Electronic signature Presentation for Administrative Myself
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FAQs
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Why haven't we sent a robotic rock/soil sample return mission to Mars yet?
Money. Commitment. Confidence. Lack of those three.It is currently the highest priority planetary science objective, but it is expensive and risky. There have been plans since the 60's for a Mars Sample Return (MSR) mission, as it's called, but nothing has launched. It got close by 1999 with an MSR project underway with planned launches in 2003 and 2005, but that incarnation was cancelled after the MCO and MPL failures in 1999. Those failures demolished our confidence in taking on the complexity of MSR. We have since rebuilt our capability and confidence in operations at Mars with several successful missions, so at least that part should not be standing in the way of MSR. What's left is the money and commitment. It's a lot of money, and NASA would have to remain committed to the objective over a few administrations.We may be on our way to clearing those hurdles. The first leg of MSR is now a pretty serious project, having just completed its Preliminary Design Review. It is called Mars 2020 (launching in 2020, hence the name). Mars 2020 will send a large Curiosity-class rover to select and acquire rock cores and soil samples for later return. Two follow-on missions in, hopefully, the 2020's will launch those samples into Mars orbit and then bring those samples from Mars orbit to Earth. If all goes well, we could have selected samples from Mars on Earth by the end of the next decade.
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How do you build stamina for working long hours?
Before I answer this question, I want to start by pointing one thing out…You’re not supposed to work long hours!According to all of the best research, after 35 hours of work, your weekly productivity will begin to declineThis means that if you were working at a level 8–10 for the first 35 hours of the week, you’ll be working at a level 5 or below for hours 35+.While there are many people like Gary Vaynerchuk who seem to be able to bend reality to their will and work insane hours for extended periods of time, the simple fact of the matter is this:You only have so much mental energy to expend in a week.You cannot be operating at 100% for 60, 70, or 80 hours a week. It’s just not possible.And if you try… Then you are going to wear yourself out and lower the quality of the other hours that you work.It’s far better to work 35 or fewer hours per week where you are hyper focused and productive than it is to work 60 where you are burnt out and frustrated.But I digress…If you’re anything like I was 2–3 years ago, you’re going to read this and completely ignore it, and that’s fine. Most people have to experience things firsthand before they will really believe them.So with that disclaimer out of the way, here are a few tips and tricks for increasing your energy, focus, and productivity so that you can work long hours effectively.(Note: Some of this content is pulled from my Ultimate Guide to Limitless Productivity where I share the most scientifically validated ways to increase your productivity and get more done in less time)1. Sleep at Least 7.5 Hours Each NightIf you want to have the stamina to work long hours, then you need to make sure that your body and mind are fully rested and equipped to handle an arduous work load.According to Medical Daily, 40.6 million Americans, more than 30% of the workforce, are chronically sleep deprived.Now, at first glance, you might think, “C’mon Andrew, who gives a crap? You can sleep when you’re dead, these people just need to work more!”But when you consider that sleep deprivation has been linked to:DepressionDeliriumHallucinationsImpaired Cognition and an Increased Risk of Preventable AccidentsNot to mention, an increased risk of infections, cancer, and overall mortality.You begin to realize that sleep deprivation is a big deal.Like a really big deal.I know that most young people love the #hustle mentality and believe that there is something noble or productive in sleep deprivation.But nothing could be further from the truth.Unless you have the rare genetic mutation, DEC2, (present in less than 5% of the population) sleeping less than 6 hours a night is literally killing you.And the worst part?You aren’t even aware that it’s happening.Now, let’s consider the positive effects that studies have shown to be present when an individual gets sufficient sleep.Improved memoryLower systemic inflammationImproved immune functionElevated moodLearning and problem-solving abilities improvedI don’t know about you, but I personally believe that having a better memory, experiencing less illness, feeling happier, learning faster, and solving problems more rapidly all strongly correlate to being more productive.This isn’t just my opinion either. In fact, some of the world’s top performers report sleeping more than 8.5 hours a night.Podcast Guest, Neil Patel sleeps 9.25 hours a night while running three multi-million dollar businesses.Arianna Huffington, Co-founder of the Huffington Post and multimillionaire claims that sleeping 8 hours a night was partially responsible for her success.James Altucher, multi-millionaire investor, and hedge fund manager includes “Sleeping 8 Hours” as one of the keys to his financial successAt this point, it should be clear that getting more sleep is, indeed, one of the quickest ways to boost your productivity.But the question still remains, “How much sleep do I need, and how can I get better quality sleep?”According to the National Sleep Foundation, adults need 7-9 hours in order to prevent the effects of sleep deprivation from affecting your life and productivity. (slightly more if you’re an avid athlete)As for increasing the quality of your sleep, it’s actually pretty simple.Go to bed before 11 p.m.Wake up at the same time each daySleep in a completely dark and cold room (research shows that 65-67 F is ideal for sleep)Exercise dailyTurn off all electronics 60 minutes before bed.I know that this particular section was a little bit long-winded, but this point is so important that I couldn’t simply breeze through it.If you want to be more productive, you need to sleep. Period.Until you are getting 7.5-9 hours of sleep on a consistent basis, the other tactics included in this guide will simply fan the flames of burnout until, eventually, you collapse in a stressed out, sleep deprived panic attack.Take it from me (and thousands of scholarly studies), quit trying to join the sleepless elite and get your 7 hours. M’kay?2. Sweat for At Least 20 Minutes a DayStudy after study after study has illustrated the tremendous importance of daily exercise.From:Decreased depressionElevated mood, reduced stress, and less anxietyImproved blood flow to the brainThe production of new brain cellsImproves memoryImproved discipline, impulse control, and decision makingIn fact, there are SO many benefits to exercise, that the Harvard Business Review has stated that regular exercise should be a mandatory part of any job description.Luckily, studies have shown that you don’t have to exercise for hours every day to reap these benefits.In fact, just 150 