star so these are all the stars that you that that the satellites measured and you can also move around in space you can zoom in and zoom out and personally I think it's quite interesting to see when you turn on the constellation of would especially when you zoom out to see that they the choice of stars of the constellation will really human choices in specific moment in time where it made sense to me based on what they saw from Earth but not not necessarily from the distance from Earth the so a lot of you is the absolute magnitude and that's the brightness of the stars as if they are from a fixed distance from her and here you can switch between them and you can see that they become most of them become bigger and brighter some of them become smaller and um but what I'm actually doing is that when you zoom out you can see you can see the results I'm actually placing all the stars in a fixed location so that's how the brightness of the star and changes work what In order to explain the concept of the absolute magnitude and there's another uh
diagram which is often used by astronomers and it's rich from Russell diagram and um on the Y axis is the absolute magnitudes and X is see temperature and you can hear switch between white and color and color based on the temperature and for astronomers this diagram is interesting because it the location of a star on this diagram tells you something about the life cycle store at the next year is about stellar
motion and what I've done here is I've created a stereographic projection so when you zoom in here you can see it especially if I turn on the constellations in previous 1 is so that the constellation were distorting inherited just become bigger and smaller so here again you can zoom in you move around but here you can simulate how stars would move over time so you can move forward and backwards in time the good example right here but the of yeah over here you see for instance this is well what's quite interesting that some plot starts moving in clusters so that's quite interesting to to know and finally there's a few where you can play with all the controls 1 so you can turn on more stars you can make them all chickens set the the maternal emotion and you can switch between 3 and the stereographic projection very nice so this is the year storm the 4 European Space
Agency had a reason why I showed you this is because some yeah I have here maybe someone is thirsty not enough but the other reason is that this is an example of storytelling with data and storytelling with data is a little bit different and literary storytelling where usually have a main character that goes through the venture and it's that's it's really a chronological sequence of events but with data that's not really the case with data you tell a story use data to support your story and a and researchers have looked at it's a lot of visualization and discovered that several models have been used over and over again and if and 3 of them are very popular and this is 1 of them and this is called a martini glass model and it's called this way because of them Marty was a narrow 1st then it widens up and that's exactly the way in the visualization structure the 1st i showed very limited views you had only 1 option and to interact with the visualization and it was only about 1 topic so it's really focused and then you move on to the next which is the same so you're in the narrow part of the martini glass and then in the final few it opens up and then you can play with all everything all at once and the reason why you wanna do that is because if if you want to explain something to you the using a visualization you and it's a little bit more complex you usually don't want to throw the visualization for him to figure it out himself you want to guide the user through the process a little bit and and explain to him what it should look at so at the end when he can play with everything at once you should know that and this is what I'm looking at and this is what was happening right now at and way 2 of to create your engaging realization is this part of the there's anyone have an idea how many trees there are in the world the total number of trees out of 5 all they 1 all the reveal the
results of its 3 . 0 4 true trees the the if you would jump
down Monterrey every 2nd it would take you
about 100 thousand years to each of them 3 so still big number maybe and live with more understandable and really big number but this was the topic of research has been done and and was published in Nature magazine and the researchers before the study the researchers only use satellite images to to make an estimate of the total number of trees in the world but for this reaches stated something different for this research they also dispatched people so and then they had those people trees so whenever they looked at a satellite image they were much more certain that this was an accurate estimate of the number of trees so and Nature magazine that was publishing this research and they wanted to an animation for this to to showcase this research and they approached me and asked me if I could do that using a visualization and let me show you the animation have some the kind of that book the at the
back and you have a lower I The on here we have the 2 we feel that this is not the goal of leading to thing the man behind on the reading and math and how you can be sure of it in the past in the it is that it is in the data in the sense of the mind and I know I I was on a lot of all holding the land in here and it kind of it I think the next and in all of our so it is and the mean that we can only ask the trained me at an air out of the the end of the and here it is the kind of hanging the way it will it will come along and not rude and all the questions so he and I met you know 1 of the good old it on the on the the the making of an on our land on the water the 2 and and and so for this project
I received the dataset and draw looking at right now it was an image of but it was a kind of special image because this was a 1 gigabyte in and the reason is that you can zoom in and zoom in and zoom in and continue theory end up with 1 pixel which is 1 square kilometer so the researchers have been able to but to to create and a map of the Earth with a resolution of 1 square kilometer and for each square kilometer they were able to estimate the density of trees which is really amazing but now when I created this realization I was looking for a kind of you like this when I think of trees I I thought maybe I want something like this so it's green also live with pumping um and that's what I had in mind when I want to create this visualization so I
started out for the all the coordinates mixed up so but then I ended up with the original idea that I had in mind which was also the area because uh well I had in mind was just to show only the trees on the globe but the transparent low and displaced adults based on the density but as you can see this image totally doesn't make sense but so yeah I quickly moved onto a solid global and and to Boston show the year the density of the trees but to get the bumpy appearance right was quite tricky to do as you can see in this image and in the next 1 the did the green is really just 1 big area of agreement and it doesn't look like the bumpy appear as I was looking at and so I've tried all kinds of different things different colors different types of representing the bars but all didn't produce right result you still see the 1 big area of green and of the and finally I figured out what did work so in the end of every pore is has a gradient it starts out with the color of the globe so kind of bluish then ends up with green color and I assigned to to 2 the bar In this way they have different heights and then you have different colors on different heights because of the gradient and this way you do see the the bumping payments and it works really well and so it also ended up on the cover of the magazine which is really nice course but reason why I'm showing this project is because of what I've done here is been using metaphors and in this case the project was about trees so I thought of what's what are some characteristics of trees that I could use 1 of obviously is green the bumpiness and there's something in general which you could think of when you create a visualization think of the the actual characteristics of the data that you're visualizing what what's the meaning can I use some of those properties in my design and I've some examples of other people's work where this is done as well this for instance is a visualization done by the South China Morning Post and it's about the deaths indirect and basically just a bar chart but since they flipped and the y axis has made the bar shredded it clearly has the appearance of lot
