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A military coup d'état is unlikely. But more likely than you think.Over the last 60 years, there have been 459 coup attempts around the world. About 50% have been successful.Storm clouds are gathering for the United States.The U.S. political system is slowly spinning down. This started with the New Deal, and accelerated rapidly during the 1960s. Meanwhile, global capitalism is speeding up.These tectonic forces will inevitably collide.U.S. government finances deteriorate each year. This is occurring at the Federal, State, and Local levels. On the other hand, capitalists grow stronger each year.The biggest clash in the U.S. is not between Left and Right. The biggest battles are between the Capitalist Class and the Political Class.Trump supporters on the Far Right have much in common with anti-globalists on the Far Left. Both groups are:PopulistAngryAlienatedBoth Far Left and Far Right groups:Complain bitterly about free trade.Seek political solutions to economic problems.Contain true believers in Politics.Want government to “fix” things.On the other hand, the typical owner of a McDonald’s, UPS Store, or 7–11 is a capitalist. So are most stock and real estate investors. They are not populists.Capitalists are not true believers in the Political system. They wish politicians would pursue lengthy vacations or cryogenic stasis. The longer, the better.They do not want the government to fix anything. Capitalists want the government to leave them alone.Tens of millions of U.S. citizens have no investments. 15% have a negative net worth. Their liabilities exceed their assets.For the bottom one-third of the population, net worth ranges from negative to +$8,000.Each day in America: 11,000 babies are born and 7,000 people die.1,600 of these 7,000 Americans die in debt. After 80 years, they leave this world with a lower net worth than they had at birth.Each day, 10,000 new people retire at age 65 with full government benefits. That will occur 365 days each year. Forever.A tsunami of additional debt is coming our way.This is our current situation.If government debt is assigned to citizens pro rata, the majority of Americans are in debt.Total debt per American family is $793,000. If unfunded commitments are totaled, the U.S. owes more money than all the other 195 nations combined.We are in debt up to our eyeballs.If you believe Donald or Hillary will change the course of events, then you haven’t been paying attention. There are far bigger historical forces at work.This is a Battle Royale. A grudge match. This is one of the few things Karl Marx got right. His magnum opus was Das Kapital. Not Das Politik.Consider these anecdotes:George Bush and Bill Clinton were political arch-enemies. Now they are great friends.A Republican now campaigns against free trade, while a Democrat gives $300,000 speeches to Goldman Sachs.President Obama (unaware his microphone is hot) calmly mentions to Mr. Putin: Announcements now would cause problems. After the election, I can be more open.President Bush’s cabinet members and press secretary observed discussions for the Iraq War before 9–11 terrorists were identified.In the Wikileaks e-mails of Hillary, she makes it clear her real position on Wall Street is the obverse of her public position.Retail politics is not what it appears to be.The third rail of American politics is redistribution from the Top 30% to the Bottom 30%. That third rail is deadly. It is more complicated than taking from the rich and giving to the poor. It is also taking from the Capitalist Class and giving to the Political Class.Politicians lie daily. Relentlessly.They need Capitalists to fund their campaigns, and they need Populists to garner votes. To retain power, politicians have no choice but to lie. Any politician who tells the truth will be zapped—either by Capitalist donors or Political voters.Side Note: Studies have found politicians and used car dealers live longer than the average American. Experimenters wrote: These subjects are less stressed by moral ambiguity.Despite all the campaign speeches, debates, and lies, politics spins in tiny circles. That’s why Capital supersedes Politics in the long run. Over time…Money always wins.When the British Empire went broke, Winston Churchill could not save it. When the French Empire was bankrupt, Napoleon could not bring it back to life.Spanish Hapsburgs extracted silver from the New World. They hit the Mega Trillion Lottery. But Spaniards spent that windfall like drunk sailors on shore leave—which they were.In 1570, poor people in Spain were rich thanks to government entitlements. But today, poor people in Spain are poor.Debt killed the Spanish Empire. Over time, they spent the original trillions…and then they kept spending. Spanish hegemony began spinning down long before the Battle of Trafalgar.Most people believe Empires exist due to Noble Ideals, Constitutions, Politics, Laws, Geography, Resources, or Military Power. Karl Marx knew better.Empires run on cash.Money = PowerSuper Money = SuperpowerIt also works in reverse. Debt killed the Roman Empire, Islamic Empire, Portuguese Empire, French Empire, Ottoman Empire, British Empire, et al.This is not new. Cash is King predates the Babylonians, Egyptians, and Assyrians. Historically speaking, Cash is King predates both cash and kings.Consider the Soviet Empire. The USSR never lost a world war. The Soviet Navy was never sunk. Their vast natural resources were not squandered. The KGB never liquidated the Politburo.The Soviet collapse was more pedestrian. They went broke.By the 1980s, expenditures exceeded receipts. Debt skyrocketed. Internal conflicts flared.Dasvidaniya, Comrade.Someday, debt will kill the U.S. Empire.If our military takes a more pronounced role, it will not occur because hothead generals seize power. It will occur because Americans lose faith in “democracy” and demand the military restore order.2,300 years ago, Plato wrote: All democracies end in tyranny.Plato believed democracies decline when citizens vote themselves benefits and loot the public treasury. Two millennia of history have proven Plato prescient.200 years after Plato, Gaius Gracchus in Rome began Annona bread subsidies. Citizens were thankful for the freebie in 123 B.C. But within two years, farmer revolts in Sicily led to skyrocketing grain prices. Romans said: The government must pay all costs. Not us.So much for gratitude.Soon, Roman politicians competed for power by promising ever-greater handouts to the masses. Emperors expanded the dole to include bread and circuses.Roman leaders eventually wised up, but it was too late. Julius Caesar and Augustus Caesar both worried freebies could not be stopped without sparking street riots.Side Note: In the Spanish Empire, the term bread and circuses was replaced by bread and bulls. But the concept of unearned handouts is always the same, Empire to Empire.Roman plebeians demanded government gifts decade after decade. Angry protests were routine. Citizens wanted continual increases in State benefits. Sound familiar?Each Empire has a slightly different tipping point — a point of cascading failure. The danger is most acute when there are more citizens taking than working.This character flaw is not unique to Romans, Spaniards, and Americans. All Empires suffer these internal battles over resources. Why?Because human nature never changes.Government handouts end badly. The problem is not politics, but mathematics. Populists want more. Always more. But there is never enough money. There never can be enough money.At the peak of the Roman Empire, Rome had a population of 1 million people. By the time the Empire fell, the population of Rome had decreased 95% to 50,000.Where Emperors once paraded with a huge entourage and Praetorian Guard, wild goats now munched on overgrown weeds. There were few humans to bother them. By 1870, the population had slowly grown to 200,000 — still 80% below the peak.In the long run: Money always wins.Is that outcome good or evil? Personal opinions are highly correlated with personal assets. But no personal opinion can alter historical facts: Debt is a killer.Karl Marx got it right.For 4,000 years, the progression of empires has followed the same path.First, the treasury is emptied. Then, taxes are raised. Then, money is borrowed from citizens. Then, from bankers. Then, from foreigners.Next, government debases the currency.The Romans clipped coins. We Americans print money. Same thing.More money is borrowed, and often dispensed within days. It is usually targeted to politically important groups.Almost nothing is spent on long-term investment. Most of the borrowed money fuels short-term consumption by the masses. After awhile, huge sums must be borrowed just to pay compound interest.A vicious debt cycle has been set in motion. Those who receive government benefits — whether in Rome, Spain, America, or somewhere else — simply do not care.They worry about Grandma’s Medicare and pension checks. Not their country’s future.In