minutes of weekly exercise (that’s 30 minutes every weekday) is more than sufficient to improve your productivity, mood, and general well being.If you are exercising exclusively for increased productivity, studies have shown that 2-3 moderately intense sessions of aerobic exercise each week will have the most dramatic impact on your ability to focus and concentrate.However, this does not mean that you should exclusively train your aerobic capacity.Further research has indicated that combining regular aerobic conditioning with an intelligent weightlifting regimen (I recommend this one) and regular yoga will have the greatest impact on your ability to be more productive and stay focused throughout the day.3. Eat Clean Burning Foods and Reduce Your Carb Load Early in the DayMost people underestimate the effect that your diet has on cognitive performance and general productivity.Think about it this way…Your brain is the center of all productivity.Although that tiny little supercomputer takes up only 2-3% of the total mass of your body, it burns more than 20% of the calories that you consume!In and of itself, this should clearly illustrate the link between food and productivity.Studies from the Harvard Business Review have shown an inextricable link between the calories that you consume and the ability for your brain to focus and achieve long-lasting concentration.I won’t bore you with all of the science, but I will suffice it to say that what you eat matters… A lot.If you want to be as productive as possible, you will want to clean up your diet.Here are a few guidelines to get you started.Eliminate as many processed foods as possibleConsume slow burning foods such as raw vegetables and fibrous carbohydrates throughout the day to properly regulate glucose levels in the brainConsume your biggest and highest carb meal after your workout or at dinnerSkip breakfast and opt for coconut oil coffee or eat a very protein and fat rich breakfast (no carbs!)Although you can dive much much deeper into the world of productivity and focus through dieting, simply eliminating processed foods, increasing the number of vegetables you eat, and waiting until later in the day to consume carbs will dramatically improve your productivity almost overnight.If you are interested in learning more about how your dietary choices and productivity are related, check out this awesome infographic from Hubspot.4. Bring the JoyAlthough it might seem like common sense, happy and excited people are more productive.How much more productive?Well, according to a study compiled by Professor Andrew Oswald, Dr. Eugenio Proto and Dr. Daniel Sgroi from the Department of Economics at the University of Warwick, happy employees are 12% more productive than their unhappy peers!I don’t have time to dive into all of the amazing research that has been compiled in recent years that details what determines human happiness, (you should check out the Happiness Advantage by Shawn Achor if you’re interested in this), I want to share a quick tactic I picked up from Brendon Burchard.The tactic, called “Bring the Joy” is simple enough, but the results you will experience are profound.All I want you to do is to set 3 alarm on your phone titled Bring the Joy.Set them to go off at different times throughout the day and, when you see the notification pop up on your screen, I want you to ask yourself three questions.What level of joy and presence am I bringing to this present moment?What am I grateful for today?How can I bring more joy and excitement into my current interactions and activities?Like I said, simple right?I challenge you to try this tactic for the next 30 days and genuinely pause and become aware of your state every time your alarm goes off.You will be amazed at how much more productive and joyful your life will become.5. Meditate for at Least 10 Minutes a DayAlthough the scientific community needs to further evaluated the direct link between meditation and productivity, several studies like this one, conducted at a Fortune 100 company, show a very clear link between a regular meditation practice and increased productivity at work.The reason for this is simple.Meditation is proven to help: (source)Lower blood pressureAlleviate symptoms of insomniaReduce depression and anxietyReduce painReduce symptoms of IBSAid in smoking cessationOh, did I mention that it has also been shown to rebuild grey matter?As I’ve already discussed, happiness and productivity are inextricably linked and it should be pretty clear that any practice which decreases depression, anxiety, and sleeplessness will, by default, improve your productive output.I challenge you to take up a meditation practice for the next 30 days and record how you feel.Arnold Schwarzenegger, the infamous bodybuilder, real estate tycoon, and “Governator” of California stated that his one year of intense TM (Transcendental Meditation) practice has created results that6. Take Strategic Breaks Throughout the Day to Maintain Your Energy and EnthusiasmOne of the most surprising ways to increase your energy and boost productivity is actually to work less and take breaks more frequently.Study after study has shown that the human brain cannot focus (effectively) for more than 90 minutes.Eventually, your brain needs a break from any given task to consolidate and process information, renew our focus, and ensure that our tasks are ultimately congruent with our goals.Later in this article, I’ll discuss the Pomodoro method which helps cement these findings into your daily workflow.But for now, I simply want to encourage you to start taking a 45-60 minute break in the middle of every workday.During these breaks, I recommend that you:Practice meditationWalk outsideEat a light snackDo some calisthenicsReadTalk with friendsTest out different methods of recharging yourself and renewing your focus throughout the day and it will pay dividends in the long run.7. Eliminate Email as Much as PossibleNothing will drain your focus and stamina more than wasting nearly 30% of your work week responding to emails!In the United States alone, the average employee spends more than 28% of their time or 13 hours a week responding to emails.That’s more than 650 hours a year wasted on largely unproductive, reactive, and unnecessary correspondence!Over the average employee’s lifespan (45 working years) that equates to more than 29250 hours wasted on email.For those of you who are quick with a calculator, this means that the average employee will spend 3 years of their life responding to and clearing out emails!That’s a jaw-dropping amount of time to spend on such an insignNow and largely unimportant task as email.So what in the hell are we going to do about it?Although entire books have been written on the topic of reducing email overload and reclaiming your inbox (and your life) I will keep things simple.I recommend that you:Check email only twice a day (I do it at 10 a.m. and 4 p.m.)