Towards a MOOC “Creating mobile apps” for entrepreneurs

In my school and in Lyon, the city where I live, everyone is helped in starting their entrepreneurial projects. Startup weekends, startup challengesboosters, incubatorsstartup creation projectsI visit and sometimes participate in these events. Listening to the pitches of the teams, I noticed something. 90% of them start the same way:

“So, our idea is to create an app, what it does is…”

But nobody in the team knows how to code an app. Nobody in the room, really, knows how to create an app. There is this hope that maybe, a geek friend from an engineering school will give a hand to code the app… or there is also the hope to convince a banker or a business angel to finance the prototype of an app… In practice, this almost never happens. Tons of excellent projects are abandoned or drag on forever because apps never materialize.

But what if apps could be created by everyone? Let’s dream a bit

iOS_Android_Windows_Phone_Wide1) everyone invited: not just those who already know how to code. Like, supra easy to create.

2) the app should be fantastic: not a web page packaged as a fake app, but a real app with access to all functionalities of the phone that it needs (camera, GPS, contacts…)

3) the app should be available on the Playstore (for Android), the App Store (for Apple), and Windows Phone’s Store.

4) and the tool to create the app should be free if possible. No yearly subscription to pay, no fee if the app is made for commercial use…

It can be done: Towards a MOOC “Creating mobile apps” for entrepreneurs

I am really excited to create a MOOC teaching anyone how to create mobile apps for Android, iOS and Windows Phone. It will open on Sept 2015. The objective of this MOOC is to take participants from a very basic starting point (no coding skills) to the creation of their own functional, native app available in Android, Apple and Windows Phone versions, and this in just 30 hours and for free. So that we see every entrepreneur able to launch their own apps.

How do I get more information?

– Subscribe to this mailing list: http://goo.gl/forms/TT7jKcKMHv

– Get in touch on Twitter! @seinecle

A special call for sponsorships This course could use the help of sponsors to make it as awesome as possible. Get in touch.

[EDIT 15/03/2015] First steps here! https://github.com/seinecle/Movie-Matcher/blob/master/readme.md

Call for abstracts – conf “Twitter for Research: Sharing Methods ans Results across disciplines”

This a one-day conference taking place in Lyon on April 24, 2015. Do consider submitting an abstract!

The impetus for co-organizing this conference was the realization that Twitter is used in many different corners in academia, and yet there is little interdisciplinary communication on it.

Researchers are not always aware of how Twitter is used in slightly or totally different ways across the scientific spectrum. Great collaborations, or at least new insights for research, could be born from a day of exchange on the varieties of ways Twitter is used in academic research.

To submit an abstract and get all the information: www.conftwitter2015.org
Some examples of how Twitter is used today for academic research:

Netnographies

Computational linguistics / natural language processing

– Social networks

Education

Marketing

Epidemiology

Finance

Media Studies

Crisis Management

Scientometrics

Journalism

Psychology

Comparing 3 free tools for sentiment analysis on Twitter

Umigon is a free tool for sentiment analysis on Twitter. There are already 2 outstanding free solutions for sentiment analysis out there, so you might wonder why Umigon was worth the effort.

I compare these 3 solutions in terms of 4 features (the 2 columns on the right are the most crucial)

   

Free to use for free text

Connected to Twitter

Works well with natural language (smileys, misspelled words, bad syntax)

Distinguishes between negative facts and negative sentiments

Sentiment140.com

 

NO

YES

YES

NO

Sentiment Analysis at Stanford

 

YES (limited to 200 lines)

  NO

NO

YES

Umigon

 

YES

YES

YES

YES

This is why Umigon can be useful:

– a tool that works well even on text with awful syntax (tweets!)

AND

– which makes a distinction between bad sentiment (“I hate war” -> negative sentiment) and sad facts (“War in Syria” -> neutral sentiment).

Next steps: offer an API, and continue researching on the detection of other semantic features of interest. Umigon already includes one: the detection of promoted discourse in tweets. Watch this space of follow me @seinecle for news!