so they this is a good example of using a metaphor and and thinking about what this data represents what is me there's another 1 that is a win visualization it's an animation if you go online
and by Martin Wattenberg and from other figures and while the common weather maps usually have arrows them but this 1 is actually especially if you go to the online version you see flows of of Lines going and they become narrower or wider and and that's much more in the way we experience Winston an error so the final product I would like to show you is this um for visualization I strongly believe I think just as with the writing code in general you only get very good at it if you practice and you can read all about it if you don't do the actual coding for creating visualisations in this case you don't it's hard to become really good at so I I really like doing personal projects as well and this is 1 of those projects and this is about the Dutch national elections of 2012 and um when the elections were over people had a few questions which point is 1 which point you've lost or possible coalitions which are which is all always the case in the Netherlands how is my C voted and these are all very good questions of course because that's what the elections are about but you can also as different kinds of questions so this is the question I personally had was which city vote in a similar way as my and maybe artisan some patterns in the data while the concept is more or less something like this so if you take these voting results and you just overlay them and then you look at the differences in the sum them up and 1 can imagine if it's 0 then they're exactly the same if it's not a day of some difference morals like this but this is the visualization that I created you see a map of the Netherlands I click and draw
map of because the Netherlands really densely populated so you can see the shape of the Netherlands just with the cities and you can offer over your you can click on cities and when you click on it the become bigger and orange and other cities also become bigger and more orange if they are more similar based on the voting results but on the top left corner you see the bar charts with the voting results of the city that you selected and this is another layout where you a radially layout a selected city is in the center of the screen and the more similar the foreign results hardly be closer to the center of the other cities but I also included another dataset about population size because I thought maybe there is a correlation between bigger cities in smaller cities and so that's what I also included and it turned out there were actually some interesting findings but the 1st 1 is this something like this which you can find it in several places of this map and it's these are regional cluster so this is around the city of paint over which is relatively large city and funnily enough I ain't
over itself is not in this cluster so it's really the cities around trying to over that followed in a similar way but also a little bit different than the rest of the Netherlands but I think this is quite interesting and and also if you would extend this project with these kind of clusters remain the same over time will require quite interesting to research another thing is in analogy with the bible belt so if I select 1 city of the bible belt you clearly see that they all followed a similar kind of way um SureSelect it's also i which is a city associated with richer people and then the other ones are also cities of where people think rich people live so that's also something that you can see in the data but it is you I've selected Amsterdam and what is the relationship is not that strong you do see that the biggest cities are more on the inside and on the outside so bigger cities also fall within the severely and as with many datasets there's also there also outlines is Burke and in there there's no city that devotes was like the fact that how so now that I've I've received e-mails that people were using this on for half an hour during work time just playing with this visualization and i've so why were they doing this because what and I I recently discovered is broken it's called the hook model of how to build habit-forming products and its the basically about so what you do when you want to create something like Facebook so that people keep using it an insider within his model and I think as model also applies to the visualization because 1st you need to have a trigger and if you move over with your mouse over the visualization you see that it turns into hand so you are trigger that you can click on it and then clicking on it is the actual action but what's key here I think is the variable reward so sometimes you see something sometimes you don't and you also have to be some effort for so I think think that's 1 of the main reasons why people were really it playing with a visualization for quite some time because yes sometimes you can really find something and sometimes you don't and also uh something like the bible belt that I discovered the visualization itself doesn't tell you that there is a Bible belt in the visualization I just follow the full of it myself and then I confronted with the visualization but you can do that to yourself as well so you can think what 1 maybe I know where farmers live or something like that and see if there's a pattern there so you can find things yourself and I think that's something that you if you can apply that to a visualization that's something that's really useful thank you very much a few
thank you very much for this inspiring talk
and think the major of thousands of people like planting trees along the word state when we're ahead for a while and also amazing how you can get that 1 white and that a picture in the representation which contains much were eating so we have many time for just 1 quick question yet here so I had to use all the self tools and libraries for the real stuff yourself to do
these kinds of visualizations it's all of custom visualization is the only
thing that I do users some frameworks so move 1 of the most popular ones so this is the 3 and the but 40 years uh the European Space Agency as I use 3 so I use libraries like that but other than that it's it's all custom OK 1 many 1 other quick question dates nanny here just because they might have and so low similar question but if you deploy these projects what's your typical technology stack that's the difficult and technology didn't get uh I would say it's today most of my realizations are web-based and and for the data preparation and wrangling and everything on that I use Python those are basically the the tool would ordered the programming languages that I use I sometimes used tableau which is a business intelligence tool but um is a allows to quickly get a sense of data because it can just opened in Excel flowers she's the found and drag and drop and you already Chris I went to some of
the most of today I'd most times Python so the but mostly Python
and web-based frameworks whatever
OK so thank you very much now there is they're called
the break that I think in again
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Framework <Informatik>

