How to be a better writer


Background- I wrote this as an accident while trolling on Quora. I was not confident of what I wrote- in fact I wrote it anonymous except people kept asking me why! It was pure serendipity- I wrote it less than 4 minutes and submitted without thinking. Then edited once based on feedback.

Some one clearly more smarter than me made my tips for writing into a picture

and it went popular on Tumblr just like it did on Quora!

Apparently if some guy like Wil Wheaton likes your words, it can go viral!  It has 41799 notes ( reblogs+hearts) on Tumblr as of now.

Words . Reposted by a member of STAR TREK:NG. I can now die a happy Geek! The Internet is a funny thing!

Thank you everyone! Now if only Google learnt to include OCR for Images as part of text search!

  1. Write 50 words . That’s  a paragraph.
  2. Write 400 words . That’s a page.
  3. Write 300 pages. That’s a manuscript.
  4. Write everyday. That’s a habit.
  5. Edit and Rewrite. That’s how you get better.
  6. Spread your writing for people to comment. That’s called feedback.
  7. Dont worry about rejection or publication. That’s a writer.
  8. When not writing, read. Read from writers better than you. Read and Perceive.

But overall, just write more to get better.

1887+ votes on Quora!! :) Probably my most viewed content ever- !

61036 people  have viewed this answer!

Also it got a mention here-

Now I think I should take some of my own advice and get back to writing changes Analytics made a cool change to WP- Stats , its default analytics program, It now counts visitors as well as visits. This is especially useful to writers like me who like to customize content based on analytics ( GA Analytics does not work on hosted sites) and who dont want to bother with hosting /hacking ( is much more hassle free than self-hosted , or even Rackspace hosted in my experience).

Of course the guy DOS-ing my poetry blog (via Yahoo Image Service) needs to use Tor to hide. Unless they dont want to hide and just want to click on my Roses poem 200 times (passing a subliminal message?) . Of course GA Analytics loves line charts and WP Stats loves Bar Charts, but we wont get into that.



R and Hadoop #rstats

Lovely ppt from the formidable Jeffrey Bean, whose lucid style in explaining R has made me a big fan of his awesome work!

Take at look at his extensive collection of Big Data with R slides  at – they are both very comprehensive and a delightful addition to anyone wishing to go the cloud, hadoop, R  route
His blog at talks of lots of very relevant topics.

Interview John Myles White , Machine Learning for Hackers

Here is an interview with one of the younger researchers  and rock stars of the R Project, John Myles White,  co-author of Machine Learning for Hackers.

Ajay- What inspired you guys to write Machine Learning for Hackers. What has been the public response to the book. Are you planning to write a second edition or a next book?

John-We decided to write Machine Learning for Hackers because there were so many people interested in learning more about Machine Learning who found the standard textbooks a little difficult to understand, either because they lacked the mathematical background expected of readers or because it wasn’t clear how to translate the mathematical definitions in those books into usable programs. Most Machine Learning books are written for audiences who will not only be using Machine Learning techniques in their applied work, but also actively inventing new Machine Learning algorithms. The amount of information needed to do both can be daunting, because, as one friend pointed out, it’s similar to insisting that everyone learn how to build a compiler before they can start to program. For most people, it’s better to let them try out programming and get a taste for it before you teach them about the nuts and bolts of compiler design. If they like programming, they can delve into the details later.

We once said that Machine Learning for Hackers  is supposed to be a chemistry set for Machine Learning and I still think that’s the right description: it’s meant to get readers excited about Machine Learning and hopefully expose them to enough ideas and tools that they can start to explore on their own more effectively. It’s like a warmup for standard academic books like Bishop’s.
The public response to the book has been phenomenal. It’s been amazing to see how many people have bought the book and how many people have told us they found it helpful. Even friends with substantial expertise in statistics have said they’ve found a few nuggets of new information in the book, especially regarding text analysis and social network analysis — topics that Drew and I spend a lot of time thinking about, but are not thoroughly covered in standard statistics and Machine Learning  undergraduate curricula.
I hope we write a second edition. It was our first book and we learned a ton about how to write at length from the experience. I’m about to announce later this week that I’m writing a second book, which will be a very short eBook for O’Reilly. Stay tuned for details.

