I am loving James blog (spent an hour this morning reading random entries after discovering it). He has some good advice on idea generation:
Get good at idea generation.
Ideas have a bad reputation. They’re a dime a dozen. They’re worthless unless implemented. Success is 90% perspiration. We’ve all seen the calls for help from a self-proclaimed designer and his business partner who have a brilliant company logo and a sure-fire concept for an app. All they need is a programmer or two to make it happen, and we all know why it won’t work out.
Now get past ideas needing to be on a grand scale–the vision for an entire project–and think smaller. You’ve got a UI screen that’s confusing. You have something non-trivial to teach users and there’s no manual. The number of tweakable options is getting out of hand. Any of the problems that come up dozens of times while building anything.
As you read through the rest of the post, you start realizing that ideas are really starting points for doing something useful. Ideas trigger other ideas. This chain reaction is what gets you to start building useful stuff. Taking small ideas, thinking a lot about them, choosing alternatives and testing a few out through implementation is part of the creative process. In fact this is the skill every product team needs, anyway.
From Guardian’s Data Blog:
We are drowning in information. The web has given us access to data we would never have found before, from specialist datasets to macroeconomic minutiae. But, look for the simplest fact or statistic and Google will present a million contradictory ones. Where’s the best place to start?
That’s how this blog came about. Everyday we work with datasets from around the world. We have had to check this data and make sure it’s the best we can get, from the most credible sources. But then it lives for the moment of the paper’s publication and afterward disappears into a hard drive, rarely to emerge again before updating a year later.
Kunal Jain on Single habit that defined the trajectory of my career
What is the single most important habit that shaped up my career? This is the habit which propelled me from from being just an ordinary analyst to some one who can influence, manage and mentor people in Analytics industry.
Here is the habit:
Spend a defined fraction of your day working on the the most important project / problem you have.
Please note the importance of two words here: defined and most important. You need to fix what fraction of your time you would spend and what is the most important task for you.
I discovered Kunal through an article on KDNuggets. Found his Twitter account and followed him and from there to his LinkedIn account to this article. It is nice to see people sharing so much of their knowledge through Tweets and blog posts.
A couple of other useful links if you are interested in Analytics from Kunal
Must read books and blogs on Web Analytics
Analytics Vidhya Twitter Account
Thanks Kunal. We need more people like you.
From A concrete approach to learning how to program
there are good arguments for and against both of these approaches. But my experience has shown me that bottom-up approaches tend to be better for the vast majority of beginners, while only a select few beginners can easily excel at the top-down approach. My hunch is that most of these folks who excel at learning via top-down approaches have some kind of prior experience with programming, or have some natural inclination towards being adept with technology.
I am biased since I started with the bottom-up approach. What is your experience in training developers?
Did You Know That Square Root of 2 Was the First Irrational Number Discovered?
And a funny story associated with it?
Did you know that Pythagoreans preached that all numbers could be expressed as the ratio of integers, and the discovery of irrational numbers is said to have shocked them.
Pappus merely says that the knowledge of irrational numbers originated in the Pythagorean school, and that the member who first divulged the secret perished by drowning
I read this first in Number Freak a great book about numbers (that i am currently reading). The proof of √2 being an irrational number is an interesting one.
My first few hires were three students from Indian Institute of Science who had no experience in programming. With a bit of help from me, they built a SQL database engine prototype in less than 6 months. That was in mid 80s. One of them later joined Microsoft (in the first batch of hires from India). and another went and built an EJB Server from scratch for another company.
Since then, I have been mostly hiring fresh, promising students, right out of the colleges and investing a few months guiding them, personally. The results have been amazing.
So if you are part of the software industry, look for promising people with the following attributes:
- Curiosity – Always questioning everything and never stop wondering, what if…
- Ability to learn fast
- An attitude of “Can Do”.
- Ability to persist undaunted in the face of failure.
- An urge to create – make something, small or big.
- A certain intelligence about everything around them (not just the subjects they are studying)
- A healthy relationship of people and things
Once a friend asked me why I don’t like aptitude tests. The main reason is that these tests can not find people with any mix of these skills. They are more like filters than tools for discovering amazing people to work with.
C and Java keep changing their positions to be in the first slot. But it is interesting that Objective moved past C# and C++. The most notable thing is that F# is moving up (now at the 12th position) and it moved past Ruby in TIOBE rankings. Here is how TIOBE calculates the rankings.
The Programming Popularity Index (the latest one as of this time is for Feb) tells a slightly different story. It is based on different parameters.
The PYPL PopularitY of Programming Language Index is created by analyzing how often language tutorials are searched on Google : the more a specific language tutorial is searched, the more popular the language is assumed to be. It is a leading indicator.
However, the job trends (are they lagging indicators) show a slightly different picture with Objective-C and Ruby lead in relative growth.
One thing is certain. Objective-C is moving up consistently in all these indexes as well as in Job Trends.
Astronomers are probably one of the most data intensive people. They use data for observation and discovery. They use some of the insights from data to find and analyze more data. Here is a classic example – From Kepler Data, Astronomers Find Galaxy Filled With More but Smaller Worlds
The new planets were culled from 3,601 candidates previously found by Kepler, using a new statistical technique known as verification by multiplicity. The method vastly reduces the need for outside telescopic observations to verify suspected planets in batches. It works only for multiple-planet systems, but as Dr. Lissauer and his colleagues pointed out, that includes about 40 percent of the Kepler candidates.
There is more to come, the astronomers said. The present results are based on a statistical analysis of only the first two years of Kepler data. There is two more years’ worth to go, and several hundred more planets are likely to be verified.
It took more than an hour to watch this video on Designing Data Visualizations. Every minute was worth it. While I knew a bit about the subject, this talk walks you through the elements of visualization and how to go about designing one. There is a lot to learn from this talk and later from the book:
- Types of Information Products
- Difference between Infographics and Visualizations
- Choice of Visualization elements
- A walk through of few visualizations
- How to go about designing a visualization
Here are few screen shots from the talk.
At a high level, you need to understand:
Then you focus on the user.
Paying attention to the data you have to work with helps.
These slides are just a small sample. They will give you an inkling about the approach. For all the good stuff, please watch the video and then get this book.
From the Preface of the book:
The path from journeyman to master is long. In the case of data visualization, the path has been well marked by many accomplished designers and cognitive scientists who have been doing great work for decades. We gladly follow in their footsteps, and we hope you will, too.
Our goal is to give you confidence as you begin your journey.
Thank God that there is internet that enable tools like YouTube which provide acess to channels like LinkedIn Tech Talks who bring you people like Noah to share their knowledge.
If all the data on the Web were open and linked, it would be easier to establish information systems combining different distributed data repositories. Thus, the Web of Data would enable access and sharing of data and knowledge without barriers.
The article talks about 7 things you need to know about Linked Open Data
- What is Linked Data and Linked Open Data?
- How does it work?
- Who is doing it?
- Why is it significant?
- What are the downsides?
- Where is it going?
- What are the implications for institutional repositories?
A great example of Linked Open Data is DbPedia (an LOD repository extracted from Wikipedia). If you have ideas for DbPedia and want to help at Google Summer of Code (GSOC) program, please read this.
We are still in need of ideas and mentors. If you have any improvements on DBpedia or DBpedia Spotlight that you would like to have done, please submit it in the ideas section now. Note that accepted GSoC students will receive about 5000 USD, which can help you to estimate the effort and size of proposed ideas. It is also ok to extend/amend existing ideas (as long as you don't hi-jack them). Please edit here: