The Unscientific Analysis of Languages popular with Indian Startups

Well all this started with this one tweet

And finally ended with this one

And I had the entire dump of Hasjobs postings. It was pretty cool of Kiran to send them across to me and saving me the time and effort to scrape that data. At that time I had very little idea what I would do with it. I am aware of R and this was the moment when I thought I could make use of the little knowledge of it I had.

So I got on with it.

Step 1: Step R on my system.
Step 2: Write some code to extract the data and cleanse it
Step 3: Generate the counts for words
Step 4: Manually pick up the technology words with counts
Step 5: Generate the image with language popularity

So as it stands the top 5 of required technologies for Indian Startups are

1. PHP
2. Android
3. Ruby
4. IOS
5. Javascript


Surprised ? No ?  At least I am because the one technology no one talks about but seems is highly used by Indian startups is PHP. Rest sound very reasonable to me. What do you guys think ?

Following was the code I wrote to extract the results. Let me know if I am missing something.

 install.packages ("tm")
install.packages ("RColorBrewer")
library(NLP)
library(tm)
library(RColorBrewer)
corpus <- Corpus(VectorSource(hasjob.content$headline))
corpus <- tm_map(corpus, content_transformer(tolower))
corpus <- tm_map(corpus, removePunctuation)
corpus <- tm_map(corpus, function(x) removeWords(x, stopwords("english")))
td.mat <- as.matrix(TermDocumentMatrix(corpus))
write.matrix(format(td.mat, scientific=FALSE),
               file = paste(targetPath, "data.csv", sep="/"), sep=",")

Comments

Product Designers Delhi said…
Hey keep posting such good and meaningful articles.
February 01, 2016 3:28 PM
Post a Comment

Popular posts from this blog

[24th November 2024] Interesting Things I Learnt This Week

Image
1. AI Makes Tech Debt More Expensive - The core argument is that generative AI significantly widens the gap between codebases with low and high tech debt. Companies with clean code can leverage generative AI to write code much faster, while companies with messy legacy codebases struggle to adopt these new tools. This widens the gap between the two codebases, making tech debt even more expensive for companies with outdated systems. My Take : Legacy systems inevitably accumulate technical debt. A common analogy for this is the state of urinals over time: missed targets lead to increasing mess, keeps pushing people away from urinals and leading to more mess in turn. AI-powered coding tools could potentially accelerate the accumulation of tech debt. However, skilled engineers can leverage AI to reduce and even eliminate it. Ultimately, AI is just a tool, and human judgment remains crucial for making informed decisions. I hope AI will be used responsibly to drive positive outcomes. 2. Make...
Read more

[17th November 2024] Interesting Things I Learnt This Week

Image
 1. Booleans are a trap : Booleans, while seemingly simple, can introduce complexity and limitations into domain modeling. When used to represent complex states, booleans can lead to unclear code and potential errors. To avoid these pitfalls, consider using enums or enum sets. These approaches provide a more expressive and maintainable way to model complex states in your applications. My Take : I wish I had learned this earlier in life. When working with Google Docs, I realized the limitations of booleans. Expanding boolean-based behaviors often led to adding more fields, making the system complex. Enums would have provided a simpler solution. The problem wasn't just the booleans themselves, but also the need to pass them through multiple layers of the stack, often requiring additional parameters. Another important aspect of enums is the inclusion of an " enum not specified " state. This state explicitly indicates that an enum value hasn't been set, which can be usef...
Read more

[1st December 2024] Interesting Things I Learnt This Week

Image
1. C++ Pitfalls - this article, explains some of the pitfalls you can fall into when programming in C++ and shares their experience with them. This includes Operator precedence, Arithmetic conversion rules, Right bit-shift, Implicit conversions and Order of evaluation.  My Take : We've all read about JavaScript's quirks, leading to unexpected outputs. But let's remember that such behaviors aren't unique to JavaScript. Even languages like C and C++ have their share of ambiguities and compiler-specific behaviors. While it's tempting to dive deep into these intricacies, I've found that focusing on clear, consistent, and unambiguous code is often more practical. However, understanding these nuances is essential for any experienced programmer. It helps us avoid pitfalls and write robust, reliable code. Remember, mastering a language isn't just about writing "Hello World." It involves delving into its intricacies, including its counterintuitive and ambi...
Read more