How can you use Open Source materials to learn Python & data science?
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| License | CC Attribution - NonCommercial - ShareAlike 3.0 Unported: You are free to use, adapt and copy, distribute and transmit the work or content in adapted or unchanged form for any legal and non-commercial purpose as long as the work is attributed to the author in the manner specified by the author or licensor and the work or content is shared also in adapted form only under the conditions of this | |
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EuroPython 201871 / 132
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00:00
Open sourceSoftwareOpen setExpert systemAlgorithmCodeCoding theoryOpen sourceGoodness of fitPerfect groupTerm (mathematics)Group action2 (number)AlgorithmPresentation of a groupComputer programmingProgrammer (hardware)CodeLink (knot theory)Open setAuthorizationComputer animation
03:47
Open sourceMaterialization (paranormal)Projective planeOpen sourcePresentation of a groupComputer animation
04:29
Content (media)VideoconferencingCodeOpen sourceFreewareTerm (mathematics)Link (knot theory)CASE <Informatik>Shared memoryGoodness of fitPresentation of a groupMaterialization (paranormal)CodeBounded variationWebsiteSelectivity (electronic)VideoconferencingOpen setMathematicsComputer animation
07:33
BuildingCodeElectronic program guidePauli exclusion principleComputer programmingOpen sourceDirected graphNumberProjective planeProgrammer (hardware)Rule of inferenceTerm (mathematics)BitMultiplication signState of matterCodeVirtual machineDifferent (Kate Ryan album)MultiplicationGoodness of fitOnline helpRootVideoconferencingPresentation of a groupLevel (video gaming)ChatterbotSoftware development kitVector potentialComputer animation
11:59
Software developerArchaeological field surveyData analysisVirtual machineAdditionFlash memoryoutputFunction (mathematics)Performance appraisalPoint (geometry)InternetworkingAlgorithmCodeArchitectureData modelComputer networkCoroutineProcess (computing)Open sourceChemical equationTransformation (genetics)Right angleLaptopSoftware developerTransformation (genetics)Open sourceTerm (mathematics)Computer configurationArchaeological field surveyMathematicianMultiplication signAlgorithmRevision controlWebsiteFormal languageError messageSlide ruleLevel (video gaming)MereologyLibrary (computing)outputFunction (mathematics)PhysicalismMathematicsVideoconferencingHacker (term)Suite (music)AbstractionWeb crawlerGoodness of fitPresentation of a groupReal-time operating systemComputer programmingProgramming languageArmWave packetReal numberIntegrated development environmentInternetworkingVideo gameBlogWordCodePerfect groupComputer animation
20:32
Open setProjective planeMultiplication signTerm (mathematics)Lattice (order)Level (video gaming)Local GroupProcess (computing)Limit (category theory)Software bugElectronic mailing listShared memoryPiEmailSlide ruleGroup actionDisk read-and-write headSoftware developerSimilarity (geometry)Self-organizationOpen sourceInternetworkingInformationVideo gameLocal ringMereologyWebsiteProgramming languageHookingProgrammer (hardware)Electronic program guideSpeech synthesisBacktrackingComputer programmingCodeComputer animation
28:20
Open sourceModul <Datentyp>SoftwareOpen setLevel (video gaming)FeedbackPerspective (visual)Projective planeExpert systemMultiplication signSlide ruleComputer animation
29:34
DampingShared memoryGauge theoryProgrammer (hardware)Computer programmingText editorComputer animation
31:31
Trigonometric functionsMountain passTransformation (genetics)Open sourceOpen setGroup actionMaterialization (paranormal)BitSoftwareTerm (mathematics)Slide ruleInformationOpen sourceEvent horizonTriangleWave packetComputer animation
34:02
Pauli exclusion principleBit rateVideoconferencingACIDContent (media)Axiom of choiceInternetworkingMusical ensembleTouchscreenFormal languageOptical disc driveLaptopRevision controlResultantInformationComputer fileVideoconferencingWeb browserCursor (computers)Goodness of fitCellular automatonKernel (computing)Library (computing)Term (mathematics)Type theoryIntegrated development environmentDirection (geometry)Computer animation
37:59
Multiplication signInformationComputer animation
38:37
Projective planeHacker (term)Self-organizationMultiplication signElectronic mailing listBitGoodness of fitProcess (computing)Computer programmingTable (information)Materialization (paranormal)Group actionExpert systemInformationTransformation (genetics)Computer animation
Transcript: English(auto-generated)
00:04
Hi, can you hear me well? Okay, perfect. Today, I would like to speak or perhaps even talk with you about Python, obviously. We are in a good place for that, about data science,
00:20
and about open source, and the education, so many, many topics, actually. Just to make this very clear and very beginning, I'm not a programmer, so it's like my background is in diversity, my background is in sociology, I also work with companies like B2B, but for
00:43
many years now, about seven or eight, I'm working about diversity and I'm working about educational programme in programming and in data science. So, yeah, just please don't ask me the question like, okay, so how I can become a data scientist in three
01:05
weeks? Okay, so, yes, first of all, I have no clue, and second of all, it's, you know, it doesn't work like that, and the second question which I would not answer or just
01:21
staying here and smiling very broadly to you, it will be, okay, so which algorithm is better? Is that algorithm A or algorithm B? Okay, fantastic, yes. So we are very lucky to have many people in here who will be very happy to answer this question.
