Rahul gives unsolicited advice

I removed a lot of personal information from Facebook on Friday night and I ended up with a small box on the left hand side of my Wall, asking me for personal information, prodding me to complete my profile. One of those questions were on where I went to college. The options I saw were a list of colleges friends of mine went to. What I found interesting was the last option that read "I didn't go to university." Why is this option Private by default? I wonder why Facebook chose to make it Private if someone didn't go to university. Is it because of the usual stigma associated with not being a college graduate? Just wanted to point it out.

So, I removed most personal information from Facebook and I ended up with a box on my Wall asking me to fill in some information. One of the questions is about where I worked at in the past. The options Facebook gave me are : The options Facebook gave surprised me, to say the least. The reason I am surprised at these options is because of how they differ from options I got for other questions. For example, the options I got for the question of where I live currently was a list of places my friends currently live in. Similarly, the options I got for the question of where I studied was a list of colleges my friends studied at. The only company on this list that my friends work at is Ather, as is evidenced. I cannot recall a single person in my Facebook friend circle who works at either TCS or Cognizant or at HCL. Why Facebook decided to add those items to the list will remain a mystery.

If you haven't read my last post , I am taking a look a Stack Overflow's yearly developer survey. After the survey is complete, the release the data collected publicly, which can be found here . At first, I was only looking at numbers from the 2017 survey but there were a few questions I wanted to ask using survey numbers from 2011-2017, such as the ratio of Male/Female developers, among those responded to the developer survey and the change in salary of developers over the last 7 years. Which is when I hit a roadblock - the actual survey data. Real life data is a mess, starting from the weird formats it is saved in to the weird data saved in them. For example, the developer's country/region has been a consistent question over the years but the possible answers have changed e.g. from "United States of America" to "United States". The column name for this data has changed as well, from "country" to "Country" to "What country ...

Prelude If you didn't know, Stack Overflow is a community of 7.3 million programmers, just like you, helping each other. It's a Q&A forum with probably the most comprehensive knowledge of the intricacies of programming languages and associated libraries, knowledge which volunteers contributed. Being part of the programming community, they run a survey every year. The survey is developer-centric and asks questions on what language you use, what language you want to learn/use, how much you get paid, what your title is, where you work from, what your gender is, and so on. It is one of the largest surveys, as far as I know. This blogpost looks at data from the most recent SO survey from 2017. Specifically, it looks at data submitted by Indian developers. Before we take a peek at the data, the data is freely available at this URL . You will also find raw data from surveys through 2011-2017. You might also want to take a look at this blogpost from the SO folks regarding...

On 21 May, I conducted a workshop on Scientific Computing 101 using Python. Over the course of 3 hours, I introduced the Numpy and Pandas packages. The participants practiced using the packages in Jupyter Notebooks. It was a great learning experience for me to make the slides and deliver the material. I did a workshop at IIT Madras earlier on the same topic but this one was better planned and cleaner content. The content was delivered using Jupyter Notebooks, which can be found on my github repository . I have been working on the notebooks to add documentation, references and a few exercises. I will keep adding more exercises in time. Going forward, the participants were interested in a followup workshop, with a focus on data science, which I presume means Machine Learning using scikit-learn. Let's see how that goes. Until next time.

Last weekend, I gave a brief talk on Virtual Environments in Python at the April meet of the PythonPune meetup group . Yesterday, I conducted a workshop on the same topic at the May meet of the PyLadies meetup group . The slides can be found on a GitHub repository . The GitHub repo also contains slides for most other talks/workshops I gave/conducted. To give you a brief overview of the slides, I introduce what a Virtual Environment are, why we should use them and finally, how we go about using them. Along with Virtual Environments, I also introduce environment managers such as conda and edm and talk about how the environments created by them differ from virtual environments. Neither virtual environments not environment managers help understand the Python language better but they are crucial to using Python in enterprise/open-source projects. A large number of open source Python libraries suggest the use of virtual environments if you are interested in contributing.

Yesterday, I conducted a workshop on Python Packages at the May meet of the PyLadies meetup group . Slides I used for the workshop can be found on the GitHub repository . The GitHub repository also contains slides I used for other talks/workshops I gave/conducted. To give you a brief overview of the workshop, I first introduced what a Python package is, why we should use them and finally, how to go about creating a Python package. Python packages make distribution and installation of your work easy, crucial to get others to use your work. Two references I highly recommend are the sample project published by PyPA and the Packaging User Guide, also published by PyPA .

The cost of launching satellites is going down, be it in the US or in India. Smaller and easier to develop micro-satellites are the latest trend, usually developed with a specific goal in mind e.g. satellite imagery. These, and a lot of other factors, have contributed to an increase in the number of satellites hovering over earth in the recent years, a number which is bound to only keep increasing. ISRO recently put 88 (micro) satellites belonging to Planet Labs , satellites which Planet Labs will use to image the Earth everyday. ISRO and other government space organizations themselves have satellites that image the Earth and/or their respective countries on a regular basis. And a number of these organizations are releasing their data publicly. With that context, I realized a while back that daily imagery will help identify and possibly curb sand mafia, specifically in India . The boom in infrastructure, specifically housing and office construction in India, was one of the reasons...

My fascination of drones is growing day after day. Of all the areas drones can be used, I want to talk about exploration for the moment. You might be wondering, what exploration? All corners of the Earth have been explored and there's nothing else to discover. Au Contraire. Correction. The surface of the Earth has been explored, not what's underneath. NASA and other space organizations have been sending out satellites and telescopes to understand what's out there but similar efforts aren't being made to understand unexplored caves and the depths of the ocean. Two articles that I read recently drove this point home for me; one on exploring a cave formation in Uzbekistan and another on exploring the Indian Ocean's floor . The first is an account of a team of scientists' and explorers' attempt at exploring a cave formation and discovering new regions of the cave. The second is an account of how the search for the missing MH370 flight also led to a detaile...

This article on Baidu and it's bet on AI sparked a thought in my head. Well, the thought was to basically copy what Baidu is doing in China and do the same in India. I wrote earlier on the language barrier in India and how AI can help solve the problem. The crux of what Baidu is doing in China is to employ a bunch of people to translate English technical documents to Chinese (Mandarin prolly). By doing so, not only is the industry able to communicate with Chinese customers better, Baidu now has a big database of English-Chinese (Mandarin) translations. Don't you think doing the same with Indian languages would be awesome? Instead of hackathons and docathons, we could have translate-athons (?) where volunteers could translate documents into the numerous Indian languages and in the process, help in compiling an impressive English-Indian language dataset, a dataset that can now be used to train AI to better translate between English and Indian languages and maybe even betwe...