“Touch it once”. Either respond to, archive or delete an email. Never leave it in your inboxStop using email folders and simply search for emails when you need themKeep your emails to 5 sentences or less and inform people of this policy in your signature (shoutout to Chris Bailey for this one)Go on an email vacation and let co-workers know you won’t be responding to email until you are done with your biggest project (they will survive I promise)If you do nothing other than implementing these five tips your productivity will skyrocket.Imagine if you could reduce the amount of time you spend on email to only one hour a week.How much more could you accomplish with 12 extra hours in your work week?How much income could you create? How many promotions could you secure?The more you think about it, the more you will realize that email is the scourge of productivity and, although it is a necessary evil, it is an evil nonetheless.8. Embrace the Power of “No”The most powerful word in the entire English language is composed of only two letters.“No”The word “No” has started wars, ended wars, overthrown oppressive governments, and, as it pertains to our conversation, revolutionized personal productivity and fulfillment for people all over the world.Just think about it for a moment.How much pain, discomfort, and genuine wasted time have you experienced in your life because you said “Yes” when you should have said “No”?How many times have you spent an afternoon with people that you didn’t like, in a setting that made you uncomfortable, for a purpose you couldn’t ascertain simply because you didn’t have the courage to say “No”?If you are anything like me, the answer is probably “A lot”.I know from first-hand experience that implementing the power of “No” into your life can be very challenging.For years, I was a chronic people pleaser.I would go to parties I didn’t want to attend, stay late at work, go on dates with people I didn’t care for, and generally lived my life for the approval of others instead of my own personal satisfaction.Until one day, I had enough.I was burnt out, stressed out, wallowing in unfinished projects, unmet personal expectations, and general angst about my existence.So I decided to say “No” more often.I said no:When family members wanted to hang out during my workdaysWhen audience members asked to take me to lunch (I love you guys but I literally cannot meet with 30,000+ men 1-on-1)When friends wanted to go out and I didn’tWhen people made unfair requests of me and my timeWhen people asked for unwarranted favors because they were “My friend”I said “No” to the bad and even the good so that I could say “Yes” to the great.And if you want to be as productive as possible and create a truly prolific life, then you must learn to do the same.9. Use the Pomodoro TechniqueRemember how we talked about the importance of taking breaks way back in Path #1 (yeah, I know this is a long ass article)?Well, it turns out that taking breaks every 50-90 minutes can be just as effective at increasing your productivity and focus throughout the day as the 45-minute renewal exercise we already discussed.The reason for this lies in something called the Ultradian Rhythm.Effectively our brain waves are cyclical and go through peaks and troughs roughly every 90 minutes.In the same way that your brain cycles through different wavelengths during a 90-minute sleep cycle, so too does your brain cycle through wavelengths in a “basic rest-activity cycle”.If you are interested in learning more about the science, you can check out this article from Tony Schwartz.Knowing that cognitive output is cyclical, meaning that you physically cannot sustain high levels of concentration without intermittent periods of rest, changes the entire approach to productivity and focus.This is where the Pomodoro technique comes in handy.Instead of fighting against your Ultradian Rhythm, the Pomodoro technique works with it.Here’s what you do.Instead of simply sitting down at your desk to work, you are going to pick one of your most important tasks of the day (which I will talk about in the next point) and focus on it for a definite length of time between 25 and 90 minutes.Then, you are going to set a timer, eliminate all distractions, and get to work on that project with single-focus until the timer goes off.When the timer buzzes, you are going to take a break anywhere from 5-22 minutes (depending on the length of your work session) before sitting back down to begin the process all over again.All you need to complete the Pomodoro Technique is:A physical or online timerSomething to work onYour brainIt really is that simple.I’ve tested this tactic out for myself and have noticed that I am consistently more productive, more efficient, and more happy with my output when I use the Pomodoro technique on a regular basis.10. Create Locational Anchors to Build Productive StatesAn underground tactic that I’ve found to be immensely effective in recent months is the use of locational anchors.This concept was first introduced to me when I listened to an excellent podcast with Jairek Robbins.Jairek discussed the concept of locational anchors by explaining that the brain works through the power of association and that, the more associations we can build for a specific task, the easier it will be to accomplish it.This is why doctors tell you to only use your bed for sleep and sex.You want to make sure that when it’s time to unwind or *ahem* perform, that your body and brain associate your bed with those activities.This is also why it’s so much easier to have an awesome workout at your local gym than it is with an Iron Gym in your living room.However, Jairek took things a step further and recommended that you actually develop locational anchors for ALL major tasks that you must complete throughout the day.For example:Check email at the kitchen tableTake conference calls at your local cafeWrite at your desk while looking out the windowDesign sales funnels at your desk with your back to the windowComplete all administrative work at a specific nook in your houseThe list goes on and on.Unfortunately, I couldn’t find any direct research to back up this particular hack, however, after experiencing its effectiveness first hand, I couldn’t leave it off this list.Give it a go for 90 days and I promise you will get more done than you ever believed possible.Final Thoughts: Take it Easy on Yourself!Before I leave you to take on the big bad world of getting sh*t done and becoming a productivity machine, I wanted to leave you with one final tip.Take it easy on yourself.The most unproductive thing you can do is to berate and belittle yourself because you haven’t been as successful or productive as you want.Yes, it’s important that you are honest with yourself and your clients and don’t sugarcoat the reality of your current capacity.However, you must remember that you were never taught this in school. You weren’t born with the knowledge of how to be massively productive.You’ve simply been operating on whatever systems you picked up from the people around you and, hopefully, you now have better systems to test and implement.Productivity and focus are both acquired skillsets.You aren’t born productive and it’s not something that is determined by your genetics. It’s a matter of principles and systems, testing, failing, and figuring out what works for you.So take it easy on yourself as you strive to get more done.The journey will take time, but it will be worth it.Good luck!Closeout this article and go get some shit done!
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How shall I prepare for IBPS PO 2016 interview?