the next phase, politicians plot to confiscate private assets. They devise a clever name like …Save the Dolphins Asset Redeployment ActCapitalists are not fooled. Rich people are not stupid. The wealthy start plotting themselves, usually in secret. Some plot to flee with their capital, others plot a revolt.Politicians announce even higher taxes and capital controls. But the Capitalists are wily. Using lawyers, technology, and suitcases, capital leaks out to more friendly jurisdictions.As capital leaves, economic activity decreases. Some workers lose their jobs and become welfare recipients. Deficit spending increases.Finally, desperate politicians are forced to reduce benefits and freebies for the masses. That’s when the shit hits the fan.The military observes this Battle Royale from the sidelines, silently contemptuous of both lying politicians and crooked capitalists. For those who have dedicated their lives to Duty, Honor, Country, it’s like watching a poisonous snake battle a rabid mongoose.When hungry people break into your home or rob your store, lofty political ideals are no longer your top concern. Hobbes argued: Safety and stability are the highest priorities of the State.If we ever get to the point of a military coup d’etat, most Americans will be relieved. Pentagon specialists have already written Ops Plans for most contingencies.It is not because they are planning a takeover. They are not. It is because they plan for every possibility.If Canada invades Mexico, there’s a dusty Ops Plan somewhere in the Pentagon basement.In 1974, General Alexander Haig sent a FLASH message to top military commanders. It instructed them to disregard any orders from President Nixon to:Mobilize military forces to take control of CongressAttack the USSR or any other foreign powerGenerals and Admirals dedicate their entire lives to defending America. They will prevent any politician — even a President — from destroying our country.Most citizens have never interacted with a Flag Officer. The American public is not familiar with the military’s jaded view of politicians and bureaucrats. That is probably for the best.Ben Stein is an economist and movie star. While teaching Economics at the University of Chicago, his father established Stein’s Law: If something cannot go on forever, it won’t.Our Generals and Admirals know we will reach a crisis point. It is now inevitable. Average Americans are too emotional to see the truth. For 25 years, global futurists and scenario planners at the 3-letter agencies in Washington have analyzed these dangers.Back in 1940, actuaries knew FDR’s programs were creating a demographic time bomb. But politicians pay no attention to actual numbers. Why would they pay attention?Asking a politician to stop spending … is like asking a shark to stop swimming.Admiral Mike Mullen, General James Mattis, and other officers have made sharp comments about our downward financial spiral.The Federal Reserve, with its hundreds of PhD economists, has been silent as a tomb. The Fed is reticent to criticize free-spending politicians.Similarly, experts at CBO, GAO, and CIA are also quiet. They have produced hundreds of terrifying forecasts — which are promptly archived without widespread distribution. Some economic reports are even classified Secret “to prevent undue alarm.”No bureaucrat in Washington wants his budget chopped because he told the truth.On the other hand, Pentagon leaders have been blunt. Amazingly blunt. Our out-of-control debt is now a national security issue.Some U.S. Senators were incensed military officers would lecture them on runaway debt.The same thing happened In 410 AD.At the Pentagon, almost all Flag Officers hold a Masters or PhD.Admiral Mullen graduated from Harvard. The current Chairman of the Joint Chiefs, General Dunford, holds advanced degrees from Georgetown and Tufts. General Seng received her Masters from MIT and PhD from Stanford.Senior officers are better educated than the politicians who issue their orders.Military leaders have a deep understanding of political economy and history. They know any nation with deteriorating finances is vulnerable. As debt increases, so does risk. Threats multiply quickly on the back side of the curve.Updates:Admiral Mullen retired as Chairman of the Joint Chiefs of Staff.General Mattis retired as Secretary of Defense.The U.S. has detailed procedures for Continuity of Government. The most sensitive COG annexes require a Top Secret (SCI) clearance. Use your imagination.Since 1789, every service member has taken this oath: I do solemnly swear… to protect against all enemies, foreign and domestic. So help me God.Over the last 80 years, the U.S. has developed a redistribution ethos — a culture of gimme. In 1935, receiving unearned money from the government was considered shameful.Back then, government payments were indicative of personal failure.But today, tens of millions boldly seek bigger monthly payments.I demand my rights!Mass culture has changed individual thinking — in Rome, Spain, and America. If a future government violates the gimme expectations of Americans, many will become angry. Angry people are dangerous. Mobs of angry people are very dangerous.The Posse Comitatus Act of 1878 forbids the use of the military against civilians inside the United States. But there are many loopholes. This Act will be bypassed if there is widespread civil unrest.Los Angeles 199254 people were killed during riots after the Rodney King verdict. Property damage was $775 million. The Posse Comitatus Act and Insurrection Act of 1807 were quietly ignored. The California National Guard, Army Rangers, and Marines restored order.Some believe the Mayor incited the violence with his populist comments on the verdict. While the military patrolled with loaded weapons, His Honor the Mayor continued making populist statements. After all, the rioters were his longtime political supporters. Not U.S. Marines.That’s not Political Class. That’s Political Low Class.Similarly, the Capitalists whose stores were looted and burned were not a political priority. When wealthy Korean, Indian, and Chinese shopkeepers lost their life savings, angry looters did not care. Neither did populist politicians.Yet the movie stars in Beverly Hills were never in danger. Neither were the Capitalists in Bel Air.L.A.P.D. moved its officers from poor areas to wealthy areas. Marx would say: Of course.Money always wins.At first, rioters assumed the Rules of Engagement for civilian police and military police were identical. That was a terminal error for some criminals. If you attack Marines or MPs, you will not be going to jail.The events in L.A. were racial. But Marx and Hegel believed the most fundamental battles are economic — not racial, not ethnic, not religious.The anger of black thugs in Los Angeles mimics the anger of white rednecks in the Deep South. And the anger of Appalachian poor mimics that of sophisticates in Seattle and New York.It’s not about race, ethnicity, religion, social class, or politics. It’s all about the Benjamins. Empires crack due to internal competition for resources. Debt exacerbates the fractures.Some economic transitions are orderly, some are ugly.American “preppers” have survival manuals, automatic weapons, electric generators, years of food, and stacks of silver and gold.To us, they seem crazy. They may not seem crazy in the future.Sir John Glubb believed empires (democratic/republican/representative) become unstable beyond 250 years. Here are his stages of Empire.The Age of PioneersThe Age of ConquestThe Age of CommerceThe Age of AffluenceThe Age of IntellectualismThe Age of DecadenceIn my opinion, we will enter stage 6 within 50 years. Some believe we are already there.China and India are still in stage 2 or stage 3.Sir John calculated these lifespans in his books.231 years Greek Empire207 years Roman Republic250 years Ottoman Empire234 years Romanov Russian Empire250 years British Empire265 years United StatesThere are many common themes among these societies. Debt and devaluation of the currency are the most obvious. Most Empires disintegrate from within.From 1880–1900, London newspapers were filled with stories about whether the British Empire might someday end. Most Englishmen said: Impossible!Today those same stories appear in U.S. newspapers.Empire disintegration does not mean the society ceases to exist. The Spanish, French, and British settled into gracious “retirement” after ruling the world. They still exert enormous influence beyond their borders.On the other hand, Russians have been uncomfortable with their new role in the world. This intensified following bankruptcies in 1991 and 1998. Russians are trying to find their niche. Many there are still conflicted about the breakup of the U.S.S.R.Russians are proud, but enigmatic. They recently sent their only carrier (Admiral Kuznetzov) into British waters and down the English Channel.Navy corvettes from Norway, Spain, and Great Britain shadowed the Russian flagship.Sonar was not required. Kuznetzov belches smoke like a 1972 Ford Pinto.Mr. Putin is a KGB-trained genius. He and his spooks once devised a secret plan to crash the U.S. dollar in conjunction with the Chinese. The Chinese replied not yet.Mr. Putin understands the subtleties of global capitalism. The debt of Russia is de minimis compared to that of the United States.All U.S. rivals analyze the finances of our government and corporations. They are searching for vulnerabilities to exploit.65% of officers for the Russian SVR and Chinese MSS engage in economic analysis or espionage.These Chinese Army officers were indicted for economic espionage.This Chinese spy ring below (Wang Lei, Wang Hongwei, Ye Jian, Lin Yong, Shaoming Li) engaged in economic spying and theft.Two were busted on their return flight to Beijing. FBI agents boarded the plane just prior to takeoff and arrested them.