 

Adventures in biggish data

This is going to be an evolving blog post retracing my current attempt at dealing with a dataset of 65 gigabytes. It will often look silly – that’s because I am not a programmer by training, and I make an effort at honestly recording the steps I took – including all mistakes and “doooohh!” moments.

See the bottom of the post for explanations on some questions. Add yours in the comments if you wish, I’ll do my best to respond.

I do this for the goal of  exploring this dataset visually (an interesting methodological question I find) – and maybe foremost, to learn how to work with big datasets in practice. That’s harder than I thought.

The dataset:

This is the IRI dataset, which is documented in the journal Marketing Science (link to the pdf, link to the jounal website).

It is delivered by post on a external hard drive containing a hierarchy of folders containing csv files (in various formatting) and excel files containing weekly data on product purchases in drugstores and shopping malls – collected across 10 years in participating stores in the US. The size of each file ranges from a couple of megabytes (Mb) to ~ 800 Mb. In total they make ~ 160 Gb, of which only 65 Gb I’ll end up using.

COUNTER OF COSTS SO FAR:

80 euros (server rental costs)
70 euros (one Terabyte external hard drive).

TOTAL ———–> 150 euros.

ACHIEVED SO FAR:

The files have been imported into a database.

Early november 2013

– delivery of the dataset (160Gb) on a 500Gb hard drive.
– reading of the 75 pages pdf coming with the dataset. The datasets contains several different aspects, I realize I’ll start using a portion of it, making 65Gb.
– copy of the dataset on the hard drive of my laptop (450Gb, spinning disk). Note: the laptop has a 2nd hard drive where the OS runs (SSD, 120Gb, almost full).
– I write Java code to parse the files and import them into a Mongo database stored on my 450Gb hard drive, using the wonderfully helpful Morphia (makes the syntax so easy).
– First attempts at importing: I realize that the database will be much bigger in size than the original flat files. Why? I investigate on StackOverflow and get to reduce the size of the future db significantly.
– Still, I don’t know the final size of the db, so there is the risk that my hard drive will get full. I buy a 1 Terabyte / USB 3.0 external hard drive (Seagate, 70 euros at my local store).

Mid November 2013

– First attempts to import the Excel / csv files into MongoDB on this external hard drive. The laptop grinds to a halt after  2 hours of so: memory issues. What, on my 16Gb RAM laptop? The cause: by design, MongoDB will use all the memory available on the system if it needs it. It’s supposed to leave enough RAM for other processes but apparently it does not. I feel stuck. Oh wait, running MongoDB on a virtual machine would allow for allocating a specific amount of RAM to it? I tried Oracle’s Virtual Box but long story short, I can’t run a 64b virtual machine on my 64b laptop because a parameter in my BIOS should be switched on to allow for it, but my BIOS does not feature this parameter (and I won’t flash a BIOS, that’s beyond what I feel able to).

– At this point I realize that the external hard drive I bought won’t serve me here. I need a distant server for the database where Mongo willl sit alone. Or were there other options to keep the data locally?

End November 2013

– I try to rent a server from OVH (13 euros for a month + 13 euros setup costs: 1 Terabyte server with a small processor from Kimsufi, their low cost offer). I don’t get access to it in the following 3 days, and give up. Got a refund later.

– I rent a server (at ~ 40 euros per month, no setup cost) with 2 Terabyte hard drives, 24Gb of RM (!!) and a high performing processor (i9720) from Hetzner’s auction site. Sounds dodgy and too good to be true, yet I get access to it within 3 hours, install Debian and Mongo on it (easier than I thought, given that I am a Linux noob).

– Re-run my Java code on my laptop for importing the Excel/csv files onto this distant server. New bottleneck: it takes ages for the data to transfer from my wifi to the server. Of course…

– I rent a second server (at ~ 40 euros per month, still at Hetzner), in the same geographical region as the first, where I’ll put the data and run my Java code from.
– Start uploading the data to it: takes ages (more than two weeks at this pace).

