The mobile gaming market in North America continues to grow. As of Q3 2014, there are 141.9MM NA mobile gamers, up from 111.3MM NA mobile gamersin 2013. These mobile gamers spent an average of $32.65 in the last year,generating $4.63B in NA mobile gaming revenue.
The graph below provides a view of how gamers discover games (click on it to get a larger view)
For a great analysis please see this free report.
14 Ways to Acquire Knowledge: A Timeless Guide from 1936 by Maria Popova is a great read.
Here are a few fragments from the original post. There is a lot more to read and reflect upon.
If you must read in order to acquire knowledge, read critically. Believe nothing till it’s understood, till it’s clearly proven.
To know it — write it! If you’re writing to explain, you’re explaining it to yourself! If you’re writing to inspire, you’re inspiring yourself! If you’re writing to record, you’re recording it on your own memory.
To learn, experiment! Try something new. See what happens. Lindbergh experimented when he flew the Atlantic. Pasteur experimented with bacteria and made cow’s milk safe for the human race. Franklin experimented with a kite and introduced electricity.
If you would have knowledge, knowledge sure and sound, teach. Teach your children, teach your associates, teach your friends. In the very act of teaching, you will learn far more than your best pupil.
Animals have knowledge. But only men can reason. The better you can reason the farther you separate yourself from animals.
I am fascinated by Machine Learning (ML) and keep looking for case studies were ML solves real world problems. This Talk – Machine Learning: The Basics by Ron Bekkerman( video), provides a great overview of machine learning and how it is being used by LinkedIn for Job Analysis. LinkedIn is one of the early companies to jump in to Data Science. With over 200 million subscribers, they have ample data to analyze. The data is very contextual too and that helps build better algorithms (they claim 95% accuracy in prediction in a specific case). At one point in the talk Ron mentions that the ML study helped in building a product that generates about 6 million dollars in revenue for LinkedIn. That is great pay off.
Why is job analysis interesting in general? It provides you with some interesting insights into the direction a specific industry is moving:
- If you are in the (IT staffing) industry, you may want to know what kinds of jobs are in demand? And which ones are growing and which ones are shrinking?
- If you are an outsourcing company, you may want to analyze the hiring patterns in different parts of the world
- What kinds of skills are in demand for startups, medium sized companies and large enterprises? Lots of people from startups to training companies can use this data to build and tailor their offerings.
- How do training companies and conference organizers meet the need for skills using job analysis?
Ultimately, it is all Market Intelligence of a kind. It is fascinating that, now we have large data to analyze and get some glimpses into the patterns of demand/supply. So where do you get all this data from? That is a topic for another blog post.
One of our interns is working on an app to do Job Classification and automatic tagging of jobs. We were debating whether we should use some simple techniques or ML. I was going around looking for case studies and stumbled upon this video.
From the book – Open Data Now:
In a series of studies led by Harvey Lewis, a research director in Deloitte’s Insight Team, the firm has identified five Open Data business “archetypes”:
- Suppliers publish their data as Open Data that can be easily used. While they don’t charge for the data—if they did, it wouldn’t be Open Data—they increase customer loyalty and enhance their reputations by releasing it.
- Aggregators collect Open Data, analyze it, and charge for their insights or make money from the data in other ways.
- Developers “design, build, and sell web-based, tablet, or smart-phone applications” using Open Data as a free resource.
- Enrichers are “typically large, established businesses” that use Open Data to “enhance their existing products and services,” for example by using demographic data to understand their customers better.
- Enablers charge companies to make it easier for them to use Open Data.
You can download a sample chapter free.
From Key Digital Trends for 2014:
Don’t just focus on Big Data; think about shifting emphasis to “Smart Data”.
So what are the jobs that let us:
- Find a variety of useful data sources and integrate them?
- Analyze large volume of (unstructured) data?
- Intelligence Monitoring?
- Make sense of it all the “smart data”
- Gain insight and ask critical business questions?
Something to think about.
If run any kind of business, this book Competitive Intelligence Advantage: How to Minimize Risk, Avoid Surprises, and Grow Your Business in a Changing World is worth a read.
Here is a picture from the book that talks about Competitive Environment (with due acknowledgement) an amazingly clear picture that demystifies Competitive Intelligence.
I will provide a brief summary once I finish the book. Sheena demystifies a lot of confusing terms like Business Intelligence, Competitive Intelligence, Competitor Intelligence, Open Source Intelligence and a whole lot of others.
This book provides a compelling argument about why every company should pay attention to competitive intelligence. Sheena provokes a lot of reflection about information, intelligence and a context in which you can think about them. .
From @sharads tweet, I discovered JP’s blog. I would like to share a couple of snippets from his About This Blog page.
I believe that Moore’s Law and Metcalfe’s Law and Gilder’s Law have created an environment where it is finally possible to demonstrate the value of information technology in simple terms rather than by complex inferences and abstract arguments.
I believe that simplicity and convenience are important, and that we have to learn to respect human time.
I believe we need to discuss these things and find ways of getting them right. And I have a fervent hope that through this blog, I can keep the conversations going and learn from them.
I agree – we do need to discuss these things and find ways of getting them right.
Twitter is a great tool of information discovery if you learn to do some of the following:
- Follow the right people or topics
- Create lists focused on your topics of interest – one list for each topic
- Find a way to filter out noise (include duplicates)
Most of my reading material comes from following some of the most informed people around me.
I love the way that Twitter points me to interesting stuff and causes me to Google, think about and research things that I might otherwise have not. I read things that resonate with me and I am almost compelled to follow them up.
From Gurteen KnowledgeLog
What is Data Science?
What is Data Science from Wikipedia Talks a bit of the history as well.
What is data science? – O’Reilly Radar
Data Science Courses and Recipes
Coursera Introduction to Data Science Course
RT @radar: Want to be a data wrangler? School of Data offers free online data science courses
If you are wondering about the applications of Data Science, please watch the first couple of videos from this course
RT @StartupYou: DIY Data Science – when will this happen and think of how big it will be
Data Science Tools: Tools slowly democratize many data science tasks
“Deep Learning – The Biggest Data Science Breakthrough of the Decade” – Free webcast from O’Reilly
Tim O’Reilly – “Data science is transformative. The first wave was marketing analytics, before that financial arbitrage.”
“Mapping Twitter’s Python and Data Science Communities”
Data science and the analytic lifecycle by @bigdata #strataconf
A bitty bundle of data science blogs Collected by @hmason. via @mikeloukides Call for more http://t.co/2iFABfIl2q (look at the comments in the blog for more resources links)
“What’s A ‘Data Scientist’ Anyway? Real-Time With m6d’s Claudia Perlich”
On Ontologies and Knowledge Sharing from the (free) book on Artifical Intelligence – Foundations of Computational Agents:
- a vocabulary of the categories of the things (both classes and properties) that a knowledge base may want to represent;
- an organization of the categories, for example into an inheritance hierarchy using subClassOf or subPropertyOf, or using Aristotelian definitions; and
- a set of axioms restricting the meanings of some of the symbols to better reflect their meaning – for example, that some property is transitive, or that the domain and range are restricted, or that there are some restriction on the number of values a property can take for each individual. Sometimes relationships are defined in terms of more primitive relationships but, ultimately, the relationships are grounded out into primitive relationships that are not actually defined