[C’mon, fellas. Where is your creativity? James Bond would never steal secrets using Orville Redenbacher Microwave Popcorn.]Other convicted spies include: Larry Wu-Tai Chin, Katrina Leung, Peter Lee, Chi Mak, Moo Ko-Suen, Fei Ye, Ming Zhong, and Hua Jun Zhao.Some Chinese and Russian spies have been jailed, some deported, and some “turned” by our counterintelligence people.Mo Hailong is now in U.S. Federal prison. FBI agents caught Mo and his stooges Red-handed.[Bad pun. Too much beer…]Of all the Chinese spies, Shaoming Li took the best mug shot.Perhaps he attended the Hilton School of Espionage.The Russians have more spies in the U.S. today than during the Cold War. Their primary taskings are industrial spying and theft of commercial secrets. Most famous is Анна Васильевна Кущенко (Anna Vasilyevna Kushchenko).To her neighbors, she was known as Anna Chapman — an apparently shy young woman. At gatherings, she would discuss her love of cooking.Ms. Chapman had plenty of cash and lots of free time. She spent hours shopping and attending parties. She dined with two billionaires, multiple millionaires, and one cryptologic linguist.She quickly initiated a relationship with the latter. He thought:Wow! I am a lucky guy. It’s difficult to find a nice girl with wholesome values.Cryptologists don’t get out much.Anna even started a small business, and it boomed. Former Communists make the best Capitalists.As it turned out, Ms. Kushchenko was neither shy nor a good cook. But she was very proficient at pulse transmissions from inside Starbucks. An unmarked vehicle on the street would collect the data, and drive on.Apparently, not all Russian diplomats at the U.N. are actually diplomats. Who knew?When instructed to buy a “burner” cell phone, Ms. Kushchenko did so promptly. In her haste, she discarded the receipt into a trash can in front of the Verizon store.U.S. counterintelligence agents quickly retrieved the receipt, listened to her calls, and busted the entire spy network. Ten Russian “sleeper agents” were arrested.Mr. Putin gave her a bear hug back in Moscow. Lyudmila Putin skipped the homecoming.Jerry Chun Shing Lee is a naturalized U.S. citizen and former CIA officer. He sold valuable U.S. secrets to the Chinese. The Chinese, in turn, forwarded some of those secrets to the Russians.Twelve U.S. agents in both China and Russia were interrogated and executed. One was dragged out of his cubicle and shot as co-workers watched. No trial required.Lee also had a side gig. He would tip off the Chinese and North Koreans about private detectives investigating smuggling. These detectives were either arrested or disappeared.Some believe Lee should be shot as a traitor. But I have a better recommendation. Let’s shoot the dumbass at CIA who recruited him.Corporations embrace the farce of political correctness. But military and intelligence agencies operate in the real world. Life and death. They cannot allow political correctness to impede their mission.China and Russia are playing for keeps. With the zeal of reformed smokers, former Reds now spend their time hustling Green.The United States is more threatened by financial competitors than ICBMs. People who focus on Politics — those depending upon Hillary or Donald — are amateurs.Politicians come and go. Presidents and Prime Ministers are here today, gone tomorrow. But Capital compounds. So does debt. Year after year, decade after decade.Money always wins.Is there a way to fix our country? Yes! We must stop borrowing money we can never repay. Every Empire has the same long-term solution:We must live within our means.Will the Political Class agree we must live within our means and follow a budget? Not a chance. Will the Capitalists agree to ever-increasing benefits for the Political Class? Not a chance.That angry standoff forces America into ever-deepening debt. The Ship of State slowly sinks into the mud — just like so many great Empires before us.Imagine a brutally honest or religiously devout candidate for President:“We Americans cannot consume more than we produce. We must pay our monthly bills. We cannot charge trillions more on the national Visa and MasterCard.We must control government spending, even if this hurts welfare recipients and retirees. We are honorable people. We must live within our means, just like everyone else.”Voters reply:Eight years ago, President Obama promised to cut our annual deficit in half. In actuality, the national debt soared $8 trillion. Mr. Obama racked up more debt in a single year than the U.S. accumulated in our first 200 years combined. Whoops.But it gets worse. The current candidates are not even pretending to reduce the national debt. At least Mr. Obama said pleasant things. Give the President credit for lying well.Mr. Trump’s announced plans will lead to $3.6 trillion in additional debt. Ms. Clinton’s plans will lead to $3.5 trillion. The actual numbers will obviously be much higher. This election forces us to choose between the Titanic and the Lusitania.There are two Americas.Group 1 of 2: The Capitalist ClassThis first group is comprised of gold collar workers, white collar workers, investors, lifetime savers, and blue collar workers who own their own business.These people work long hours, 6 days a week. Compared to most of the 7.1 billion people on Earth, they make enormous amounts of money. These people avoid taking vacations and are often stressed out. But they have money in the bank. Some have billions in the bank.They own real estate and other investments. Their children grow up with every possible advantage. Their lifestyle is luxurious compared to most.Whatever happens over the next 50 years, these people will be fine. They know how to compete and win anywhere in the world.Despite the stress and lack of sleep, these people are happy.American movies and television focus on the luxurious lifestyle of this first group. Our popular culture is a primary export to other nations.People around the world exert enormous effort to become U.S. citizens. America is often their first and only choice for new citizenship.But we Americans should temper our delusions of grandeur. Newcomers aspire to wealth. Not America.No one travels thousands of miles to experience U.S. democracy. Foreigners do not gaze in awe at our well-dressed and slightly-crooked politicians.What causes people to leave behind family, friends, country — everything they know?They come to America to get rich. Filthy rich.It has been that way for 250 years.Group 2 of 2: The Political ClassThis second group contains true believers in the political system. This group is mainly comprised of Populists. These people work less and make less. They often live paycheck to paycheck. Some are highly educated, some poorly educated.Some Populists have been discriminated against, some have health problems, some want “balanced” lives, and some are just plain lazy.Some suffer from bad luck. More suffer from bad choices.This group wants the U.S. government to make things “fair.” They see Politics as a way to bring equality and justice into American society. From a philosophical point of view, this group has many strong arguments. They are often idealists, and they are usually right.But from a practical point of view, this group is in big trouble. They are fighting both history and destiny.These people will not be fine over the next 50 years. They can barely compete now, even with all the built-in advantages of the American system.As future globalization forces them to compete with Indians, Chinese, Russians, etc., many of these Americans will sink. As Marx predicted, capitalism is becoming hyper-capitalism.Harvard University conducted a poll in April 2016. 