Metadaten

Formale Metadaten

Titel How to create inspiring data
Serientitel EuroPython 2017
Autor Tulp, Jan Willem
Lizenz CC-Namensnennung - keine kommerzielle Nutzung - Weitergabe unter gleichen Bedingungen 3.0 Unported:
Sie dürfen das Werk bzw. den Inhalt zu jedem legalen und nicht-kommerziellen Zweck nutzen, verändern und in unveränderter oder veränderter Form vervielfältigen, verbreiten und öffentlich zugänglich machen, sofern Sie den Namen des Autors/Rechteinhabers in der von ihm festgelegten Weise nennen und das Werk bzw. diesen Inhalt auch in veränderter Form nur unter den Bedingungen dieser Lizenz weitergeben
DOI 10.5446/33725
Herausgeber EuroPython
Erscheinungsjahr 2017
Sprache Englisch

Inhaltliche Metadaten

Fachgebiet Informatik
Abstract How to create inspiring data [EuroPython 2017 - Keynote - 2017-07-11 - Anfiteatro 2] [Rimini, Italy] Many times data visualizations need to communicate insights clearly and effectively. But sometimes the goals of a visualization go beyond that: they need to inspire and engage people. But how do you draw them in? What is the process behind creating a creative data visualization? During this talk, I will show some of my projects, and explain a little about the process behind it. Peter Hoffmann - Infrastructure as Python Code: Run your Services on Microsoft Azure "Infrastructure as Python Code: Run your Services on Microsoft Azure [EuroPython 2017 - Talk - 2017-07-11 - Anfiteatro 1] [Rimini, Italy] Using Infrastructure-as-Code principles with configuration through machine processable definition files in combination with the adoption of cloud computing provides faster feedback cycles in development/testing and less risk in deployment to production. The Microsoft Azure Cloud (https://azure.microsoft.com/) allows different ways to provision, deploy and run your python service: The Azure Resource Manger Templates (https://azure.microsoft.com/en-us/resources/templates/) allows you to provision your application using a declarative template. With parameters, variables and Azure template functions, the same template can be used to deploy your application in different stages (dev, test, production) and environments for different customers. We open sourced the tropo library (https://pypi.python.org/pypi/tropo/) to create Azure Resource Templates from python. Azure SDK for Python (http://azure-sdk-for-python.readthedocs.io) for a low level access to manage resources in the Azure Cloud. An Azure Ansible Module (https://docs.ansible.com/ansible/guide azure.html) based on the Azure SDK to automate software provisioning, configuration management, and application deployment in a single environment. Each of the alternatives has different strengths and drawbacks. Presenting our learnings from migrating our infrastructure into the Azrue Cloud will help to avoid common pitfalls and show deployment patterns that will ease the live of devops

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