Ajay-  What are the key things that a potential reader can learn from this book?

John- We cover most of the nuts and bolts of introductory statistics in our book: summary statistics, regression and classification using linear and logistic regression, PCA and k-Nearest Neighbors. We also cover topics that are less well known, but are as important: density plots vs. histograms, regularization, cross-validation, MDS, social network analysis and SVM’s. I hope a reader walks away from the book having a feel for what different basic algorithms do and why they work for some problems and not others. I also hope we do just a little to shift a future generation of modeling culture towards regularization and cross-validation.

Ajay- Describe your journey as a science student up till your Phd. What are you current research interests and what initiatives have you done with them?

John-As an undergraduate I studied math and neuroscience. I then took some time off and came back to do a Ph.D. in psychology, focusing on mathematical modeling of both the brain and behavior. There’s a rich tradition of machine learning and statistics in psychology, so I got increasingly interested in ML methods during my years as a grad student. I’m about to finish my Ph.D. this year. My research interests all fall under one heading: decision theory. I want to understand both how people make decisions (which is what psychology teaches us) and how they should make decisions (which is what statistics and ML teach us). My thesis is focused on how people make decisions when there are both short-term and long-term consequences to be considered. For non-psychologists, the classic example is probably the explore-exploit dilemma. I’ve been working to import more of the main ideas from stats and ML into psychology for modeling how real people handle that trade-off. For psychologists, the classic example is the Marshmallow experiment. Most of my research work has focused on the latter: what makes us patient and how can we measure patience?

Ajay- How can academia and private sector solve the shortage of trained data scientists (assuming there is one)?

John- There’s definitely a shortage of trained data scientists: most companies are finding it difficult to hire someone with the real chops needed to do useful work with Big Data. The skill set required to be useful at a company like Facebook or Twitter is much more advanced than many people realize, so I think it will be some time until there are undergraduates coming out with the right stuff. But there’s huge demand, so I’m sure the market will clear sooner or later.

The changes that are required in academia to prepare students for this kind of work are pretty numerous, but the most obvious required change is that quantitative people need to be learning how to program properly, which is rare in academia, even in many CS departments. Writing one-off programs that no one will ever have to reuse and that only work on toy data sets doesn’t prepare you for working with huge amounts of messy data that exhibit shifting patterns. If you need to learn how to program seriously before you can do useful work, you’re not very valuable to companies who need employees that can hit the ground running. The companies that have done best in building up data teams, like LinkedIn, have learned to train people as they come in since the proper training isn’t typically available outside those companies.
Of course, on the flipside, the people who do know how to program well need to start learning more about theory and need to start to have a better grasp of basic mathematical models like linear and logistic regressions. Lots of CS students seem not to enjoy their theory classes, but theory really does prepare you for thinking about what you can learn from data. You may not use automata theory if you work at Foursquare, but you will need to be able to reason carefully and analytically. Doing math is just like lifting weights: if you’re not good at it right now, you just need to dig in and get yourself in shape.
John Myles White is a Phd Student in  Ph.D. student in the Princeton Psychology Department, where he studies human decision-making both theoretically and experimentally. Along with the political scientist Drew Conway, he is  the author of a book published by O’Reilly Media entitled “Machine Learning for Hackers”, which is meant to introduce experienced programmers to the machine learning toolkit. He is also working with Mark Hansenon a book for laypeople about exploratory data analysis.John is the lead maintainer for several R packages, including ProjectTemplate and log4r.

(TIL he has played in several rock bands!)

You can read more in his own words at his blog at
He can be contacted via social media at Google Plus at or twitter at

Download Decisionstats

I was picking up some funny activity on my web analytics, so to make it easier for readers, here is the entire Decisionstats wordpress xml file zipped. You can download it, unzip and then read it in any wordpress reader to read at your leisure.



