Well, learning something is quite hard for some people, not some people I think for many people. Getting knowledge is the key process of learning. if we want to achieve something, learning helps to make that process very easy.
We deal with a lot of stuff every day that require knowledge, skills, hard work.
but wait how we get that??? "books" I mean seriously 'books' ??
I have to study a whole lot of book?? Dude seriously who have so much time?
but wait a minute? the answer is yes obviously.
Sounds crazy but that's the truth. Books have a great meaning in delivering a vast knowledge that is why we read everything from books in schools, colleges, blah blah...
Coming back to learning, it can be in any field like learning programming languages, learning maths, learning chemistry, learning physics, even learning rocket science...
We all do learning in some or other way. That's how we educate ourselves.
but what if this process becomes very simple that it's a fun way process rather than a boring process.
Well, there was a guy named Richard Phillips Feynman.
he was an American theoretical physicist, known for his work in the path integral formulation of quantum mechanics, the theory of quantum electrodynamics, and the physics of the superfluidity of supercooled liquid helium, as well as in particle physics for which he proposed the parton model. For his contributions to the development of quantum electrodynamics, Feynman, jointly with Julian Schwinger and Shin'ichirō Tomonaga, received the Nobel Prize in Physics in 1965.
The point here is not telling the history of Richard Feynman but the real point is to highlight his idea behind the way of studying or learning something. So Feynman proposed a technique called "The Feynman Technique".
The purpose of Learning is completed only when you understand that thing completely. And also if you can explain it to someone simply.
A quote by Albert Einstein here is perfect to mention:
If you can't explain it simply, you don't understand it well enough.
The Feynman technique is a method for learning or reviewing a concept quickly by explaining it in plain, simple language.
The first principle is that you must not fool yourself and you are the easiest person to fool.
So the Feynman technique is explained in 4 steps, follow these steps blindly:
1. identify the topic :
write down the name of the concept on top of a blank paper.
2. identify relevant points :
write down everything you know about the topic, and consistently add new points to it, each time when you know something new about that topic. write it in simple plain words. "don't use jargons or difficult vocabulary".
3. teach it to a child :
if you can teach a concept to a child, then you can teach it to anyone, that means you have a better understanding of that topic.
4. identify gaps:
So what you are missing? and what you don't know?? just try to fill that gap.
5. review + simplify:
review your concept and check all the shortcomings, and try to make it even more simple. remove any ambiguity, mistakes in the concept.
Conclusion:
so this way you make the learning process easier and simpler. I know maybe you're thinking that what is the rocket science in this process, I mean we all know this. but do we really follow this approach???
just when we decide to make a change in our habit, the thing also gets change. just try it once...
Please share this post with school and college students, your children, friends and family. maybe it will help them to earn more marks and maybe it can change there learning style.. go ahead... Good Luck!!!
The first example of Data Mining and Business Intelligence comes from service providers in the mobile phone and utilities industries. Mobile phone and utilities companies use Data Mining and Business Intelligence to predict ‘churn’, the terms they use for when a customer leaves their company to get their phone/gas/broadband from another provider. They collate billing information, customer services interactions, website visits and other metrics to give each customer a probability score, then target offers and incentives to customers whom they perceive to be at a higher risk of churning.
Another example of Data Mining and Business Intelligence comes from the retail sector. Retailers segment customers into ‘Recency, Frequency, Monetary’ (RFM) groups and target marketing and promotions to those different groups. A customer who spends little but often and last did so recently will be handled differently to a customer who spent big but only once, and also some time ago. The former may receive a loyalty, upsell and cross-sell offers, whereas the latter may be offered a win-back deal, for instance.
Perhaps some of the most well -known examples of Data Mining and Analytics come from E-commerce sites. Many E-commerce companies use Data Mining and Business Intelligence to offer cross-sells and up-sells through their websites. One of the most famous of these is, of course, Amazon, who use sophisticated mining techniques to drive their, ‘People who viewed that product, also liked this’ functionality.
Supermarkets provide another good example of Data Mining and Business Intelligence in action. Famously, supermarket loyalty card programmes are usually driven mostly, if not solely, by the desire to gather comprehensive data about customers for use in data mining. One notable recent example of this was with the US retailer Target. As part of its Data Mining programme, the company developed rules to predict if their shoppers were likely to be pregnant. By looking at the contents of their customers’ shopping baskets, they could spot customers who they thought were likely to be expecting and begin targeting promotions for nappies (diapers), cotton wool and so on. The prediction was so accurate that Target made the news by sending promotional coupons to families who did not yet realise (or who had not yet announced) they were pregnant!
The use of Data Mining and Business Intelligence is not solely reserved for corporate applications and this is shown in our final example. Beyond corporate applications, crime prevention agencies use analytics and Data Mining to spot trends across myriads of data – helping with everything from where to deploy police manpower (where is crime most likely to happen and when?), who to search at a border crossing (based on age/type of vehicle, number/age of occupants, border crossing history) and even which intelligence to take seriously in counter-terrorism activities.