51% of millennials aged 18–29 agreed with this statement: I do not support capitalism.This is an astounding result for a country built upon capitalism. On the other hand, a change in values is common in late-stage Empires.Populists dislike global competition, international trade, and hyper-capitalism.They do not want to:study hard for 4–8 additional yearswork long hours under stresstake laptops with them on vacationcomplete extra work on weekendsTheir opinion…“If government must tax Capitalists 73% or enact disastrous trade barriers, so be it.If exploding government debt results in more money for my family, then let’s borrow all we can. When something disastrous happens in the future, the U.S. can stop borrowing then.I want a balanced and happy lifestyle right now.Someone owes me that!The government is my ticket to the Good Life. I fervently believe in Politics and Equality. Not global competition.Additional education? Long work hours? Acute stress?That’s for other people. Not me.”Who would possibly object to people over profit?Plato would object. So would Aristotle. Both believed democracy was parekbaino (deviant and perverse) because it leads to bankruptcy of the State.2,300 years of historical data support their view. Democracies go bankrupt when too much is confiscated from workers and given to takers.People Over Profit = Expropriation and RedistributionOf course, there are many different ways to bankrupt an Empire. Possible paths include:military defeatsdiplomatic errorscivil warsecological problemsethnic uprisingsclass divisionsnatural catastrophesimperial overstretchBut citizens cannot know ex ante if these problems are temporary or fatal. There are too many variables to predict the trajectory of an Empire — with one notable exception.There is one guaranteed path to disaster:Emptying the public treasury followed by exploding debt.Money always wins.True to form, today’s populists (on both the Left and Right) want happy and balanced lives. And they want the US government to help pay for it. That really means they want their fellow citizens to pay for it — just as Plato and Aristotle predicted.When the expectations of populists are disappointed, they get angry and take to the streets.Observers often equate populism with Leftist movements such as Occupy Wall Street. But there are also Right-leaning populists, such as AARP, trade warriors, tariff-seekers, and those who seek to ban all immigration.Populists on both the Left and Right ask for government intervention to advance their interests against the Capitalists. Populists are not concerned about government debt or their country’s future — as long as their advocacy group gets what it wants. This focus on narrow self-interest is precisely what offended Plato and Aristotle.When populist demonstrations become violent, the military intervenes.Could the military also be used against Capitalists? Yes. But this is rare.Historically, capitalists are more likely to take their capital and flee. Pudgy bankers have no interest in fighting Marines.Capitalist cocktails are Stolichnaya. Not Molotov.On both the political Left and Right, populists engage in fantasy. They mistakenly believe Capitalism can be stopped by voting. Politicians offer sweet lies, and populists gobble it up:Vote for me. I’ll protect your job, even if you are uncompetitive. Plus, I’ll boost your pay!It is a sham. A con job. A brazen fabrication.Global Capitalism cannot be stopped by voters, politicians, or protesters. It would be easier to stop a tsunami with a raincoat.When it comes to global competition, we have 5,000 years of anecdotal data and 2,000 years of quantitative data. There is no free lunch. Not here, not anywhere.We Americans must compete.Roman politicians and American politicians are similar. But Roman capitalists and American capitalists are different.In Rome, capital was physical property. In America, most liquid wealth is digital and intellectual. That makes it portable. The stocks of GE, IBM, and Honeywell are worth the same in New York, Zurich, Singapore, and Hong Kong. Same thing for bonds. Portable.At first glance, digital wealth seems like a great thing. American entrepreneurs and capitalists have created enormous value-added (and huge riches) around the world. Google, Amazon, Apple, and Facebook are just a few examples.But look deeper. If populist revolts start in Detroit, Baltimore, or San Francisco, the government cannot dispatch its Praetorian Guard to confiscate Big Money.Modern capital is not held in estates, vineyards, and other physical property.If things get bad in the U.S., modern wealth (financial assets) will flee in a blinding flash of electrons. Even the military would be powerless to stop it. America needs that capital.There is an invisible cap on tax rates today. There was no cap in ancient Rome. The emperors could confiscate everything you owned.If tax rates are doubled tomorrow, Google multi-millionaires will not be sticking around Silicon Valley or the USA. Gold collar workers are in demand worldwide. They will not remain in any locale which assesses 73% tax rates.This 73% rate was suggested by Paul Krugman and Thomas Piketty. How can two wunderkind be so smart and yet so dumb?In Great Britain in 1967, the top tax rate was 98%. 83% base rate + 15% surtax = 98%The wealthy kept £2 out of every £100 earned. 98 of 100 pounds went to the politicians. Wealthy Brits failed to maintain a stiff upper lip. The Beatles were so irritated they stopped singing about love. Instead, they wrote a hit song about taxes. Then, they left the U.K.We have census data from that period. Some of the smartest people in England left. Many never moved back. It was a huge Brain Drain in pursuit of “equality.”In older Empires, the Political Class could easily threaten the Capitalist Class using the military. All governments (then and now) enforce a monopoly on:taxationimprisonmentuse of force against individualsBut for modern empires like the U.S., it becomes more difficult to tax liquid assets with each passing year.Inequality is a genuine problem. But many proposed solutions simply will not work. Politicians make bombastic speeches and voters applaud loudly. But that’s about it, folks.If the U.S. tries to screw rich people, the wealthy will send their capital out of the country — just like sending their kids to boarding school in Switzerland.If populists really go crazy, rich people will leave the United States. And they will take their Big Brains and their Big Assets with them.Money always wins.Voters want to soak the rich. But that is easier said than done. Liquid capital can flow overseas at the touch of a button.On the other hand, taxes on real estate have not changed much since Roman times. After all, you cannot easily move a 40-room mansion overseas. Similarly, restaurants, shopping malls, grocery stores, oil wells, coal mines, dairy farms, and hydroelectric dams are not going anywhere.Prediction: over the next 50 years, expect to see taxes on U.S. real estate rise and taxes on financial assets decline. Politicians cannot tax it…if the County Sheriff cannot impound it.Clearly, this is not fair. But this is the real world. The global system runs on Capital Flows. Not Ethics. Not Politics. Not Equality.So what does this have to do with the military? Just this…Many Americans have a notion of “fairness” which can never be achieved except in populist daydreams.When voters are finally told the truth — that we must encourage and reduce taxes on Capital for the long-term good of our country — Regular Joes will become very unhappy.If they are simultaneously out of work or having trouble providing for their families, that simmering anger could lead to civil disorder.No one likes arrogant billionaires. Egomaniacs!But we need them…and their money.ConclusionThe gathering storm for the U.S. is still far away — on the distant horizon. But it is coming. Government debt never goes away. It just gets bigger and bigger.Our problem is not unique. Many other Superpowers have been in this predicament before us. Unlike them, we Americans still have a chance to solve our problems.We are rapidly running out of time. The vicious cycle of debt has already started to accelerate. $8 trillion in 8 years — that is unprecedented in all human history.If we do not live within our means, then we will face crisis after crisis. It is inescapable. The United States is powerful. But worldwide capital flows are more powerful.