What differentiates data science from statistics is that data science is a holistic approach. We’re increasingly finding data in the wild, and data scientists are involved with gathering data, massaging it into a tractable form, making it tell its story, and presenting that story to others.
The biggest challenge most web applications face is building a significant path to customers. Rather than going through customer discovery, customer validation, and then tackle customer creation, which can take months, I find it critical to start building and testing “significant enough” paths to customers much sooner.
You may not be able to apply others’ lessons to your business. But you can always use these lessons to spur thinking. Does this lesson apply to me? How? Can I abstract it in some way to guide my own journey?
the limit on how many cores I can use depends on the application and how much of it I can express in parallel.
The 48-core SCC processor could theoretically scale to 1,000 cores, according to Mattson. Credit: Intel
It turns out that getting S below one percent can be very hard. For algorithms with huge amounts of “embarrassingly parallel” operations, such as graphics, this can be straightforward. For more complex applications, it can be prohibitively difficult.
A big part of the problem lies with companies themselves, which remain trapped in an outdated approach to value creation that has emerged over the past few decades. They continue to view value creation narrowly, optimizing short-term financial performance in a bubble while missing the most important customer needs and ignoring the broader influences that determine their longer-term success. How else could companies overlook the well-being of their customers, the depletion of natural resources vital to their businesses, the viability of key suppliers, or the economic distress of the communities in which they produce and sell? How else could companies think that simply shifting activities to locations with ever lower wages was a sustainable “solution” to competitive challenges?
What are some of the significant emerging technologies to pay attention to?
Here are some answers I added (to my question)
1.Social Web – social networks and social aspects of apps
2. Mobile Apps on Smartphones
3. Games – especially Learning Games and Social games
5. Multi-core and Parallel Programming
6. Next evolution of the web (you can call it semantic web, web 3.0 or whatever name you want to call it)
7. Sensors and sensor based networking
8. Cloud Computing – especially platform as a service
9. Everything Data – Big Data, Open Linked Data, Data Analytics, Visualization, Data mining and Machine Learning, Data Science
10. Internet of things
11. Evolution of collaboration tools including semantic extensions to Wikis
12. Internet Operating system (or Cloud OS)
13. Next generation languages that let you build web applications faster, cheaper
14. Touch and gestural interfaces
15. Evolution in databases including NoSQL
16. Web Query Languages
17. Location based computing
18. Government Apps – apps working on Gov data and helping both governments and people
19. Web Intelligence
Waiting for more answers on Quora.
In the future, in much the same way that algebra was shaken by the discovery of calculus by Newton or Liebnitz, Martin hints to his audience that they have an opportunity to transform everything about the way we think about programming.
Thanks to @nethi for sharing this on Facebook.
I was listening to this talk in the morning and smiled when I heard a reference to PDP-8. That was the first computer I worked on (actually a clone called TDC-12) for a few months before switching to TDC-16, a PDP-11 clone. All we had was machine language – not even an assembler (which came later).
I never learned LISP or Smalltalk and regret it a lot (need to make up for it soon). My life went from Machine Language (where we coded in octal/binary) to Assembler to Basic to Cobol, C and VB. After a long gap, now I am dabbling a bit in Python (mostly exploring but no serious coding). It would be fun to try out Clojure.
If you are interested in tracking languages you may also want to look at TiOBE index and how they compute ranking http://bit.ly/2m2r0D
Milk is a machine learning toolkit in Python.
Its focus is on supervised classification with several classifiers available: SVMs (based on libsvm), k-NN, random forests, decision trees. It also performs feature selection. These classifiers can be combined in many ways to form different classification systems.
Researching machine learning toolkits and libraries in Python.
Does the internet favor dictators or dissenters? Issue1 #86735
We have mapped this YouTube debate as a first step in a long term project to build a comprehensive map of the impact of the web and related technologies on the open society. It serves as a discussion starter – a seeding debate – that allows a quick canvass of the some of the main issues.
It is a fascinating combination of debategraph and deep linking of YouTube video. This is just the beginning of a wonderful world of debates and story telling combined.
Here is a scenario I would like to play in your mind.
Let us collect a set of videos on a specific topic. Let us create a debategraph that deep links various videos. Let people pitch in and add their arguments. We will be building an amazing knowledge base that combines some of the best resources (free) on the internet.