Have fun


Updated- There seems to be unusual traffic activity on my poetry blog To make it more convenient for readers , you can download that as a zipped WordPress XML file here-



Interview Alvaro Tejada Galindo, SAP Labs Montreal, Using SAP Hana with #Rstats

Here is a brief interview with Alvaro Tejada Galindo aka Blag who is a developer working with SAP Hana and R at SAP Labs, Montreal. SAP Hana is SAP’s latest offering in BI , it’s also a database and a computing environment , and using R and HANA together on the cloud can give major productivity gains in terms of both speed and analytical ability, as per preliminary use cases.

Ajay- Describe how you got involved with databases and R language.
Blag-  I used to work as an ABAP Consultant for 11 years, but also been involved with programming since the last 13 years, so I was in touch with SQLServer, Oracle, MySQL and SQLite. When I joined SAP, I heard that SAP HANA was going to use an statistical programming language called “R”. The next day I started my “R” learning.

Ajay- What made the R language a fit for SAP HANA. Did you consider other languages? What is your view on Julia/Python/SPSS/SAS/Matlab languages

Blag- I think “R” is a must for SAP HANA. As the fastest database in the market, we needed a language that could help us shape the data in the best possible way. “R” filled that purpose very well. Right now, “R” is not the only language as “L” can be used as well ( …not forgetting “SQLScript” which is our own version of SQL ( . I have to admit that I tried Julia, but couldn’t manage to make it work. Regarding Python, it’s an interesting question as I’m going to blog about Python and SAP HANA soon. About Matlab, SPSS and SAS I haven’t used them, so I got nothing to say there.

Ajay- What is your view on some of the limitations of R that can be overcome with using it with SAP HANA.

Blag-  I think mostly the ability of SAP HANA to work with big data. Again, SAP HANA and “R” can work very nicely together and achieve things that weren’t possible before.

Ajay-  Have you considered other vendors of R including working with RStudio, Revolution Analytics, and even Oracle R Enterprise.

Blag-  I’m not really part of the SAP HANA or the R groups inside SAP, so I can’t really comment on that. I can only say that I use RStudio every time I need to do something with R. Regarding Oracle…I don’t think so…but they can use any of our products whenever they want.

Ajay- Do you have a case study on an actual usage of R with SAP HANA that led to great results.

Blag-   Right now the use of “R” and SAP HANA is very preliminary, I don’t think many people has start working on it…but as an example that it works, you can check this awesome blog entry from my friend Jitender Aswani “Big Data, R and HANA: Analyze 200 Million Data Points and Later Visualize Using Google Maps “ (!/2012/04/big-data-r-and-hana-analyze-200-million.html)

Ajay- Does your group in SAP plan to give to the R ecosystem by attending conferences like UseR 2012, sponsoring meets, or package development etc

Blag- My group is in charge of everything developers, so sure, we’re planning to get more in touch with R developers and their ecosystem. Not sure how we’re going to deal with it, but at least I’m going to get myself involved in the Montreal R Group.



Name: Alvaro Tejada Galindo
Profession: Development
Company: SAP Canada Labs-Montreal
Town/City: Montreal
Country: Canada
Instant Messaging Type: Twitter
Instant Messaging ID: Blag
Personal URL:
Professional Blog URL:
My Relation to SAP: employee
Short Bio: Development Expert for the Technology Innovation and Developer Experience team.Used to be an ABAP Consultant for the last 11 years. Addicted to programming since 1997.

and from

SAP HANA is SAP AG’s implementation of in-memory database technology. There are four components within the software group:[1]

  • SAP HANA DB (or HANA DB) refers to the database technology itself,
  • SAP HANA Studio refers to the suite of tools provided by SAP for modeling,
  • SAP HANA Appliance refers to HANA DB as delivered on partner certified hardware (see below) as anappliance. It also includes the modeling tools from HANA Studio as well replication and data transformation tools to move data into HANA DB,[2]
  • SAP HANA Application Cloud refers to the cloud based infrastructure for delivery of applications (typically existing SAP applications rewritten to run on HANA).