“Retirement” could be rough for us. The American national character is not suited to being a gracious loser. But we may have no choice.If we continue our out-of-control spending, we will be forced to join the Brits, French, and Spanish in the Superpower Emeritus Club.Bottom LineThe probabilities of a military coup d'état within the U.S. are very small. But if economic chaos becomes acute, Americans will be grateful the military is prepared.Further ReadingDas Kapital, MarxThe Republic, PlatoWhy Nations Fail, Acemoglu and RobinsonColossus: The Rise and Fall of the American Empire, FergusonEmpire: Rise and Demise of the British Order with Lessons for Global Power, FergusonCivilization: The Six Killer Apps of Western Power, FergusonThe Ascent of Money, FergusonFrom Hegel to Marx, Hook21 Trends for the 21st Century, G. MarxEnd of the Free Market: Who Wins the War Between States and Corporations, BremmerCapitalism and Freedom, FriedmanSinking Nation: Unraveling the Complexities of U.S. Debt, MarshallSuicide of a Superpower, BuchananCoup d' État: A Practical Handbook, LuttwakDebt and Disorder: International Instability and U.S. Imperial Decline, MacEwanGlobal Debt Crisis: U.S. and European Federalism, Peterson and NadlerThe Rise of the West, McNeillDebt: The First 5000 Years, GraeberWealth and Poverty of Nations: Why Some Are So Rich and Some So Poor, LandesBooks by Sir John GlubbThe Fate of EmpiresThe Course of EmpireThe Story of the Arab LegionA Soldier with the ArabsBritain and the Arabs: A Study of Fifty Years, 1908 to 1958War in the Desert: An R.A.F. Frontier CampaignThe Great Arab ConquestsThe Empire of the ArabsThe Yezidis, Sulubba, and Other Tribes of Iraq and Adjacent RegionsThe Lost Centuries: From the Muslim Empires to the Renaissance of EuropeSyria, Lebanon and JordanThe Middle East Crisis: A Personal InterpretationA Short History of the Arab PeoplesThe Life and Times of MuhammadPeace in the Holy Land: An Historical Analysis of the Palestine ProblemSoldiers of Fortune: The Story of the MamlukesThe Way of Love: Lessons from a Long LifeHaroon Al Rasheed and the Great AbbasidsInto Battle: A Soldier's Diary of the Great WarArabian Adventures: Ten Years of Joyful ServiceThe Changing Scenes of Life: An AutobiographyBad Books - Fuzzy Opinion from Fuzzy AcademicsCapital in the Twenty First Century, Piketty and GoldhammerPeddling Prosperity, KrugmanThe Limits to Growth: A Report to the Club of Rome, Meadows and RandersThe Population Bomb, EhrlichGuns, Germs, and Steel: The Fates of Human Societies, DiamondCollapse, DiamondThe Rise and Fall of the Great Powers, KennedySaving Capitalism, ReichPolitical Order and Political Decay, Fukuyama

Thinking about angles between vectors is a very useful way to understand and visualize factor analysis and principal components analysis.Factor analysis begins with the set of all correlations among k variables (this is summarized in a correlation matrix, R).A goal of factor analysis is to see whether you can understand the variables in terms of a smaller number of underlying dimensions. For example, suppose you have k different assessments of intelligence. Can these all be understood as measures of a single kind of general intelligence? Or are they better understood as measures of two kinds of intelligence (verbal versus quantitative)? Or do you need to imagine many kinds of intelligence?At one extreme, each of the tests measures something completely different from the others. At the other extreme, all the tests can be thought of as measuring one “same” thing. In most empirical applications, analysts try to reduce variables to two or three or other small numbers of dimensions or factors.To represent the correlation information geometrically, start with variables X1 and X2. You can draw these vectors on a flat piece of paper (2 dimensional space).In lecture, I demonstrate this two dimensional space by drawing two vectors on the table top.When you add a third variable, you need another dimension, a vector above or below the table top. I put my elbow on the table and move it around to demonstrate.When we add a rourth variable, we need a fourth dimension (by that time we can’t visualize it well).I think of these vectors as a bundle of umbrella spokes in a k dimensional space; the angles among these k vectors correspond exactly to the correlations in the R matrix.For dimension reduction, we want to “flatten” this bundle of vectors into a smaller dimensional space with minimal distortion of all the angles between vectors.The results of factor analysis are represented by new vectors called “factors”. We now represent the correlations among variables in a different way, by showing the correlation of each variable with each factor (factor loadings are correlations between variables and factors).The factor loadings can be used to reproduce the correlation matrix R. If we use k factors to represent correlations among k variables, we can perfectly reproduce R. If we use fewer factors, the ability to reproduce R declines. There are informal rules and goodness of fit measures to decide if the loss of information is acceptable.(I am skipping many steps here… an initial solution is obtained for k factors to represent correlations among k variables; the factors are listed in order of their ability to reproduce correlations (this is indexed by eigenvalues); and a decision is make to retain only the few factors with the largest eigenvalues).Here is a hypothetical situation in which variables are represented relative to two retained or extracted factors.These data come from the Bem Sex Role Inventory. Bem’s goal was to show that self ratings of masculine and feminine traits are two separate dimensions, not opposite endpoints of a single dimension. You can see that the masculine adjectives form one ‘bundle’ of vectors; the feminine adjectives form a second ‘bundle’ of vectors. The small angles within bundles indicate that the variables in that group have fairly high positive correlations with each others. The nearly 90 degree angle between the bundles indicates that masculine items have small correlations with feminine items and vice versa.From linear algebra, a pair of orthogonal factors forms the “basis” for a two dimensional space. However, the location of this reference vectors (factors) is arbitrary. Factor rotation involves rotating the factors in space. The goal is to align the factor axes with the bundles.After rotation, the new solution looks like this.Based on this rotated pattern of loadings, we can say that Factor 1 has high positive correlations with ratings of affectionate, warm, etc. and that Factor 2 has high positive correlations with dominant, aggressive, forceful, etc. Factors are named by looking at their correlates. We could call Factor 1 a femininity factor and Factor 2 a masculinity factor.This is a convenient way to understand factor analysis.Refer to Justin Rising's answer to What is the geometric interpretation of variance? and you will realize that the lengths of vectors in these diagrams provide information about the proportion of variance that can be reproduced for each variable.After a factor analysis of mental ability items, the analyst can make statements about: How many factors (different kinds of) mental ability are needed to do a reasonably good job of representing the correlations among measures in the data, and, how would you name or characterize these factors based on the mental ability measures that each one correlates with?Note that factor analysis does not provide a final answer to the question: How many kinds of mental ability are there out in the world? But only to the question, how many kinds of mental ability can we infer from the hand picked measures and cases in our sample? We can only “find” factors for which we have included measures and as with all other analyses, violations of linearity and other assumptions, and sampling error, affect results.Pretty neat, huh?All figures (except the first) are from Warner (2012), Applied statistics: From bivariate through multivariate techniques. Thousand Oaks, CA: Sage. The factor analysis chapter provides details about the computations of factor loadings.

Below I have listed the 326 courses available and also the course instructors. Included is the length of each course. For more information on Datacamp visit my blog at http://www.mlnomad.com where I have posted more details.CoursesAcquire new skills fast in courses that combine short expert videos with immediate hands-on-keyboard exercises.All Data Science CoursesIntroduction to RMaster the basics of data analysis by manipulating common data structures such as vectors, matrices, and data frames.Python Data Science Toolbox (Part 2)Continue to build your modern Data Science skills by learning about iterators and list comprehensions.Python Data Science Toolbox (Part 1)Learn the art of writing your own functions in Python, as well as key concepts like scoping and error handling.Introduction to Importing Data in PythonLearn to import data into Python from various sources, such as Excel, SQL, SAS and right from the web.Supervised Learning with scikit-learnLearn how to build and tune predictive models and evaluate how well they'll perform on unseen data.Introduction to SQLMaster the basics of querying tables in relational databases such as MySQL, SQL Server, and PostgreSQL.Introduction to Deep Learning in PythonLearn the fundamentals of neural networks and how to build deep learning models using Keras 2.0.Machine Learning with Tree-Based Models in PythonIn this course, you'll learn how to use tree-based models and ensembles for regression and classification using sciki...Object-Oriented Programming in PythonLearn the fundamentals of object-oriented programming: classes, objects, methods, inheritance, polymorphism, and others!Intermediate RContinue your journey to becoming an R ninja by learning about conditional statements, loops, and vector functions.6 hoursFILIP SCHOUWENAARSData Science Instructor at DataCampIntroduction to Machine LearningLearn to train and assess models performing common machine