01:41
That's not me. And also, in terms of this question, it's like perhaps if you will ask that kind of question, you will get an answer, okay, could you be more specific, could you tell me something more about this issue? So, yes, just so you know.
02:01
And, in terms of, I would like to learn something about you. So, how many of you are writing Python code less than six months? Okay. Perfect. How many of you is writing in Python more than two years? Okay. And how many of you have run more than 100 data
02:30
science experiments? Okay. You will not learn so much. Maybe something about open source, I hope. Okay. And how many of you are very, very fresh, consider
02:44
themselves as very, very fresh in data science? If you are in the right place. Okay. So, this presentation, I hope that will be helpful for you in a couple ways. And the very first way, those are resources. So, I prepared more than 20 links for you
03:05
about open source, about data science, about Python, about different groups all over the world. And you can find all these resources as well as the whole presentation on my GitHub account. So, yeah, please just use that. And also, it's CC by. So, you can
03:26
use that freely. It's just a matter of if you will say that, okay, the author is Kamilastapniewska and this kind of thing, you will be okay. And so, some basics about
03:42
open source, some basics about data science workflow. And that's and after that, something which I hope it's very helpful and not only for beginners, it's the way of thinking that if you are learning something, it's good to have three things in your learning experience.
04:04
And one is working on projects. The second one is cooperation. So, cooperate with other people. And this third one is contribution. So, it's not only about learning from others, it's also giving to others. For example, giving a talk, giving
04:21
lightning talk, open source materials. So, I hope that's something which you will take from this presentation. Okay. So, shall we? And open source. So, basically, I believe that many of you are familiar with open source. We are in really good place for
04:46
that. But just to make some kind of reminder. And it's about free use, free modification, free sharing. And as a user, you might consider two cases. Like one case is like if you are
05:06
using some materials which are text, which are pictures, which are videos, and that kind of materials perhaps will be on if those are open materials that will be on Creative Commons. And if you are writing a code, then perhaps this sorry, if you are using a
05:26
code which is open source, then perhaps that will be on one of very popular licenses. And that might be MIT, that might be GNU, that might be actually many, many others.
05:41
Apache is also very popular. And one thing which is really cool, for example, for GitHub, it's like when you are doing requests, you can just choose the full license from in a GitHub. You don't need to necessarily copy and paste that. So just as a user, if you are using some materials which are open source, just please
06:07
keep in mind if those are Creative Commons, then you have couple of variations. And the most important is just to remember that the basic one is CC by. And CC by allows you to
06:21
use material freely, to share material, to modify, to use that for commercial and non-commercial purposes as well. And then you have a couple of variation which are basically are different variation of if you can, if you need to use the same license or not,
06:41
if you need to, if you can change the source or not, and if you can use that for commercial purposes or not. So those are basics. Very useful basics, I hope. And if you are a creator, if you are building your own code, or if you are building your own
07:05
text, video, or other kind of material, those are links which hopefully will be very helpful. So basically in terms of general use and general selection, Charles license is a very good website
07:21
for text, basically Creative Commons, and for code, opensource.org. So please remember that will be on the presentation which is available for you. And let's go to Python. So asking a question why Python in this place is a little bit tricky. So it's like,
07:46
you know, so maybe actually someone from you can answer me, why Python? Why do you use Python? TensorFlow. Oh, great. So data science basically, yes. Sorry?
