- Start the day with yoga or a fitness routine http://www.youtube.com/watch?v=xEyyu7kk0ZI
- Eat Healthy (hopefully using a nutrition chart printed out and make your food, but if not eat out at a nearby place. If this is too complicated just eat a lot of salad, water and tea)
- Lets get some brain exercises to improve memory and cognition with Lumosity or something else . Most people end up with mobile games ( They may not be the best brain exercise games)
- Plan your day using a notepad and pencil (less complicated) on what you want to do
- DO THE DAY JOB THAT PAYS FOR COFFEE FOR 8 hours a DAY
- Skill up
- Github – contibute every day and be seen
- Linkedin – promote yourself once by a SEO profile
- Twitter – #mybrand #myexpertise once a week
- Wind down for the day using yoga or relaxation music
- Meet people for dinner
- Do until
When overworked analysts use shortcuts to search huge noisy dirty databases, they create trails which can be mined for actual heuristics
A heuristic technique (/hjᵿˈrɪstᵻk/; Ancient Greek: εὑρίσκω, “find” or “discover”), often called simply a heuristic, is any approach to problem solving, learning, or discovery that employs a practical method not guaranteed to be optimal or perfect, but sufficient for the immediate goals. Where finding an optimal solution is impossible or impractical, heuristic methods can be used to speed up the process of finding a satisfactory solution
Example- A Police chief in Chicago may adopt different heuristics than in New York than in New Orleans for allocating human resources
Solution- Make a database of heuristics as actually in practice for that particular domain
Additional Solution- Search Companies to partner not just in giving data but also training and in some case search algorithms for database analysis and database design reviews of Homeland Security
Occam’s razor (also written as Ockham’s razor, and lex parsimoniae in Latin, which means law of parsimony) is a problem-solving principle attributed to William of Ockham (c. 1287–1347), who was an English Franciscan friar, scholastic philosopher and theologian. The principle can be interpreted as stating Among competing hypotheses, the one with the fewest assumptions should be selected.
Related – How to amplify noise in social media using other algorithms
Despite the plethora of data generated in Sports, there is not much open data for Olympics and one wonders why if sharing best practices and data openly on what works and what does not can reduce the level of Russian athletes being banned in a cylical cold war era game.
Some links I found useful
Could data mining techniques accurately predict the medal counts at the Olympics? A predictive model could give us an estimate of the number of medals each nation might win; but how close could we get to the actual outcomes? It was a tantalizing project …
• Which nation will bring home the most medals at the upcoming Winter Olympics in Sochi, Russia?
• Will any nation from Africa, South America, or the Middle East finally break through and win a medal?
• Why do some nations win a bundle of medals while others win only a few?
• Can data mining give us the answers to these questions?
the Graettinger brothers do? They used a seemingly standard methodology: learn from the past to predict the future. More precisely, they used past Olympics results to build a predictive model. Each country is represented by a feature vector, i.e. a set of quantities drawn form several categories:
- Human Development
- Politics and Freedom
Then they used a standard technique known as linear regression to find which set of features were best for predicting medal count. I was reading their blog post with great interest until I saw what were the most meaningful features found by the linear regression algorithm:
- Geographic area
- GDP per capita
- Value of Exports
- Latitude of Nation’s Capital
I was able to find data in many categories:
- Human Development
- Politics and Freedom
Thankfully, there were some good sources out there[f3], and I collected enough data that I felt I had a good chance to predict some meaningful outcomes. But would it be enough? There is more than one way to go about predicting the medal count at the Olympics, and the route before me was the “30,000 feet” approach.
So any takers?
Hackers for Hacking the Olympics 🙂
An uncomfortable fact that policy makers and intelligence analysts do not want to confront is that lack of integration and disillusionment is caused by inbred racism in Western societies to non-conformance of the Caucasian or Judeo-Christian mould. Thanks to regulation, explicit racism is banned, but implicit racism exists and is enabled by both economics as well as technology. Unless you confront racism inherent in some societies or geographies, you will be doing post mortems on events rather than pre-emptive cures. Why does India have much lower cases of home grown terror with 150 million Muslims. It is because they fit well here. Muslim males are not fitting well in Florida or California or on the French Riviera. The golden age of surveillance and the cooperation between technology service providers and government agencies cannot solve the problems of lack of integration due to racism.
Motivating students for online education is a dilemma. Students come from diverse cultures with different levels of communication, hierarchy, expectations. Exit barriers to dropping out also make some students drop out too easily by giving up since the course is free or at a nominal price. I believe one way to motivate students is to keep them involved by constant quizzes, feedback mechanisms as well as ensure how to maximize the rate of knowledge gained by student per hour invested. Time is a key investment by a student. Unfortunately one of the reasons of very good content by many MOOCs still continues to have a high dropout rate is they focus on revenue (verified certificates for a small price), and content (better projects and industry interaction) and course breadth (more courses or bundling them into a specialization) than the key underlying principle of motivating students for a global audience
Abstract The advent of massive open online courses (MOOCs) poses new learning opportunities for learners as well as challenges for researchers and designers. MOOC students approach MOOCs in a range of fashions, based on their learning goals and preferred approaches, which creates new opportunities for learners but makes it difficult for researchers to figure out what a student’s behavior means, and makes it difficult for designers to develop MOOCs appropriate for all of their learners. Towards better understanding the learners who take MOOCs, we conduct a survey of MOOC learners’ motivations and correlate it to which students complete the course according to the pace set by the instructor/platform (which necessitates having the goal of completing the course, as well as succeeding in that goal). The results showed that course completers tend to be more interested in the course content, whereas non-completers tend to be more interested in MOOCs as a type of learning experience. Contrary to initial hypotheses, however, no substantial differences in mastery-goal orientation or general academic efficacy were observed between completers and non-completers. However, students who complete the course tend to have more self-efficacy for their ability to complete the course, from the beginning.
Massive Open Online Courses (MOOCs) have recently experienced rapid development and garnered significant attention from various populations. Despite the wide recognition of MOOCs as an important opportunity within educational practices, there are still many questions as to how we might satisfy students’ needs, as evidenced by very high dropout rates. Researchers lack a solid understanding of what student needs are being addressed by MOOCs, and how well MOOCs now address (or fail to address) these needs. To help in building such an understanding, we conducted in-depth interviews probing student motivations, learning perceptions and experiences towards MOOCs, paying special attention to the MOOC affordances and experiences that might lead to high drop rates. Our study identified learning motivations, learning patterns, and a number of factors that appear to influence student retention. We proposed that the issue of retention should be addressed from two perspectives: retention as a problem but also retention as an opportunity.