learning tasks such as classification and clustering.6 hoursGILLES INGHELBRECHTDoctoral Student at Vrije Universiteit BrusselCleaning Data in RLearn to explore your data so you can properly clean and prepare it for analysis.4 hoursNICK CARCHEDIProduct Manager at DataCampIntermediate R: PracticeStrengthen your knowledge of the topics you learned in Intermediate R with a ton of new and fun exercises.4 hoursFILIP SCHOUWENAARSData Science Instructor at DataCampData Visualization with ggplot2 (Part 1)Learn to produce meaningful and beautiful data visualizations with ggplot2 by understanding the grammar of graphics.5 hoursRICK SCAVETTARick Scavetta is a co-founder of Scavetta Academy.Data Visualization with ggplot2 (Part 2)Take your data visualization skills to the next level with coordinates, facets, themes, and best practices in ggplot2.5 hoursRICK SCAVETTARick Scavetta is a co-founder of Scavetta Academy.Data Visualization with ggplot2 (Part 3)This course covers some advanced topics including strategies for handling large data sets and specialty plots.6 hoursRICK SCAVETTARick Scavetta is a co-founder of Scavetta Academy.Text Mining with Bag-of-Words in RLearn the bag of words technique for text mining with R.4 hoursTED KWARTLERSenior Director, Data Scientist at Liberty MutualCase Study: Exploring Baseball Pitching Data in RUse a rich baseball dataset from the MLB's Statcast system to practice your data exploration skills.4 hoursPlay previewBRIAN M. MILLSAssistant Professor at the University of FloridaIntroduction to Portfolio Analysis in RApply your finance and R skills to backtest, analyze, and optimize financial portfolios.5 hoursKRIS BOUDTProfessor of Finance and Econometrics at VUB and VUACredit Risk Modeling in RApply statistical modeling in a real-life setting using logistic regression and decision trees to model credit risk.4 hoursLORE DIRICKManager of Data Science Curriculum at Flatiron SchoolMachine Learning with caret in RThis course teaches the big ideas in machine learning like how to build and evaluate predictive models.4 hoursZACHARY DEANE-MAYERAutomation First Data Scientist at DataRobotIntroduction to Databases in PythonIn this course, you'll learn the basics of relational databases and how to interact with them.4 hoursPlay previewJASON MYERSCo-Author of Essential SQLAlchemy and Software EngineerManipulating Time Series Data with xts and zoo in RThe xts and zoo packages make the task of managing and manipulating ordered observations fast and mistake free.4 hoursPlay previewJEFFREY RYANCreator of xts and quantmodTime Series Analysis in RLearn the core techniques necessary to extract meaningful insights from time series data.4 hoursPlay previewDAVID S. MATTESONAssociate Professor at Cornell UniversityImporting & Cleaning Data in R: Case StudiesIn this series of four case studies, you'll revisit key concepts from our courses on importing and cleaning data in R.4 hoursNICK CARCHEDIProduct Manager at DataCampFinancial Trading in RThis course covers the basics of financial trading and how to use quantstrat to build signal-based trading strategies.5 hoursPlay previewILYA KIPNISProfessional Quantitative Analyst and R programmerImporting and Managing Financial Data in RLearn how to access financial data from local files as well as from internet sources.5 hoursPlay previewJOSHUA ULRICHQuantitative Analyst & member of R/Finance Conference committeeInteractive Data Visualization with BokehLearn how to create versatile and interactive data visualizations using Bokeh.4 hoursPlay previewTEAM ANACONDAData Science TrainingCase Study: Exploratory Data Analysis in RUse data manipulation and visualization skills to explore the historical voting of the United Nations General Assembly.4 hoursPlay previewDAVID ROBINSONChief Data Scientist, DataCampIntroduction to Importing Data in RIn this course, you will learn to read CSV, XLS, and text files in R using tools like readxl and data.table.3 hoursFILIP SCHOUWENAARSData Science Instructor at DataCampIntermediate Importing Data in RParse data in any format. Whether it's flat files, statistical software, databases, or data right from the web.3 hoursFILIP SCHOUWENAARSData Science Instructor at DataCampData Visualization in RThis course provides a comprehensive introduction to working with base graphics in R.4 hoursPlay previewRONALD PEARSONPhD in Electrical Engineering and Computer Science from M.I.T.Statistical Thinking in Python (Part 1)Build the foundation you need to think statistically and to speak the language of your data.3 hoursPlay previewJUSTIN BOISLecturer at the California Institute of TechnologyStatistical Thinking in Python (Part 2)Learn to perform the two key tasks in statistical inference: parameter estimation and hypothesis testing.4 hoursPlay previewJUSTIN BOISLecturer at the California Institute of TechnologyIntroduction to Statistical Modeling in RThis course is designed to get you up to speed with the most important and powerful methodologies in statistics.4 hoursDANIEL KAPLANDeWitt Wallace Professor at Macalester CollegeIntermediate Statistical Modeling in RIn this follow-up course, you will expand your stat modeling skills from the introduction and dive into more advanced...4 hoursDANIEL KAPLANDeWitt Wallace Professor at Macalester CollegeIntermediate Portfolio Analysis in RAdvance you R finance skills to backtest, analyze, and optimize financial portfolios.5 hoursPlay previewROSS BENNETTCo-author of PortfolioAnalytics R packageIntermediate Importing Data in PythonImprove your Python data importing skills and learn to work with web and API data.2 hoursPlay previewHUGO BOWNE-ANDERSONData Scientist at DataCamppandas FoundationsLearn how to use the industry-standard pandas library to import, build, and manipulate DataFrames.4 hoursPlay previewTEAM ANACONDAData Science TrainingManipulating DataFrames with pandasYou will learn how to tidy, rearrange, and restructure your data using versatile pandas DataFrames.4 hoursPlay previewTEAM ANACONDAData Science TrainingMerging DataFrames with pandasThis course is all about the act of combining, or merging, DataFrames, an essential part your Data Scientist's toolbox.4 hoursPlay previewTEAM ANACONDAData Science TrainingBond Valuation and Analysis in RLearn to use R to develop models to evaluate and analyze bonds as well as protect them from interest rate changes.4 hoursPlay previewCLIFFORD ANGVice President at Compass LexeconFoundations of Inference in RLearn how to draw conclusions about a population from a sample of data via a process known as statistical inference.4 hoursPlay previewJO HARDINProfessor at Pomona CollegeIntroduction to Data Visualization in PythonLearn complex data visualization techniques using Matplotlib and seaborn.4 hoursPlay previewTEAM ANACONDAData Science TrainingExploratory Data Analysis in RLearn how to use graphical and numerical techniques to begin uncovering the structure of your data.4 hoursPlay previewANDREW BRAYAssistant Professor of Statistics at Reed CollegeCorrelation and Regression in RLearn how to describe relationships between two numerical quantities and characterize these relationships graphically.4 hoursPlay previewBEN BAUMERAssistant Professor at Smith CollegeIntroduction to Data in RLearn the language of data, study types, sampling strategies, and experimental design.4 hoursPlay previewMINE CETINKAYA-RUNDELAssociate Professor at Duke University & Data Scientist and Pr...ARIMA Models in RBecome an expert in fitting ARIMA (autoregressive integrated moving average) models to time series data using R.4 hoursPlay previewDAVID STOFFERProfessor of Statistics at the University of PittsburghUnsupervised Learning in RThis course provides an intro to clustering and dimensionality reduction in R from a machine learning perspective.4 hoursPlay previewHANK ROARKSenior Data Scientist, BoeingVisualizing Geospatial Data in RLearn to read, explore, and manipulate spatial data then use your skills to create informative maps using R.4 hoursPlay previewCHARLOTTE WICKHAMAssistant Professor at Oregon State UniversityIntroduction to Network Analysis in PythonThis course will equip you with the skills to analyze, visualize, and make sense of networks using the NetworkX library.4 hoursPlay previewERIC MAData Carpentry instructor and author of nxviz packageCase Studies: Manipulating Time Series Data in RStrengthen your knowledge of the topics covered in Manipulating Time Series in R using real case study data.4 hoursPlay previewLORE DIRICKManager of Data Science Curriculum at Flatiron SchoolObject-Oriented Programming with S3 and R6 in RManage the complexity in your code using object-oriented programming with the S3 and R6 systems.4 hoursRICHIE COTTONCurriculum Architect at DataCampSentiment Analysis in RLearn sentiment analysis by identifying positive and negative language, specific emotional intent and making compelli...4 hoursTED KWARTLERSenior Director, Data Scientist at Liberty MutualCleaning Data in PythonThis course will equip you with all the skills you need to clean