Interview is the most random thing happening with you in the actual exam. Randomness comes on the date of the interview, randomness comes on the board of the interview, randomness comes on the time of the interview. You may be the first candidates of the after noon session or you may be the last canididate of the last session. Hence its became random. The most important thing here is “How to start and take a flow of your preparation for taking selecting in this exam.” I’ll guide you here step by step. Do as I say and believe me if you do this correctly you’ll definitely got success in this examination which is seems to be wild goose chase and most of the aspirants were afraid of facing the interview memebers. So let us take a start.First of all you need to know about yourself. Here yourself means know about your likes and dislikes/know about your strong and week points regarding your graduation subjects/know about your habits- what do you want to read in your free times, what is the thing which excites you the most. These are thing in which me or anybody can’t help you. You have to required all this questions answer in your own language. Hence first try to know more about yourself in a well mannered. Don’t cheat yourself here. If there are an interviewed person who is romantic and ask you “What are the things you want to be in your life partner/boyfriend.” then tell them about it honestly. Here I don’t write girlfriend because this question they never ask to any boy, this question only they ask to girls. This question was ask in IBPS clerk 2015 to one of my friends. She is beautiful hence one of the member asked her. If you are goodlooking then must be answered her without shocking. So, that is the thing. you have to relate directly relate to the board persons mind. This is the first thing you should do know more about yourself.The Second thing you have to do is “Try to be honest”. If you done something wrong in life and they ask you about that give them answer that yes you have. Everyone not going to be Mother teressa. They don’t want Mahatma gandhi as a Assistant manager. We all are human and human being required their.The Third thing you have to do is “Try to collect well mannered answer regarding Banking/financial awareness.” Please well arranged yourself for present norm of banks. What is FDI. what is FII, Give me a breif history about the banking in India, How its arranged, what do you do for the people who is fighting with Demonetisation if you are a RBI governor, Who is Urgit Patel, how many governor were there, what is GDP, what is GNP, what is CLR and SLR, How many governor are there, what is the role of governor in bank, what are their responsibilities, What is the difference between American and Indian banks, What is paytm, how it works, Is go to cashless scheme of PM is good, Is everyone was able to connect with cashless, Is rural people are well aranged themselves for the cashless system, How the concept of cashless system arise, what is Demonetisation, Is this good then why, Is this bad then why, What is black money, Do you have black money, If you have black money then how do you tackle with this problem in that time, How black money afftect the whole country, What is Mat, What is SEBI, How Indian banks compete with themselves, Public sector and private banks -what are it and how many it.” These are the thing you should prepare if you want to make interview better than someone else. The best thing is all the things I mentioned here is easy to remember and easy to read. How, I would tell you here.You have “Lucent” right. Economic section of this book is awsome. Learn economic basics from there and make a note on that. Write in that note book all the essentials imfromation which you think is best regading your interview. This will not take your more than one day. After that you should start using internet in the best way. Search all th topics I would mention here and try to understand it well. It will help you a lot. dont buy more books for your interview preparation after reading economic section think your self about the best topic which is essentials and googling things from it. try to read as much as you can. That all you would do for the preparation of your interview. Further I’ll discussed the thing which is important for you for your representation.The fourth thing I’ll discuss here is representation. How you represent yourself in front of the examiner, What is the best dress for wearing on the day of your interview, How you enter in the interview room, how would you sit there, how would you answered the question of examiner, how would you stand up after the exams and how you go out from the room. All thing we’ll discuss here and that is important regarding your interviews.Presentation are the thing which is needed in your interview for your selection. The first thing here is your dress because alll the thing are matters after your dress. First they go through your dress and make something in their mind regarding you. Hence try to wear that dress which suits to you. Girls who are between 21–25 age group, if you slim then Suit is the best option there for your presentation. A simple suit on which not much work are done is best choice for you. Avoid saree as much as you can but if you are healthy then dont wear suit replace it from saree. All 25+ age froup girls must wore sarees because this is the best opiton for you. Except this the thing matter here more is the dress in which you looking preety. Don’t wear plain or very simple not worthconsidering saree because they dont want Mother Teressa as an Assistant manager, she was very good in social reformer. For Saree from choosing to wearing take your mom help for this. All the boys go for the interview must use Simple black pant and white shirts. But not wear simple black pant use Royal black color pants and Shirts are different whites. Don’t use normal black and white because they dont want school students. At least try to look like a banker. This is important in this time. Use a normal black sweater on white shirts. Prefer half sweater most. Try to avoid half sweater. You can also use cream colors sweater but use onle simple among that. Thats all are the dressing thing important for exams.Other important thing here is your preformance. In performance I want to add here the normal quesiton which they ask to many candidates because theses are the relative quesiton and answer of all of that not similar to the interview. Like If you done something wrong in your life and they ask about that then tell them yes you have. Dont try to misguided them. We all are human and human being are required in this examination not any superhuman or other great superheroes. We all do mistakes hence tell them about the thing which is wrong and you did it also tell them about your appoligies about that particular thing or person. Make every answer which is create less questions. Don try to add more thing in your quesiton. There are also some quesition which you need to prepare for the interview like “Tell me about yourself/ tell me about your habits/ which books you read in your free time/ what are the last book you read ( here if you not read any book then tell them honestly that you didnt read any book which is not related to your exams because of x,y and z reasons). If they ask you something and you know the answer of that particular quesiton well then dont try to answered them untill the question was end. When the interview was end then stand up comfortably and tell them thankyou and give back to the door from facing them dont view them your back. Go out from facing them with greeting all of them and then slowly slowly outside. This is the last thing you do which shows them how much you respect them because that was the movie ending time and in the last your merit will be decided from there.That is the all thing you should do regarding your preparation. I’l tell you here everything just go from that. At last I want to say avoid much books and use internet, search all the thing you didn’t know. If there are still some quesitons which is in your mind ask then write a message and drop it on my fb inbox. Whenever I’ve time I’ll answered. But I think I shared here everthing and this will help you a lot.
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What is big data and how do I learn about it?