08:04
They rub the word. Okay. Yeah, in a way, definitely. And one thing, why they or we rub the word, it's like, because it's a community. So it's very easy to just create
08:21
things and very easy to share these things and very easy to contribute, actually. Nice. So, yeah, welcoming, supportive, very good for very beginners. So in terms of general learning experience, I hope that like building projects, finding a project
08:42
which will be interesting for you, finding a project which you are very, you are very dedicated to. Finding right people and finding the way how you can contribute. Those I hope that that's helpful. Yeah, in terms of being a beginner in Python, it's good to know that number eight
09:09
is just will help you a lot in terms of how to use Python properly, how to make a good practice, how to have a good style, and it's something which you might not think about
09:24
on the very beginning, because you just want to write a code and you want it to work, but it's something which will help you in advance. Oh, Zen of Python. So you might have
09:41
been on a lightning talk yesterday, and it was a little bit of trolling about Zen of Python, so why is it not helpful? I would say it was a trolling, and a really good presentation, by the way, but the spirit of like using Zen of Python is more of a very, very high level,
10:05
so it's like please don't take that very serious, but in a very, very high level, that might be helpful. Yeah, if you are a very beginner in Python, actually Python Software
10:20
Foundation is the best place to go, so Python Software Foundation has really great resources, and I really like that they are giving resources for programmers and non-programmers, because it's very helpful. It's like a different state of mind, and actually in these resources,
10:42
you can find books, you can find videos, you can find tutorials, you can find many other kinds of resources, and it's updated, so it's alive. And you might know Lynn Root. She will be also a keynote speaker today, and her talk, which was
11:06
I believe the very first time she done this talk on EuroPython in Florence, I believe, and Sink or Swim, you have not only these rules how to learn, how to code,
11:25
for beginners, but you also have some projects which you basically can run yourself, so there is multiple projects on API, for example, on some chatbots, and some other projects which you basically can use for your education.
11:43
And, okay, so data science. You know, machine learning, it is how it is, like we are searching until we will find the right answer, the right answer, that's the most important. But besides that, why data science and Python goes together very well?
12:08
I'm not sure if you are familiar with this, this results, it was done also, like PyCharm in a way had some finger there as well, but it's a very good survey which was taken
12:28
on more than 9,000 developers from almost 150 countries, and it gives really good knowledge about how Python is used by developers nowadays, and data science actually are very, very strong
12:48
in this survey. And what kind of technologies in data science? So you can see this
13:00
is very popular now, which is pandas, which is stick learn, you also have like many, many more. That's a good slide for those of you who are new into data science just to check what's there. And a couple more which might be helpful, so PyCharm and
13:28
Spyder, those are IDE, so those are the environments which you will be using. Spyder is basically for Python use, PyCharm is for all languages purpose,
13:48
and something which actually I was using very often, it's Jupyter notebook, it's something which is very helpful in terms of trainings especially, because if you are building a training
14:02
and you would like your participants to basically work with code like in the real time, that's something which can help you a lot, and it's very easy to prepare, and it's Jupyter notebooks, it's something which is very helpful in education of programming.
14:29
So, in general, Python is Python in data science, it's something which you want to consider to use as a tool to build your tools, so it's not a purpose
14:44
as a purpose to just use Python, it's something which is a programming language which can help you to just build what you definitely want to build in data science. It's just a tool in this way. And, like, some words about how data scientists' everyday life and everyday work looks like.
15:09
So, okay, like, a bunch of the time, it's just preparing data, so it's see what you actually have in data set, see what you are missing, see what kind of errors
15:26
do you have, so like cleaning, clearing data, and then just praying to have enough of them to run your experiments. But then there is a fun part. The fun part,
15:46
that's just an example how you can think about that, but the most important things from the slide is, like, the understanding of your problem and understanding of your issue which you are focusing at. It's crucial to just understand what kind of data do you have,
16:08
what is the input, what you would like to be an output. It's very crucial, and it's something which, at the very beginning, you might not think so seriously about. And then you have this
16:21
really fun part between, like, search and experiment. Actually, it's like going in between searching, experimenting, searching, experimenting, searching, experimenting, so those are many steps to take, but it might be a fun part. That's a question, when I was preparing myself for this presentation, I asked
16:44
a couple of data scientists, my friends, how do they actually find the right algorithm? How do they find the right sources? And the very first version of a question which I was asking, it was where do you find the right resources? So I got a very simple answer
17:07
all over in the internet. Okay, yes, great. That's good to know. But what are the criteria? So how you can think that, how you can just decide that one algorithm is good for something,
17:26
and for your purposes, or it's not? So you will have your own judgement, you will use your own judgement here, but some good practices, if you are more experienced in Python,
17:42
you can just see the code and see what's there, try to modify that, so that's one of the very good options. And basically, as always, as in science in general, so see the resource, see if those people, what is the credit of these people? What is the credit of the source? So
18:05
nothing new, it's just you need to try. Okay. Some hacks. So that's for more advanced people, actually. It's a tool which, if you are,
18:22
if you already run more than 100, 1,000, couple thousand of experiments, and actually you have these issues which you will have at this stage, so if you need to be really, you need to know
18:40
what's going on in experiments, and what does it mean, and so Steppe, it's a library which might help you. Yeah, the very two basic abstractions there, so steps and transformer.