your data in Python.4 hoursPlay previewDANIEL CHENData Science Consultant at Lander AnalyticsUnsupervised Learning in PythonLearn how to cluster, transform, visualize, and extract insights from unlabeled datasets using scikit-learn and scipy.4 hoursPlay previewBENJAMIN WILSONDirector of Research at Lateral.ioVisualizing Time Series Data in RLearn how to visualize time series in R, then practice with a stock-picking case study.4 hoursPlay previewARNAUD AMSELLEMQuantitative Trader and creator of the R Trader blogLife Insurance Products Valuation in RLearn the basics of cash flow valuation, work with human mortality data and build life insurance products in R.4 hoursPlay previewKATRIEN ANTONIOProfessor, KU Leuven and University of AmsterdamFoundations of Probability in RIn this course, you'll learn about the concepts of random variables, distributions, and conditioning.4 hoursPlay previewDAVID ROBINSONChief Data Scientist, DataCampScalable Data Processing in RLearn how to write scalable code for working with big data in R using the bigmemory and iotools packages.4 hoursPlay previewMICHAEL KANEAssistant Professor at Yale UniversityCase Study: School Budgeting with Machine Learning i...Learn how to build a model to automatically classify items in a school budget.4 hoursPlay previewPETER BULLCo-founder of DrivenDataIntroduction to R for FinanceLearn essential data structures such as lists and data frames and apply that knowledge directly to financial examples.4 hoursPlay previewLORE DIRICKManager of Data Science Curriculum at Flatiron SchoolIntermediate R for FinanceLearn about how dates work in R, and explore the world of if statements, loops, and functions using financial examples.5 hoursPlay previewLORE DIRICKManager of Data Science Curriculum at Flatiron SchoolSupervised Learning in R: ClassificationIn this course you will learn the basics of machine learning for classification.4 hoursPlay previewBRETT LANTZData Scientist at the University of MichiganString Manipulation with stringr in RLearn how to pull character strings apart, put them back together and use the stringr package.4 hoursPlay previewCHARLOTTE WICKHAMAssistant Professor at Oregon State UniversityWriting Efficient R CodeLearn to write faster R code, discover benchmarking and profiling, and unlock the secrets of parallel programming.4 hoursPlay previewCOLIN GILLESPIEAssoc Prof at Newcastle University, Consultant at Jumping RiversForecasting in RLearn how to make predictions about the future using time series forecasting in R.5 hoursPlay previewROB J. HYNDMANProfessor of Statistics at Monash UniversityMachine Learning with Tree-Based Models in RIn this course, you'll learn how to use tree-based models and ensembles for regression and classification.4 hoursPlay previewGABRIELA DE QUEIROZData Scientist and founder of R-LadiesWorking with Web Data in RLearn how to efficiently import data from the web into R.4 hoursPlay previewCHARLOTTE WICKHAMAssistant Professor at Oregon State UniversityQuantitative Risk Management in RWork with risk-factor return series, study their empirical properties, and make estimates of value-at-risk.5 hoursPlay previewALEXANDER J. MCNEILProfessor of Actuarial Science at the University of York.Spatial Statistics in RLearn how to make sense of spatial data and deal with various classes of statistical problems associated with it.4 hoursPlay previewBARRY ROWLINGSONResearch Fellow at Lancaster UniversityData Visualization with lattice in RLearn to visualize multivariate datasets using lattice graphics.4 hoursPlay previewDEEPAYAN SARKARMember of R-Core & the creator of latticeIntroduction to Spark with sparklyr in RLearn how to analyze huge datasets using Apache Spark and R using the sparklyr package.4 hoursPlay previewRICHIE COTTONCurriculum Architect at DataCampData Types for Data Science in PythonConsolidate and extend your knowledge of Python data types such as lists, dictionaries, and tuples, leveraging them t...4 hoursPlay previewJASON MYERSCo-Author of Essential SQLAlchemy and Software EngineerSentiment Analysis in R: The Tidy WayIn this course, you will the learn principles of sentiment analysis from a tidy data perspective.4 hoursPlay previewDATACAMP CONTENT CREATORCourse InstructorIntermediate Network Analysis in PythonAnalyze time series graphs, use bipartite graphs, and gain the skills to tackle advanced problems in network analytics.4 hoursPlay previewERIC MAData Carpentry instructor and author of nxviz packageMultiple and Logistic Regression in RIn this course you'll learn to add multiple variables to linear models and to use logistic regression for classificat...4 hoursPlay previewBEN BAUMERAssistant Professor at Smith CollegeInference for Linear Regression in RIn this course you'll learn how to perform inference using linear models.4 hoursJO HARDINProfessor at Pomona CollegeIntroduction to Natural Language Processing in PythonLearn fundamental natural language processing techniques using Python and how to apply them to extract insights from ...4 hoursPlay previewKATHARINE JARMULFounder, kjamistanBuilding Chatbots in PythonLearn the fundamentals of how to build conversational bots using rule-based systems as well as machine learning.4 hoursPlay previewALAN NICHOLCo-founder and CTO of RasaExtreme Gradient Boosting with XGBoostLearn the fundamentals of gradient boosting and build state-of-the-art machine learning models using XGBoost to solve...4 hoursPlay previewSERGEY FOGELSONVP of Analytics and Measurement Sciences, ViacomEquity Valuation in RLearn the fundamentals of valuing stocks.4 hoursPlay previewCLIFFORD ANGVice President at Compass LexeconCase Studies: Building Web Applications with Shiny in RPractice your Shiny skills while building some fun Shiny apps for real-life scenarios!4 hoursPlay previewDEAN ATTALIFounder & Lead R-Shiny Consultant at AttaliTech LtdSupervised Learning in R: RegressionIn this course you will learn how to predict future events using linear regression, generalized additive models, rand...4 hoursPlay previewJOHN MOUNTCo-founder, Principal Consultant at Win-Vector, LLCImporting and Managing Financial Data in PythonIn this course, you'll learn how to import and manage financial data in Python using various tools and sources.5 hoursPlay previewSTEFAN JANSENFounder & Lead Data Scientist at Applied Artificial IntelligenceOptimizing R Code with RcppUse C++ to dramatically boost the performance of your R code.4 hoursTEAM THINKRR TrainingManipulating Time Series Data in PythonIn this course you'll learn the basics of working with time series data.4 hoursPlay previewSTEFAN JANSENFounder & Lead Data Scientist at Applied Artificial IntelligenceTime Series Analysis in PythonIn this course you'll learn the basics of analyzing time series data.4 hoursPlay previewROB REIDERConsultant at Quantopian and Adjunct Professor at NYUParallel Programming with Dask in PythonLearn how to take the Python workflows you currently have and easily scale them up to large datasets without the need...4 hoursPlay previewTEAM ANACONDAData Science TrainingSpatial Analysis with sf and raster in RAnalyze spatial data using the sf and raster packages.4 hoursPlay previewZEV ROSSPresident, ZevRoss Spatial AnalysisIntroduction to PySparkLearn to implement distributed data management and machine learning in Spark using the PySpark package.4 hoursNICK SOLOMONData ScientistNetwork Analysis in RIn this course you'll learn to analyze and visualize network data with the igraph package.4 hoursPlay previewJAMES CURLEYAssociate Professor at UT AustinCase Studies in Statistical ThinkingTake vital steps towards mastery as you apply your statistical thinking skills to real-world data sets and extract ac...4 hoursPlay previewJUSTIN BOISLecturer at the California Institute of TechnologyJoining Data in SQLJoin two or three tables together into one, combine tables using set theory, and work with subqueries in PostgreSQL.5 hoursPlay previewCHESTER ISMAYData Science Evangelist at DataRobotIntroduction to the TidyverseGet started on the path to exploring and visualizing your own data with the tidyverse, a powerful and popular collect...4 hoursPlay previewDAVID ROBINSONChief Data Scientist, DataCampIntroduction to ShellThe Unix command line helps users combine existing programs in new ways, automate repetitive tasks, and run programs ...4 hoursGREG WILSONCo-founder of Software CarpentryDeveloping R PackagesCreate and share your own R Packages!4 hoursAIMEE GOTTHead of Skill Assessment Content at DataCampInference for Numerical Data in RIn this course you'll learn techniques for performing statistical inference on numerical data.4 hoursMINE CETINKAYA-RUNDELAssociate Professor at Duke University & Data Scientist and Pr...Visualizing Time Series Data in PythonVisualize seasonality, trends and other patterns in your time series data.4 hoursPlay previewTHOMAS VINCENTHead of Data Science at Getty ImagesData Manipulation with data.table in RMaster core concepts about data manipulation such as filtering, selecting and calculating groupwise