What is big data and how do I learn about it?Big Data is defined by the three V’s:Volume—large amounts of data;Variety—the data comes in different forms, including traditional databases, images, documents, and complex records;Velocity—the content of the data is constantly changing through the absorption of complementary data collections, the introduction of previously archived data or legacy collections, and from streamed data arriving from multiple sources.It is important to distinguish Big Data from “lots of data” or “massive data.” In a Big Data Resource, all three V’s must apply. It is the size, complexity, and restlessness of Big Data resources that account for the methods by which these resources are designed, operated, and analyzed.The term “lots of data” is often applied to enormous collections of simple-format records. For example every observed star, its magnitude and its location; the name and cell phone number of every person living in the United States; and the contents of the Web.These very large datasets are sometimes just glorified lists. Some “lots of data” collections are spreadsheets (2-dimensional tables of columns and rows), so large that we may never see where they end.Big Data resources are not equivalent to large spreadsheets, and a Big Data resource is never analyzed in its totality. Big Data analysis is a multi-step process whereby data is extracted, filtered, and transformed, with analysis often proceeding in a piecemeal, sometimes recursive, fashion. As you read this book, you will find that the gulf between “lots of data” and Big Data is profound; the two subjects can seldom be discussed productively within the same venue.Big Data Versus Small DataActually, the main function of Big Science is to generate massive amounts of reliable and easily accessible data... Insight, understanding, and scientific progress are generally achieved by ‘small science.’Big Data is not small data that has become bloated to the point that it can no longer fit on a spreadsheet, nor is it a database that happens to be very large. Nonetheless, some professionals who customarily work with relatively small data sets, harbor the false impression that they can apply their spreadsheet and database know-how directly to Big Data resources without attaining new skills or adjusting to new analytic paradigms.As they see things, when the data gets bigger, only the computer must adjust (by getting faster, acquiring more volatile memory, and increasing its storage capabilities); Big Data poses no special problems that a supercomputer could not solve. More information please refer to the Office 2019 Guide.This attitude, which seems to be prevalent among database managers, programmers, and statisticians, is highly counterproductive. It will lead to slow and ineffective software, huge investment losses, bad analyses, and the production of useless and irreversibly defective Big Data resources.Let us look at a few of the general differences that can help distinguish Big Data and small data. – Goals small data—Usually designed to answer a specific question or serve a particular goal. Big Data—Usually designed with a goal in mind, but the goal is flexible and the questions posed are protean.Here is a short, imaginary funding announcement for Big Data grants designed “to combine high-quality data from fisheries, coast guard, commercial shipping, and coastal management agencies for a growing data collection that can be used to support a variety of governmental and commercial management studies in the Lower Peninsula.”In this fictitious case, there is a vague goal, but it is obvious that there really is no way to completely specify what the Big Data resource will contain, how the various types of data held in the resource will be organized, connected to other data resources, or usefully analyzed. Nobody can specify, with any degree of confidence, the ultimate destiny of any Big Data project; it usually comes as a surprise.– Locationsmall data—Typically, contained within one institution, often on one computer, sometimes in one file.Big Data—Spread throughout electronic space and typically parceled onto multiple Internet servers, located anywhere on earth.– Data structure and content small data—Ordinarily contains highly structured data. The data domain is restricted to a single discipline or sub-discipline. The data often comes in the form of uniform records in an ordered spreadsheet.PRINCIPLES AND PRACTICE OF BIG DATABig Data—Must be capable of absorbing unstructured data (e.g., such as free-text documents, images, motion pictures, sound recordings, physical objects). The subject matter of the resource may cross multiple disciplines, and the individual data objects in the resource may link to data contained in other, seemingly unrelated, Big Data resources.– Data preparationsmall data—In many cases, the data user prepares her own data, for her own purposes. Big Data—The data comes from many diverse sources, and it is prepared by many people. The people who use the data are seldom the people who have prepared the data.– Longevitysmall data—When the data project ends, the data is kept for a limited time (seldom longer than 7 years, the traditional academic life-span for research data); and then discarded.Big Data—Big Data projects typically contain data that must be stored in perpetuity. Ideally, the data stored in a Big Data resource will be absorbed into other data resources. Many Big Data projects extend into the future and the past (e.g., legacy data), accruing data prospectively and retrospectively.– Measurementssmall data—Typically, the data is measured using one experimental protocol, and the data can be represented using one set of standard units.Big Data—Many different types of data are delivered in many different electronic formats. Measurements, when present, may be obtained by many different protocols. Verifying the quality of Big Data is one of the most difficult tasks for data managers. [Glossary Data Quality Act]– Reproducibilitysmall data—Projects are typically reproducible. If there is some question about the quality of the data, the reproducibility of the data, or the validity of the conclusions drawn from the data, the entire project can be repeated, yielding a new data set.Big Data—Replication of a Big Data project is seldom feasible. In general, the most that anyone can hope for is that bad data in a Big Data resource will be found and flagged as such.– Stakessmall data—Project costs are limited. Laboratories and institutions can usually recover from the occasional small data failure.Big Data—Big Data projects can be obscenely expensive. A failed Big Data effort can lead to bankruptcy, institutional collapse, mass firings, and the sudden disintegration of all the data held in the resource. As an example, a United States National Institutes of Health Big Data project known as the “NCI cancer biomedical informatics grid” cost at least $350 million for fiscal years 2004–10.An ad hoc committee reviewing the resource found that despite the intense efforts of hundreds of cancer researchers and information specialists, it had accomplished so little and at so great an expense that a project moratorium was called.Soon thereafter, the resource was terminated. Though the costs of failure can be high, in terms of money, time, and labor, Big Data failures may have some redeeming value. Each failed effort lives on as intellectual remnants consumed by the next Big Data effort.– Introspectionsmall data—Individual data points are identified by their row and column location within a spreadsheet or database table. If you know the row and column headers, you can find and specify all of the data points contained within.Big Data—Unless the Big Data resource is exceptionally well designed, the contents and organization of the resource can be inscrutable, even to the data managers. Complete access to data, information about the data values, and information about the organization of the data are achieved through a technique herein referred to as introspection.– Analysissmall data—In most instances, all of the data contained in the data project can be analyzed together, and all at once.Big Data—With few exceptions, such as those conducted on supercomputers or in parallel on multiple computers, Big Data is ordinarily analyzed in incremental steps. The data are extracted, reviewed, reduced, normalized, transformed, visualized, interpreted, and re-analyzed using a collection of specialized methods.Whence Comest Big Data?Often, the impetus for Big Data is entirely ad hoc. Companies and agencies are forced to store and retrieve huge amounts of collected data (whether they want to or not). Generally, Big Data comes into existence through any of several different mechanisms:– An entity has collected a lot of data in the course of its normal activities and seeks to organize the data so that materials can be retrieved, as needed.The Big Data effort is intended to streamline the regular activities of the entity. In this case, the data is just waiting to be used. The entity is not looking to discover anything or to do anything new. It simply wants to use the data to accomplish what it has always been doing;only better. The typical medical center is a good example of an “accidental” Big Data resource. The day-to-day activities of caring for patients and recording data into hospital information systems results in terabytes of collected data, in forms such as laboratory reports, pharmacy orders, clinical encounters, and billing data.Most of this information is generated for one-time specific use (e.g., supporting a clinical decision, collecting payments for a procedure). It occurs to the administrative staff that the collected data can be used, in its totality, to achieve mandated goals: improving the quality of service, increasing staff efficiency, and reducing operational costs.