19:00
So that's something which you just take a look. And a good thing about data science, and some resources for data science, it's at this website, at data science masters, okay, it's all purpose. Even if you are advanced data scientist, that might be a website which
19:24
you actually would like to consider to just take a look at there. It's something which might be very helpful. You have a bunch of resources, and you have there, like, both videos, also,
19:41
you have, like, regular tutorials, you have text, you have a bunch of things there. And do we have any mathematicians or let's say mathematicians in the room? Okay, there is one. Okay, perfect. And there is a second one. Okay, but you are advanced, so it's not for you.
20:02
But if you will be a mathematician or a physics, and you would like to start with data science, there is a blog post by Peter McDow, which might be very useful, and it's like he made himself the switch from science, basically from mathematics and physics, into data science,
20:22
so it's, like, very personal blog, but very useful things, like useful hacks, which you can find there. Okay, how much time do I have? Okay, perfect. So, how you can learn?
20:47
Basically, projects, cooperation, and contribution. And in terms of projects, it's something which is very good for very beginners, and if you are the beginner in a particular programming language,
21:02
if you are beginner in data science, if you are beginner in anything connected, in my opinion, anything connected with coding, it's good to find your project, it's good to just find what you actually would like to build, why you want to build that, and then check if it's
21:22
possible. So, I would say, like, start with the sky is the limit, and then see what's actually possible. The other way, it's like, let's see what's there, so let's see some projects which are in data science, and see if there is something which is interesting for you.
21:44
So, in here, on the slide, you have three resources where you can find data science projects, and you can basically just see if there is something which you feel connected in terms of, okay, yes, I definitely would like to build something similar, or that's the topic which I
22:04
would like to just dive deep. So, yes, that might be a good thing to do. And cooperation. I'm very happy to see a gentleman with this T-shirt. Hi. And in terms of cooperation, in
22:24
terms of, like, what, how you can learn from community, how you can be a part of community, how you can just build community, which will be very helpful for your learning experience, but it will be also, you know, it's just nice. It's just nice to be a part of a community
22:43
as well. It's safer, and it's just nicer, I would say. But in terms of offline things, ladies are very lucky, in a way, like, there is PyLadies, there is sorry, Giggers Karas, it was something which I was co-created, but there is also
23:04
GirlGeek, which are very nice. They also have their appearance here in Edinburgh. And there is a bunch of local groups which have usually this monthly meetings, and
23:20
usually they also build or just conduct some workshops or some conferences, and it's, you can find a bunch of information about these local groups in the internet. And it's, yeah, you can think about that as also one life hack which I have. If I'm in a new city,
23:46
I'm travelling a lot for my work, and just because I like as well, and if I'm in the new city, I'm checking if there is, for example, PyID meeting, or if there is a Women Who Code meeting. For all of us, not only women, there are definitely PyData, which are also global,
24:08
and that's a community which also have really great meetings, so that's something to check. And basically, for online appearance, PySlack, definitely, this Python mailing list, this
24:26
tutor, Python mailing list, it's something very helpful if you have particular questions. You also can see, because this mailing list is really old, so many questions were already
24:40
answered, so you can just go to archives, and you can see if you will find the answer for your questions, and that might be a good idea. And there is a group of Facebook Python programmers that might be also helpful. And contribution, the best part.