statistics using ...4 hoursMATT DOWLEAuthor of data.tableFundamentals of Bayesian Data Analysis in RLearn what Bayesian data analysis is, how it works, and why it is a useful tool to have in your data science toolbox.4 hoursRASMUS BÅÅTHSenior Data Scientist at King (Activision Blizzard)Working with Dates and Times in RLearn the essentials of parsing, manipulating and computing with dates and times in R.4 hoursPlay previewCHARLOTTE WICKHAMAssistant Professor at Oregon State UniversityIntroduction to GitThis course is an introduction to version control with Git for data scientists.4 hoursGREG WILSONCo-founder of Software CarpentryPython for R UsersThis course is for R users who want to get up to speed with Python!5 hoursPlay previewDANIEL CHENData Science Consultant at Lander AnalyticsCluster Analysis in RDevelop a strong intuition for how hierarchical and k-means clustering work and learn how to apply them to extract in...4 hoursPlay previewDMITRIY GORENSHTEYNLead Data Scientist at Memorial Sloan Kettering Cancer CenterIntroduction to Financial Concepts in PythonUsing Python and NumPy, learn the most fundamental financial concepts.4 hoursPlay previewDAKOTA WIXOMQuantitative Analyst and Founder of Begin Your Quant Journey - Financial Timeseries AnalysisIntroduction to Portfolio Risk Management in PythonEvaluate portfolio risk and returns, construct market-cap weighted equity portfolios and learn how to forecast and he...4 hoursPlay previewDAKOTA WIXOMQuantitative Analyst and Founder of QuantCourse.comInference for Categorical Data in RIn this course you'll learn how to leverage statistical techniques for working with categorical data.4 hoursANDREW BRAYAssistant Professor of Statistics at Reed CollegeConda EssentialsLearn how to easily manage your software using conda.3 hoursTEAM ANACONDAData Science TrainingInteractive Data Visualization with plotly in RLearn to create interactive graphics entirely in R with plotly.4 hoursADAM LOYAssistant Professor of Statistics at Carleton CollegeVisualizing Big Data with TrelliscopeLearn how to visualize big data in R using ggplot2 and trelliscopejs.4 hoursRYAN HAFENAuthor of TrelliscopeJSBuilding Dashboards with flexdashboardIn this course you'll learn how to create static and interactive dashboards using flexdashboard and shiny.4 hoursELAINE MCVEYDirector of Quantitative Mobility, TransLocCommunicating with Data in the TidyverseLeverage the power of tidyverse tools to create publication-quality graphics and custom-styled reports that communica...4 hoursTIMO GROSSENBACHERData Journalist at SRF DataIntroduction to Linear Modeling in PythonExplore the concepts and applications of linear models with python and build models to describe, predict, and extract...4 hoursJASON VESTUTOData Scientist, University of Texas at AustinIntroduction to MongoDB in PythonLearn to manipulate and analyze flexibly structured data with MongoDB.4 hoursDONNY WINSTONDonny is a computer systems engineer at Lawrence Berkeley Nati...ChIP-seq Workflows in RLearn how to analyse and interpret ChIP-seq data with the help of Bioconductor using a human cancer dataset.4 hoursPETER HUMBURGStatisticianJoining Data with data.table in RThis course will show you how to combine and merge datasets with data.table.4 hoursSCOTT RITCHIEPostdoctoral Researcher in Systems GenomicsFoundations of Functional Programming with purrrLearn to easily summarize and manipulate lists using the purrr package.4 hoursDATACAMP CONTENT CREATORCourse InstructorVisualization Best Practices in RLearn to effectively convey your data with an overview of common charts, alternative visualization types, and percep...4 hoursNICHOLAS STRAYERBiostatistician at VanderbiltBuilding Dashboards with shinydashboardIn this course you'll learn to build dashboards using the shinydashboard package.4 hoursPlay previewLUCY D’AGOSTINO MCGOWANPostdoctoral fellow, Johns Hopkins Department of BiostatisticsModeling with Data in the TidyverseExplore Linear Regression in a tidy framework.4 hoursPlay previewALBERT Y. KIMAssistant Professor of Statistical & Data Sciences at Smith Co...Human Resources Analytics: Exploring Employee Data in RManipulate, visualize, and perform statistical tests on HR data.5 hoursPlay previewBEN TEUSCHPeople Analytics Partner at FacebookBusiness Process Analytics in RLearn how to analyze business processes in R and extract actionable insights from enormous sets of event data.4 hoursGERT JANSSENSWILLENAuthor of bupaR packageWorking with Data in the TidyverseLearn to work with data using tools from the tidyverse, and master the important skills of taming and tidying your data.4 hoursPlay previewALISON HILLProfessor and Data ScientistSupervised Learning in R: Case StudiesApply your supervised machine learning skills by working through four case studies using data from the real world.4 hoursPlay previewDATACAMP CONTENT CREATORCourse InstructorForecasting Product Demand in RLearn how to identify important drivers of demand, look at seasonal effects, and predict demand for a hierarchy of pr...4 hoursPlay previewARIC LABARRDirector and Senior Scientist at Elder ResearchMachine Learning for Marketing Analytics in RIn this course you'll learn how to use data science for several common marketing tasks.4 hoursPlay previewVERENA PFLIEGERData Scientist at INWT StatisticsCase Studies: Network Analysis in RApply fundamental concepts in network analysis to large real-world datasets in 4 different case studies.4 hoursTED HARTSenior Data ScientistHierarchical and Mixed Effects Models in RIn this course you will learn to fit hierarchical models with random effects.4 hoursPlay previewRICHARD ERICKSONQuantitative EcologistMachine Learning for Time Series Data in PythonThis course focuses on feature engineering and machine learning for time series data.4 hoursCHRIS HOLDGRAFFellow at the Berkeley Institute for Data ScienceCustomer Analytics and A/B Testing in PythonLearn how to use Python to create, run, and analyze A/B tests to make proactive business decisions.4 hoursRYAN GROSSMANData Scientist at EDO Inc.Linear Classifiers in PythonIn this course you will learn the details of linear classifiers like logistic regression and SVM.4 hoursPlay previewMIKE GELBARTInstructor, the University of British ColumbiaHuman Resources Analytics: Predicting Employee Churn...In this course you'll learn how to apply machine learning in the HR domain.4 hoursHRANT DAVTYANAssistant Professor of Data Science at the American University...Parallel Programming in RThis course covers in detail the tools available in R for parallel computing.4 hoursPlay previewHANA SEVCIKOVASenior Research Scientist, University of WashingtonFeature Engineering with PySparkLearn the gritty details that data scientists are spending 70-80% of their time on; data wrangling and feature engine...4 hoursJOHN HOGUELead Data Scientist, General MillsIntroduction to BioconductorLearn to use essential bioconductor packages using datasets from virus, fungus, human and plants!4 hoursPAULA MARTINEZData ScientistHuman Resources Analytics: Predicting Employee Churn...Predict employee turnover and design retention strategies.4 hoursANURAG GUPTAPeople Analytics PractitionerMarketing Analytics: Predicting Customer Churn in Py...Learn how to use Python to analyze customer churn and build a model to predict it.4 hoursMARK PETERSONSenior Data Scientist at Alliance DataData Privacy and Anonymization in RPublicly release data sets with a differential privacy guarantee.4 hoursPlay previewCLAIRE BOWENPostdoctoral Researcher at the Los Alamos National LaboratoryNonlinear Modeling in R with GAMsGAMs model relationships in data as nonlinear functions that are highly adaptable to different types of data science ...4 hoursPlay previewDATACAMP CONTENT CREATORCourse InstructorBayesian Modeling with RJAGSIn this course, you'll learn how to implement more advanced Bayesian models using RJAGS.4 hoursALICIA JOHNSONAssociate Professor, Macalester CollegeStructural Equation Modeling with lavaan in RLearn how to create and assess measurement models used to confirm the structure of a scale or questionnaire.4 hoursPlay previewERIN BUCHANANProfessor at Harrisburg University of Science and TechnologyDifferential Expression Analysis with limma in RLearn to use the Bioconductor package limma for differential gene expression analysis.4 hoursJOHN BLISCHAKPostdoctoral Scholar at University of ChicagoFactor Analysis in RExplore latent variables, such as personality using exploratory and confirmatory factor analyses.4 hoursJENNIFER BRUSSOWPsychometrician at Ascend LearningIntroduction to Predictive Analytics in PythonIn this course you'll learn to use and present logistic regression models for making predictions.4 hoursNELE VERBIESTData Scientist at Python PredictionsChoice Modeling for Marketing in RLearn to analyze and model customer choice data in R.4 hoursELEA MCDONNELL FEITAssistant Professor of Marketing at Drexel UniversityCategorical Data in the TidyverseGet ready to categorize! 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