– An entity has collected a lot of data in the course of its normal activities and decides that there are many new activities that could be supported by their data.Consider modern corporations; these entities do not restrict themselves to one manufacturing process or one target audience. They are constantly looking for new opportunities.Their collected data may enable them to develop new products based on the preferences of their loyal customers, to signNow new markets, or to market and distribute items via the Web. These entities will become hybrid Big Data/manufacturing enterprises.– An entity plans a business model based on a Big Data resource.Unlike the previous examples, this entity starts with Big Data and adds a physical component secondarily. Amazon and FedEx may fall into this category, as they began with a plan for providing a data-intense service (e.g., the Amazon Web catalog and the FedEx package tracking system).The traditional tasks of warehousing, inventory, pick-up, and delivery had been available all along but lacked the novelty and efficiency afforded by Big Data.– An entity is part of a group of entities that have large data resources, all of whom understand that it would be to their mutual advantage to federate their data resources.An example of a federated Big Data resource would be hospital databases that share electronic medical health records.– An entity with skills and vision develops a project wherein large amounts of data are collected and organized, to the benefit of themselves and their user-clients.An example would be a massive online library service, such as the U.S. National Library of Medicine’s PubMed catalog, or the Google Books collection.– An entity has no data and has no particular expertise in Big Data technologies, but it has money and vision.The entity seeks to fund and coordinate a group of data creators and data holders, who will build a Big Data resource that can be used by others. Government agencies have been the major benefactors. These Big Data projects are justified if they lead to important discoveries that could not be attained at a lesser cost with smaller data resources.The Most Common Purpose of Big Data Is to Produce Small DataIf I had known what it would be like to have it all, I might have been willing to settle for less.Imagine using a restaurant locater on your smartphone. With a few taps, it lists the Italian restaurants located within a 10-block radius of your current location.The database being queried is big and complex (a map database, a collection of all the restaurants in the world, their longitudes and latitudes, their street addresses, and a set of ratings provided by patrons, updated continuously), but the data that it yields is small (e.g., five restaurants, marked on a street map, with pop-ups indicating their exact address, telephone number, and ratings). Your task comes down to selecting one restaurant from among the five, and dining thereat.In this example, your data selection was drawn from a large data set, but your ultimate analysis was confined to a small data set (i.e., five restaurants meeting your search criteria). The purpose of the Big Data resource was to proffer the small data set. No analytic work was performed on the Big Data resource; just search and retrieval.The real labor of the Big Data resource involved collecting and organizing complex data so that the resource would be ready for your query. Along the way, the data creators had many decisions to make (e.g., Should bars be counted as restaurants? What about takeaway only shops? What data should be collected? How should missing data be handled? How will data be kept current?Big Data is seldom if ever, analyzed in toto. There is almost always a drastic filtering process that reduces Big Data into smaller data. This rule applies to scientific analyses. The Australian Square Kilometre Array of radio telescopes [8], WorldWide Telescope, CERN’s Large Hadron Collider and the Pan-STARRS (Panoramic Survey Telescope and Rapid Response System) array of telescopes produce petabytes of data every day. Researchers use these raw data sources to produce much smaller data sets for analysis [9].Here is an example showing how workable subsets of data are prepared from Big Data resources. Blazars are rare super-massive black holes that release jets of energy that move at near-light speeds. Cosmologists want to know as much as they can about these strange objects. A first step to studying blazars is to locate as many of these objects as possible.Afterward, various measurements on all of the collected blazars can be compared, and their general characteristics can be determined. Blazars seem to have a gamma ray signature that is not present in other celestial objects. The WISE survey collected infrared data on the entire observable universe.Researchers extracted from the Wise data every celestial body associated with an infrared signature in the gamma-ray range that was suggestive of blazars; about 300 objects. Further research on these 300 objects led the researchers to believe that about half were blazars [10]. This is how Big Data research often works; by constructing small data sets that can be productively analyzed.Because a common role of Big Data is to produce small data, a question that data managers must ask themselves is: “Have I prepared my Big Data resource in a manner that helps it become a useful source of small data?”Big Data Sits at the Center of the Research UniverseIn the past, scientists followed a well-trodden path toward truth: hypothesis, then experiment, then data, then analysis, then publication. The manner in which a scientist analyzed his or her data was crucial because other scientists would not have access to the same data and could not re-analyze the data for themselves.Basically, the results and conclusions described in the manuscript was the scientific product. The primary data upon which the results and conclusion were based (other than one or two summarizing tables) were not made available for review. Scientific knowledge was built on trust. Customarily, the data would be held for 7 years, and then discarded.In the Big data paradigm, the concept of a final manuscript has little meaning. Big Data resources are permanent, and the data within the resource is immutable. Any scientist’s analysis of the data does not need to be the final word; another scientist can access and re-analyze the same data over and over again.Original conclusions can be validated or discredited. New conclusions can be developed. The centerpiece of science has moved from the manuscript, whose conclusions are tentative until validated, to the Big Data resource, whose data will be tapped repeatedly to validate old manuscripts and spawn new manuscripts.Today, hundreds or thousands of individuals might contribute to a Big Data resource. The data in the resource might inspire dozens of major scientific projects, hundreds of manuscripts, thousands of analytic efforts, and millions or billions of search and retrieval operations. The Big Data resource has become the central, massive object around which universities, research laboratories, corporations, and federal agencies orbit.These orbiting objects draw information from the Big Data resource, and they use the information to support analytic studies and to publish manuscripts. Because Big Data resources are permanent, any analysis can be critically examined using the same set of data, or re-analyzed anytime in the future. Because Big Data resources are constantly growing forward in time (i.e., accruing new information) and backward in time (i.e., absorbing legacy data sets), the value of the data is constantly increasing.Big Data resources are the stars of the modern information universe. All matter in the physical universe comes from heavy elements created inside stars, from lighter elements.All data in the informational universe is complex data built from simple data. Just as stars can exhaust themselves, explode, or even collapse under their own weight to become black holes; Big Data resources can lose funding and die, release their contents and burst into nothingness, or collapse under their own weight, sucking everything around them into a dark void. It is an interesting