Early December 2013

– Went to my university to benefit from their transfer speed. After some hicups I got the 65Gb to transfer from my laptop to one of the remote servers I rented in just a couple of hours.
– Starting the import of these 65Gb of csv / Excel files from this server to the Mongodb server. Monitoring the thing since the last 30 minutes, I see that already 60,000,000 917,000,000 (close to 1 billion!!) weekly purchase data transactions have transferred to the db – and counting! (one transaction looks like “this week, 45 packs of Guiness were bought at the store XXX located at Austin, Texas for a total of 200$”). Big data here I come! For some reason the stores descriptions didn’t get stored yet though. I’ll see that later. Very excited about the 1 billion transaction thing. Also worried on how to query this. We’ll see.
– For some reason the database crashed after 1,1 billion transactions imported. Trying to relaunch the import where it stopped, I accidentally drop (delete) the database. Oooops.
– Before relaunching the import, I optimize a bit the code, clear a bug, and go!
– 14 hours that this new import has started. 2,949 stores found and stored, 138,985 products found and stored. And 1,3 billion transactions found and stored, and counting. Wow. No crash, looks good.
– 2 days after it started, the import has finished without a crash! 2.29 billion “weekly purchase data” entries were found and stored in the db. The csv / Excel files take 65Gb of disk space, but once imported in the db the same data takes 400 Gigabytes of space. Wow. Next step: building indexes and start a first query.

QUESTIONS:

– Why not using university infrastructures?

I am transitioning between two universities (from Erasmus University Rotterdam to EMLyon Business School) at the moment, that’s not the right moment to ask for the set-up of a server, which could take weeks anyway. When arriving at EMLyon I’ll reconsider my options. The other reason is that I want to learn how “big data” works in practice. My big dataset is still smallish, and I already run into so many issues. So I am happy to go through it, as it will give me a better comprehension of what’s involved in dealing with the next scale: terabytes. I feel that this first hand knowledge will give me to teach the students in a better way, and that I will make more informed choices when dealing with experts (IT admins from the university or the CNRS) when comes the moment to launch larger scale projects in big data.

– Why MongoDB?

I was just seduced by the easyness of their query syntax. That’s horrifying as a decision parameter, I know. Still, I stand by it. I feel that it is indeed a determining factor because if the underlying performance is good enough (I’ll see that), then as a coder I can choose the db system which is the less painful / nicest to use (though I don’t use it myself, the MongoDB javascript console is I think a main driver behind the adoption of Mongo as a default for the Node.js community, I think). And with the Morphia library added to it, Mongo for Java is just a breeze to use: create POJOs, save POJOs, query POJOs. That’s it:

Datastore ds = ...; // like new Morphia(new Mongo()).createDatastore("hr")
morphia.map(Employee.class);

ds.save(new Employee("Mister", "GOD", null, 0));

// get an employee without a manager
Employee boss = ds.find(Employee.class).field("manager").equal(null).get();

No table, rubbish query syntax or whatever.

Of course, I’ll see with this current experiment if Mongo fits the job or not in terms of performance. If it doesn’t, I’ll explore Neo4J or SQL (in this order).

– Why not Amazon services?

Yes, yes. I am constrained by my attachement to MongoDB here. I would have run MongoDB on Amazon and all would have been fine, maybe. But the instructions on how to run Mongo on Amazon EC2 got me scared.

Can Gephi become an explorer for 3D worlds / virtual realities?

[I am far from being an expert in 3D / virtual reality / vector shapes so feel free to send a tweet @seinecle for corrections if you spot mistakes below]

Yesterday I wrote a plugin that imports vector shapes of country maps (originally in .shp format) into Gephi. It is easy to think that not just 2D shapes like maps, but 3D, dynamic (time evolving) shapes could also be easily imported in Gephi. Because Gephi handles x, y and z coordinates, and handles time-dependent attributes too. So we’ve got all we need to view 3D worlds in Gephi. Here is how I would do it:

– write a parser of 3D shapes formats (DXF, X3D…).
– add the shapes to the graph. Each vector is two nodes and an edge connecting them. Putting that into Gephi is as simple as:

graph.add(node1);
graph.add(node2);
Edge edge = new Edge(node1,node2);
graph.add(edge);

Possible extensions

Yes, the code above would just give you wireframes. Already a good start. I am out of my league here, but I think that new shaders can be written and added to Gephi’s JOGL engine to accomodate for textures, etc. No?