R is integrated in HANA DB via TCP/IP. HANA uses SQL-SHM, a shared memory-based data exchange to incorporate R’s vertical data structure. HANA also introduces R scripts equivalent to native database operations like join or aggregation.[20] HANA developers can write R scripts in SQL and the types are automatically converted in HANA. R scripts can be invoked with HANA tables as both input and output in the SQLScript. R environments need to be deployed to use R within SQLScript

More blog posts on using SAP and R together

Dealing with R and HANA
R meets HANA

HANA meets R
When SAP HANA met R – First kiss–first-kiss



SAP HANA: My experiences on using SAP HANA with R

and of course the blog that started it all-

Jitender Aswani’s




Here is an interview with Charlie Parker, head of large scale online algorithms at

Ajay-  Describe your own personal background in scientific computing, and how you came to be involved with machine learning, cloud computing and

Charlie- I am a machine learning Ph.D. from Oregon State University. Francisco Martin (our founder and CEO), Adam Ashenfelter (the lead developer on the tree algorithm), and myself were all studying machine learning at OSU around the same time. We all went our separate ways after that.

Francisco started Strands and turned it into a 100+ million dollar company building recommender systems. Adam worked for CleverSet, a probabilistic modeling company that was eventually sold to Cisco, I believe. I worked for several years in the research labs at Eastman Kodak on data mining, text analysis, and computer vision.

When Francisco left Strands to start BigML, he brought in Justin Donaldson who is a brilliant visualization guy from Indiana, and an ex-Googler named Jose Ortega who is responsible for most of our data infrastructure. They pulled in Adam and I a few months later. We also have Poul Petersen, a former Strands employee, who manages our herd of servers. He is a wizard and makes everyone else’s life much easier.

Ajay- You use clojure for the back end of .Are there any other languages and packages you are considering? What makes clojure such a good fit for cloud computing ?

Charlie- Clojure is a great language because it offers you all of the benefits of Java (extensive libraries, cross-platform compatibility, easy integration with things like Hadoop, etc.) but has the syntactical elegance of a functional language. This makes our code base small and easy to read as well as powerful.

We’ve had occasional issues with speed, but that just means writing the occasional function or library in Java. As we build towards processing data at the Terabyte level, we’re hoping to create a framework that is language-agnostic to some extent. So if we have some great machine learning code in C, for example, we’ll use Clojure to tie everything together, but the code that does the heavy lifting will still be in C. For the API and Web layers, we use Python and Django, and Justin is a huge fan of HaXe for our visualizations.

 Ajay- Current support is for Decision Trees. When can we see SVM, K Means Clustering and Logit Regression?

Charlie- Right now we’re focused on perfecting our infrastructure and giving you new ways to put data in the system, but expect to see more algorithms appearing in the next few months. We want to make sure they are as beautiful and easy to use as the trees are. Without giving too much away, the first new thing we will probably introduce is an ensemble method of some sort (such as Boosting or Bagging). Clustering is a little further away but we’ll get there soon!

Ajay- How can we use the API using R and Python.

Charlie- We have a public github repo for the language bindings. Right now, there there are only bash scripts but that should change very soon. The python bindings should be there in a matter of days, and the R bindings in probably a week or two. Clojure and Java bindings should follow shortly after that. We’ll have a blog post about it each time we release a new language binding.

Ajay-  How can we predict large numbers of observations using a Model  that has been built and pruned (model scoring)?

Charlie- We are in the process of refactoring our backend right now for better support for batch prediction and model evaluation. This is something that is probably only a few weeks away. Keep your eye on our blog for updates!

Ajay-  How can we export models built in for scoring data locally.

Charlie- This is as simple as a call to our API. The call gives you a JSON object representing the tree that is roughly equivalent to a PMML-style representation.


You can read about Charlie Parker at and the rest of the BigML team at