metaphor.GlossaryBig Data resource A Big Data collection that is accessible for analysis. Readers should understand that there are collections of Big Data (i.e., data sources that are large, complex, and actively growing) that are not designed to support analysis; hence, not Big Data resources.Such Big Data collections might include some of the older hospital information systems, which were designed to deliver individual patient records upon request; but could not support projects wherein all of the data contained in all of the records were opened for selection and analysis. Aside from privacy and security issues, opening a hospital information system to these kinds of analyses would place enormous computational stress on the systems (i.e., produce system crashes).In the late 1990s and the early 2000s, data warehousing was popular. Large organizations would collect all of the digital information created within their institutions, and these data were stored as Big Data collections, called data warehouses. If an authorized person within the institution needed some specific set of information (e.g., emails sent or received in February 2003; all of the bills paid in November 1999), it could be found somewhere within the warehouse.For the most part, these data warehouses were not true Big Data resources because they were not organized to support a full analysis of all of the contained data. Another type of Big Data collection that may or may not be considered a Big Data resource are compilations of scientific data that are accessible for analysis by private concerns, but closed for analysis by the public.In this case, a scientist may make a discovery based on her analysis of a private Big Data collection, but the research data is not open for critical review. In the opinion of some scientists, including myself, if the results of data analysis are not available for review, then the analysis is illegitimate. Of course, this opinion is not universally shared, and Big Data professionals hold various definitions for a Big Data resource.ConclusionsConclusions are the interpretations made by studying the results of an experiment or a set of observations. The term “results” should never be used interchangeably with the term “conclusions.” Remember, results are verified. Conclusions are validated.Data Quality Act In the United States the data upon which public policy is based must have quality and must be available for review by the public. Simply put, public policy must be based on verifiable data. The Data Quality Act of 2002 requires the Office of Management and Budget to develop government-wide standards for data quality.Data manager This book uses “data manager” as a catchall term, without attaching any specific meaning to the name. Depending on the institutional and cultural milieu, synonyms and plesionyms (i.e., near-synonyms) for data manager would include technical lead, team liaison, data quality manager, chief curator, chief of operations, project manager, group supervisor, and so on.Data resource A collection of data made available for data retrieval. The data can be distributed over servers located anywhere on earth or in space. The resource can be static (i.e., having a fixed set of data), or in flux. Pseudonyms for data resource is a data warehouse, data repository, data archive, and data store.Database A software application designed specifically to create and retrieve large numbers of data records (e.g., millions or billions). The data records of a database are persistent, meaning that the application can be turned off, then on, and all the collected data will be available to the user.Grid A collection of computers and computer resources (typically networked servers) that are coordinated to provide the desired functionality. In the most advanced Grid computing architecture, requests can be broken into computational tasks that are processed in parallel on multiple computers and transparently (from the client’s perspective) assembled and returned. The Grid is the intellectual predecessor of Cloud computing. Cloud computing is less physically and administratively restricted than Grid computing.ImmutabilityImmutability is the principle that data collected in a Big Data resource is permanent and can never be modified. At first thought, it would seem that immutability is a ridiculous and impossible constraint. In the real world, mistakes are made, information changes, and the methods for describing information changes. This is all true, but the astute Big Data manager knows how to accrue informa-tion into data objects without changing the pre-existing data.IntrospectionWell-designed Big Data resources support introspection, a method whereby data objects within the resource can be interrogated to yield their properties, values, and class membership. Through introspection, the relationships among the data objects in the Big Data resource can be examined and the structure of the resource can be determined. Introspection is the method by which a data user can find everything there is to know about a Big Data resource without downloading the complete resource.Large Hadron Collider The Large Hadron Collider is the world’s largest and most powerful particle accelerator and is expected to produce about 15 petabytes (15 million gigabytes) of data annually.Legacy data Data collected by an information system that has been replaced by a newer system, and which cannot be immediately integrated into the newer system’s database. For example, hospitals regularly replace their hospital information systems with new systems that promise greater efficiencies, expanded services, or improved interoperability with other information systems. In many cases, the new system cannot readily integrate the data collected from the older system.The previously collected data becomes a legacy to the new system. In such cases, legacy data is simply “stored” for some arbitrary period of time in case someone actually needs to retrieve any of the legacy data.After a decade or so the hospital may find itself without any staff members who are capable of locating the storage site of the legacy data, or moving the data into a modern operating system, or interpreting the stored data, or retrieving appropriate data records, or producing a usable query output.MapReduceA method by which computationally intensive problems can be processed on multiple computers, in parallel. The method can be divided into a mapping step and a reducing step.In the mapping step, a master computer divides a problem into smaller problems that are distributed to other computers. In the reducing step, the master computer collects the output from the other computers. Although MapReduce is intended for Big Data resources and can hold petabytes of data, most Big Data problems do not require MapReduce.Missing data Most complex data sets have missing data values. Somewhere along the line data elements were not entered, records were lost, or some systemic error produced empty data fields. Big Data, being large, complex, and composed of data objects collected from diverse sources, is almost certain to have missing data.Various mathematical approaches to missing data have been developed; commonly involving assigning values on a statistical basis; so-called imputation methods. The underlying assumption for such methods is that missing data arise at random. When missing data arises non-randomly, there is no satisfactory statistical fix.The Big Data curator must track down the source of the errors and somehow rectify the situation. In either case, the issue of missing data introduces a potential bias and it is crucial to fully document the method by which missing data is handled. In the realm of clinical trials, only a minority of data analyses bothers to describe their chosen method for handling missing data.MutabilityMutability refers to the ability to alter the data held in a data object or to change the identity of a data object. Serious Big Data is not mutable. Data can be added, but data cannot be erased or altered. Big Data resources that are mutable cannot establish a sensible data identification system, and cannot support verification and validation activities.The legitimate ways in which we can record the changes that occur in unique data objects (e.g., humans) over time, without ever changing the key/value data attached to the unique object.For programmers, it is important to distinguish data mutability from object mutability, as it applies in Python and other object-oriented programming languages. Python has two immutable objects: strings and tuples.Intuitively, we would probably guess that the contents of a string object cannot be changed, and the contents of a tuple object cannot be changed. This is not the case. Immutability, for programmers, means that there are no methods available to the object by which the contents of the object can be altered.Specifically, a Python tuple object would have no methods it could call to change its own contents. However, a tuple may contain a list, and lists are mutable. For example, a