We also need to write some code for mouse movements, to allow for the exploration of the scene in 3D. Not trivial, but this has been implemented in many languages already, so that should be easy to port.

Also, there is no video export function to record animations made in Gephi at the moment, and that’s a pity because movies of 3D animations of vector shapes in Gephi would then become possible. But that’s something that will arrive at some point.

Why would it be interesting?

Well, Gephi is a free even for commercial use, open source, solidly architectured and extensible, multi OS, memory efficient (check here) desktop app. That makes it a robust platform to reach users.

I am up for this project, and at this stage I would appreciate any feedback on the general perspective. Reach me @seinecle on Twitter.

New Gephi plugin: add background maps to your networks

I release a new plugin for Gephi: “Map of countries”.

This plugin is useful when you have a network with geolocalized agents. A plugin released by Alexis Jacomy already makes it possible to display your networks according to geographical coordinates. Now you can add country borders as a background!

You can download this plugin directly from your Gephi software on your computer: go to Tools -> Plugins -> Available plugins. Click on “Check for updates” and then look for “Map of Countries” in the list.

Instructions on how to use this plugin are available here: https://marketplace.gephi.org/plugin/maps-of-countries/

You can choose to display a world map:

world

 

 

or a continent:

 

continent

 

 

or a sub-continent:

subcontinent

 

 

 

or a single country (here, Mexico):

country

 

 

Note: as the map is basically made of nodes and edges just like any network, you can run functions on it. Here is the map of the world, with the community detection applied to it:

coloredworld

 

Enjoy!

Questions, feature requests, bug reports: https://github.com/seinecle/My-Plugins-for-Gephi/issues

 

(I am Clement Levallois, and you can find my work here, and follow me on Twitter).

 

 

 

Gephi – curated list of tutorials

gephi-logo-2010-text-orignal

1. General introductions to Gephi

Gephi Quick start by the Gephi Consortium
A slideshare presentation created by the Gephi team.

Introduction to network visualization with Gephi by Martin GrandJean
All the basics explained in one single web page with clear graphics.

Gephi: A tutorial for beginners
A pdf document,  a bit dense but complete by yours truly.

Gephi: A video tutorial by Stratidev (in French)
A Youtube video in 15 minutes.

Intro to Gephi Handout by Katya Ognyanova
4-page pdf with many screenshots introducing Gephi main functions, very readable.

Gephi Tutorial by Devin Gaffney
A simple web page with illustrations, with a Github repo for more advanced steps and examples of files to play around.

2. Tutorials focused on social media networks

Facebook Network Analysis using Gephi by Sarah Joy Murray.
How to visualize the network of your Facebook friends.

Step-by-step introduction tutorial to Gephi using Facebook network by  learly explained with many screenshots.

Getting Started With The Gephi Network Visualisation App – My Facebook Network part 1 and part 2 by Tony Hirst
A tutorial which has a lot of success.

Visualising Twitter Friend Connections Using Gephi by Tony Hirst
Very detailed, full of tips blog post on how to effectively create a viz of a Twitter network.

3. Gephi: tutorials on advanced functions

Gephi: A tutorial to visualize dynamic networks by myself.
A pdf doc on how to visualize networks that evolve in time with Gephi.

Visualisez dynamiquement le crawl du Googlebot avec Gephi by Aurelien Berrut
[in French]. An excellent blog post with screenshots on how to create dynamic networks from log data.

Getting started with Gephi by History Blogger
This is a small introductory tutorial, but it provides a step-by-step explanation on how to use the plugin SigmaJS to export a visualization to the web. Nice!

Video tutorials on filters and more, by Jennifer Golbeck
Very clearly explained v
ideos accompanying a book on “Analyzing the social web“.

Tutoriel sur les fonctionnalités avancées de Gephi : usage des filtres pour obtenir des cartographies plus lisibles, by  Guillaume Sylvestre
[in French]. Very detailed tutorial focusing on filters.

__________________________________________________________________

[add your tutorial there! Contact me at info[[[this is an arobase]]]exploreyourdata.com or post a comment below]

I am Clement Levallois, and you can find me on Twitter (@seinecle) or check my work on http://clementlevallois.net