25:04
Backtrackers. It's very easy. It's like if you go for this website, for bugs-python.org, then you will see a bunch of requests for basically backtracking, and you can see if the
25:23
bug is taken or not, if someone is working on that or not, if you can contribute there very basically, very basically by just taking some back and try to fix that, and bugs are on very different levels of ... some bugs are very simple, some bugs are very advanced,
25:47
so it's something which might be good on many stages for your journey with Python. Also, bugs are fixed on a sprint, so sprints will be Saturday and Sunday,
26:02
on EuroPython and PyCon, there are sprints as well, so that's also a very nice occasion to just meet core developers and to be more involved. And in general, not only in Python, but contributing to open source projects,
26:22
there is this open source guide which can answer many questions. Basically, PySlack which I mentioned before, and PyData which I also mentioned before, but you can think about the PyData as an attendee, but you can think about PyData also as a
26:45
speaker, so it's always good to just, if you have some topic which you would like to share with the community, or even if you want to challenge yourself, and you do not have a topic, but you would like to just find some and share with the community, prepare this talk,
27:04
make some effort there and share that with the community, then PyData is a good place to to just contact organizers, local organizers, and say that, hey, I have this talk, or I would like
27:23
to be a speaker. If you have some previous speech, then it's always good to share that as well. If not, it's also just good to reach out. And in terms of like workshops, this
27:41
Django Girls workshop, they made a really, really great job, because Django Girls, they prepare not only the tutorial for workshops from Django for beginners, but they also prepare them all like setup, how to prepare this workshop, what you need to focus on, how to speak with a venue,
28:04
so very, very detail-oriented resource which you can use to prepare Django Girls workshops, so that's something which, yeah, they made a really great job. And
28:24
something which I was not able to just fit into any of other slides, that's an idea of open education in general, open education on an academia level. The idea basically is like, if you are writing your paper, that might be from STEAM,
28:47
but that might be also from sociology, that might be from different perspectives as well, and you would like to share that openly, you would like to make that open source, but in the same time you want to make that properly, you want to make sure that the paper will be reviewed,
29:08
that other experts will just take care of this paper, will just see if is it something which is viable, and you will get some feedback as well. So that's rather
29:25
a new project, but I keep my fingers crossed very strongly for this one. And basically, yeah, now I would like to start a discussion, and actually I'm very curious about your stories.
29:47
Do we have a mic? Oh, mm-hmm. So basically, if you don't mind, it would be great if you will
30:10
tell, share with us some things about, like, how did you start to learn Python, how did you start to learn data science? And actually, yeah, I'm very happy to just answer some questions if
30:25
there are some. Yeah, like you mentioned the Jungle Girls tutorial, which I think is really great, because as you say, it's really starting from scratch also for people. If you don't know about that, it's like explaining what's a text editor, and how to use Git, and all these things that
30:45
most people consider is like a prerequisite to become a programmer, so it's really good for non-technical people to start Jungle and Python, but like the problem for me, it's not very a problem, but it's focused on Jungle, and I've got a lot of people asking me, okay, I want to
31:04
do data science, and what resources can you advise to start in data science from scratch? And the question is, are you aware of any effort to, like, duplicate this kind of Jungle Girls tutorials towards more, like, data science things? Do you know if
31:25
any things like that exist? Is there any? That's a really good question. So I haven't found anything like that, so, basically, I just would like to be on this, no.
31:46
Yeah, so, unfortunately, I haven't found anything like that for data science. I know that the group in Krakow, actually in Poland, a group of gigahertz, they are working on some data
32:01
science tutorials for very, very beginners. That's a different group than the Jungle Girls, but, yeah, I know that they are working on that. That's not released yet, but in terms of Jungle Girls, that might be actually really good to just speak with them and see if there is
32:21
some way how you can combine this all knowledge which they have in terms of organizing workshop, in terms of work with very beginners, and I'm sure that they are very open for that because it's open source, so, basically, that should be possible. And then, for example,
32:43
get this information from this data science masters and just combine something together. So, unfortunately, I haven't found that kind of workshop yet, but I'm sure that will happen
33:00
if you would like to speak about that after, then I will be more than happy. So I actually have an answer for you. You should look at Software Carpentry. It's a bit hard to actually organize an official event for Software Carpentry. You have to go through the training or so, but all their materials are open, so you can just use them
33:23
or adapt them. Yeah, thank you, that's helpful. Another question from the audience. Anyone who would like to share the story, like how did you start with data science? I'm
33:45
really curious. I'm serious. So I have a question for you. One of your slides was all the Creative Commons license, and there is big discussions about some of the restrictions that
34:02
people decide to apply to that content, especially the non-commercial one. What's your opinion about that? In general, I want people to have a choice. In general, I want people to have choice. So if they want to choose that their work might be used
34:26
only for non-commercial purposes, so no one can use this work to just earn some money in direct or not direct way on that, then I'm fine with that. Like things which I'm creating
34:46
usually, you can use that for commercial use. That's my choice, but I really believe that other people should have this choice to make of themselves. I'm not sure if that's answering
35:03
your question. Yeah, that's answered. Looks like you're going to say something. No. I have another question for you. You mentioned Jupyter as a suggestion for people to use.