list may have an append method that will add an item to the list object. You can change the contents of a list contained in a tuple object without violating the tuple’s immutability.Parallel computing Some computational tasks can be broken down and distributed to other computers, to be calculated “in parallel.” The method of parallel programming allows a collection of desktop computers to complete intensive calculations of the sort that would ordinarily require the aid of a super-computer.Parallel programming has been studied as a practical way to deal with the higher computational demands brought by Big Data. Although there are many important problems that require parallel computing, the vast majority of Big Data analyses can be easily accomplished with a single, off-the-shelf personal computer.Protocol A set of instructions, policies, or fully described procedures for accomplishing a service, operation, or task. Protocols are fundamental to Big Data. Data is generated and collected according to protocols. There are protocols for conducting experiments, and there are protocols for measuring the results.There are protocols for choosing the human subjects included in a clinical trial, and there are protocols for interacting with the human subjects during the course of the trial. All network communications are conducted via protocols; the Internet operates under a protocol (TCP-IP, Transmission Control Protocol-Internet Protocol).Query The term “query” usually refers to a request, sent to a database, for information (e.g., Web pages, documents, lines of text, images) that matches a provided word or phrase (i.e., the query term). More generally a query is a parameter or set of parameters that are submitted as input to a computer program that searches a data collection for items that match or bear some relationship to the query parameters.In the context of Big Data, the user may need to find classes of objects that have properties relevant to a particular area of interest. In this case, the query is basically introspective, and the output may yield metadata describing individual objects, classes of objects, or the relationships among objects that share particular properties.For example, “weight” may be a property, and this property may fall into the domain of several different classes of data objects. The user might want to know the names of the classes of objects that have the “weight” property and the numbers of object instances in each class.Eventually, the user might want to select several of these classes (e.g., including dogs and cats, but excluding microwave ovens) along with the data object instances whose weights fall within a specified range (e.g., 20–30 pound). This approach to querying could work with any data set that has been well specified with metadata, but it is particularly important when using Big Data resources.Raw data Raw data is the unprocessed, original data measurement, coming straight from the instrument to the database with no intervening interference or modification. In reality, scientists seldom, if ever, work with raw data.When an instrument registers the amount of fluorescence emitted by a hybridization spot on a gene array, or the concentration of sodium in the blood, or virtually any of the measurement that we receive as numeric quantities, the output is produced by an algorithm executed by the measurement instrument.Pre-processing of data is commonplace in the universe of Big Data, and data managers should not labor under the false impression that the data received is “raw,” simply because the data has not been modified by the person who submits the data.Results The term “results” is often confused with the term “conclusions.” Interchanging the two concepts is a source of confusion among data scientists. In the strictest sense, “results” consist of the full set of experimental data collected by measurements. In practice, “results” are provided as a small subset of data distilled from the raw, original data.In a typical journal article, selected data subsets are packaged as a chart or graph that emphasizes some point of interest. Hence, the term “results” may refer, erroneously, to subsets of the original data, or to visual graphics intended to summarize the original data. Conclusions are the inferences drawn from the results. Results are verified; conclusions are validated.Science, Of course, there are many different definitions of science, and inquisitive students should be encouraged to find a conceptualization of science that suits their own intellectual development. For me, science is all about finding general relationships among objects.In the so-called physical sciences the most important relationships are expressed as mathematical equations (e.g., the relationship between force, mass, and acceleration; the relationship between voltage, current, and resistance). In the so-called natural sciences, relationships are often expressed through classifications (e.g., the classification of living organisms).Scientific advancement is the discovery of new relationships or the discovery of a generalization that applies to objects hitherto confined within disparate scientific realms (e.g., evolutionary theory arising from observations of organisms and geologic strata). Engineering would be the area of science wherein scientific relationships are exploited to build new technology.Square Kilometer Array The Square Kilometer Array is designed to collect data from millions of connected radio telescopes and is expected to produce more than one exabyte (1 billion gigabytes) every day.
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How do i add an electronic signature to a word document?
When a client enters information (such as a password) into the online form on , the information is encrypted so the client cannot see it. An authorized representative for the client, called a "Doe Representative," must enter the information into the "Signature" field to complete the signature.
How to use electronic signature paint?
Here is how to use electronic signature paint:
1. Get started in this tutorial, and follow all steps.
2. Take your digital signature and print it on a piece of paper, paper that is not too thick. You can use a regular paper for that. We recommend paper with the same color as your skin, as it will ensure that your signature will be easy to see. If your signature does not fit on your paper, it will be very difficult to see on it.
3. Get a paint marker. You would be surprised how much this costs, and for good reasons. We've found cheap paint markers at local craft shops. If you can't make these yourself, then get a good brand like Tacky or Wet N' Wild. You can buy these at local craft stores, or you can buy them online. We buy ours at , where it costs just $ for a ounce bottle.
4. After you've purchased and used a paint marker, take that paint marker to a surface that is not too slick for ink to adhere to, and lightly paint your digital signature onto it. This will not be too messy, and it is a good idea to paint lightly, since the thicker the paint, the more ink that will be needed.
5. Place your signature on the paper that you want your digital signature on, such as a piece of newspaper.
6. Using the tip of the paint marker, apply very light pressure to the paper with a very light stroke. The lighter your stroke, the harder it will be to see. You want it to be very lightly brushed, without the brush leaving any ink on the paper.
7. Remove the paper from the paper hol...
How to sign pdf in system viewer?
> The following tutorial is to show that it is possible to view or create pdf files in System Viewer:
First of all, a note of caution. Although the tutorial will work, it does require a little bit of knowledge about the way the pdf viewer works and how to use it. You have been warned!
First, if you don't know how to use system viewer, then I would recommend reading the following article on how to use system viewer: How to View PDFs in Microsoft System Viewer Tutorial. (Note that the article will only apply for Windows 7 and Vista)
The tutorial is based on using an existing PDF file called MyPDF file. To create a new PDF file from scratch, you only need the following file in the location "C:\Users\%username%\documents\Windows\MyPDF" to create the new PDF files.
You just need to make sure that you save MyPDF to "C:\Users\%username %\documents\Windows\MyPDF"
To view a PDF file in system viewer: Start System Viewer.
Select File, then open the file you want to view or create. You will see a file manager window where you can select or add folders or files.
Select MyPDF from the File list and then open the file.
To create a new PDF file, select New, then select PDF from the drop down list.
The new PDF will have the following features:
1. Background colour
2. Fullscreen
3. Text and image support
4. Multiple font formats
If you want to view an existing PDF file, go to "C:\Users\%username%\documents\Windows\MyPDF". There there you can select the file and open it....
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