35:21
Did you ever try JupyterLab, the same thing that Jupyter notebooks are building as the next IDE? Did you have any experience with JupyterLab? Not yet, but I would like to learn something more, so if you can actually tell something more about that, that would be great.
35:40
So, for those who don't know, the same thing that was building Jupyter notebooks, they decided to build the next IDE on top of all the technology that they were using for Jupyter notebooks. So, in terms of technology, you have a kernel, and you use MCQ, if I'm not wrong,
36:01
to connect the web browser with the kernel. That can be a Python kernel, it can be an R kernel, another language that's going to process the cells and send back the results. So they decided to use that technology to build a full IDE, and it looks very promising as far as
36:22
features in ITAR. They only reached version 1.0 a few months ago, but it looks very interesting, and they still support the Jupyter notebooks. They make it easy for you to access all the local files and have all the information in one screen. I will definitely take a look at that.
36:46
JupyterLab. Sorry? I don't think that anyone kind of wrote any tutorial yet. Like, early days, you can look, find videos online of people demonstrating, but it's easy to install
37:06
as any other Python library now. You just need to install JupyterLab. JupyterLab, but they never use it, so I wonder, is there documentation for that?
37:24
There was some documentation on the internet, so you can google. Any other questions? What about the MOOCs, Coursera, and EDX and things like that? Oh, that's a good question. So it's like, if you all think about a way as you are learning,
37:48
as a learning experience, so if you will think about it not only about some courses which you have in the internet, which sometimes, like Coursera, or like a bunch of other resources,
38:07
like, for example, by, okay, I think we don't need that for now. So a bunch of other resources, like, for example, created by General Assembly or some other online schools, those are helpful, but it's very, like, you will find a tutorial there, so you will find,
38:29
okay, very, most of the time, very academic way of thinking about some issues in data science, which not very often, which very often they are lacking this information how to, how and why
38:44
actually you will need to use that. So what I really prefer in a learning experience in general is, like, you have this project which you can work on, then, yes, for this purpose, you can use, for example, Coursera, and find some courses on very typical,
39:03
on very particular projects, problem, sorry, so you can find that, but it will be not, like, doing only this kind of courses, it's not enough. Actually, I was very happy and lucky, actually, to work with many women in Europe and in the US, like, mostly women,
39:28
not only but mostly, work, like, to help them in this transformation from one of the, like, for example, for working in finance into data science or into programming in general,
39:44
and what was working very well for an adult who already has some kind of job, it was like if they had a project, if they had people around who support that, and then resources, it's something which is necessary, but it's not the most important thing.
40:03
Yeah, I just take this a little bit around, but if you would like to, I have also the list of commercial educational materials, so if you are interested in that, I will be happy to share.
40:20
We still have time for one question, if anyone from the audience wants to ask. Okay, I'll not tell my story, because it's a bit long, but one takeaway from my story is that the workshops you mentioned, like Django Girls and stuff like that, are really great if
40:40
you can find one. It's also fun to organize them, but it can be a bit daunting at first, so if you want to, so it takes three kinds of people for these workshops. You have somebody, some teacher, some mentor, people who learn, and the organizer, and the organizer is kind of
41:05
a bit overlooked, but if you want to learn something, it's usually not that hard to find the mentor, if you know where to look. If you go, for example, to a Python meetup, you will find experts who will be very happy to actually teach someone,
41:23
so if you want to learn something, I would recommend going to a Python meetup, fishing around for someone who wants to teach, and then organizing a workshop. It doesn't have to be big, you can have three people, one mentor, and if that goes well, you can then scale up, go
41:42
to the full Django Girls or some big workshop. That's really good tip, and actually, on the very beginning, you can think about that as like a hackathon, or just Hack Night. Hack Night, that's a good name, so it's like to find this mentor and to just organize like three, four people, and just set up the place, so, for example, some coffee shop which
42:06
will be nearby, you know, in that place, you will have a table which will fit four people, and that's it. Just announce when that will be, and like in that kind of group, just emails or a messenger or anything like that, and that should work. Yeah, thank you for this comment.
42:24
Thank you for all the questions from the audience, and I want to thank Camilla again, so Rona, I suppose, for her. Thank you.