Big Data: How Data Analytics Is Transforming the World

Course No. 1382
Professor Tim Chartier, Ph.D.
Davidson College
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Course No. 1382
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What Will You Learn?

  • Learn how to put "big" data to work in your own life - from your cell phone use to your exercise routine.
  • Delve into artificial intelligence and discover how computers are programmed to think and make decisions like humans.
  • See how a mathematical approach called bracketology can help you pick winners during March Madness.
  • Test the theory that everyone is connected by six degrees of separation.

Course Overview

Data is everywhere, shedding light on all aspects of life. Retailers know what’s selling and who’s buying. Pollsters test opinions on everything from candidates to consumer goods. Doctors follow their patients’ vital signs. Social networks register the interactions of millions. Sensors measure the changing weather. And as athletes play, fans collect exhaustive statistics on their performance.

If something can be measured, then in all likelihood a vast archive of data is already being compiled—and it is growing daily. Often, the data is unprocessed, waiting for someone to analyze it and discover new, valuable knowledge about the world.

This is the role of data analytics, a powerful set of tools for making sense of datasets of all sizes—from a personal exercise log to the massive collections of “big data” that define our information age. From science to sales, from sociology to sports, data analytics is unraveling the fascinating secrets hidden in numbers, patterns, relationships, and information of every kind.

Consider these examples:

  • Cell phone science: If you are an avid user of your cell phone, try downloading several months of your calling data. You may see daily and long-term patterns in your usage that surprise you. Plus, any changes in your routine, such as a vacation, will show up prominently.
  • Hardball analytics: The book and film Moneyball tell how the Oakland A’s overcame one of the smallest budgets in major league baseball to assemble a division-winning team. The secret? Managers used overlooked data analytics to hire undervalued, high-performing players.
  • Presidential prediction: In the 2012 presidential election, statistician Nate Silver and a few others correctly predicted the winner of all 50 states and the District of Columbia. Here, weighting criteria make it possible to analyze data collected by hundreds of pollsters from thousands of distinct polls.

In our age of accelerating progress in so many fields, it’s easy to lose sight of the underlying innovation that makes this revolution possible. In case after case, the big breakthrough comes from data analytics, the mathematical magic that turns undigested information into life-transforming insights and advances.

Big Data: How Data Analytics Is Transforming the World introduces you to the key concepts, methods, and accomplishments of this versatile approach to problem solving. Taught by Professor Tim Chartier, an award-winning Associate Professor of Mathematics and Computer Science at Davidson College, these 24 half-hour lectures give you the big picture on big data, highlighting the crucial role of data analytics in today’s world and the even greater impact it will have in the future.

A Course for Data Users at All Levels

You need no expertise in mathematics to follow this exciting story. Professor Chartier explains the basic computational techniques used in data analytics, but his focus is on how these ideas are applied and the amazing results they achieve. His wealth of case histories and his many helpful graphics make Big Data both accessible and entertaining. Those who will benefit from his presentation include

  • those in business, government, science, and other endeavors, who want a view into what data analytics can do for them;
  • the intellectually curious, eager to investigate the role of computing and “data scraping” in the modern-day miracles of the information age;
  • math enthusiasts who relish seeing a wide range of mathematical techniques address practical challenges;
  • those considering, or already pursuing, work with data and aspiring to explore the full scope of their remarkable field; and
  • anyone who relies on the Internet, smart phones, social media, or other tools that make them a participant in the data analytics revolution.

Big Data at Work

The volume, velocity, and variety of available data have increased at an astonishing rate during the last twenty years. That is to say, there are vast amounts of stockpiled data, and more is being generated constantly; the speed at which data is used, updated, and overturned in favor of newer data continues to accelerate; and data comes from many different sources and can be put to diverse uses. The miracle of data analytics is that ingenious algorithms are able to process this data deluge, which has been compared to trying to drink from a fire hose of information.

For instance, in just fifteen minutes the number of photos uploaded to Facebook exceeds the total number of photographs stored in the New York Public Library’s photo archives. Yet you can see a picture on your Facebook news feed within seconds after it’s posted. A high-speed computer algorithm allows the flood of imagery to be managed in a way that’s both timely and orderly. Professor Chartier explains how programmers achieve such feats by focusing only on the data that’s crucial to a specific task, while ignoring everything that’s irrelevant.

Big Data takes you behind the scenes to witness many examples of data analysis in action, including the following:

  • Google Flu Trends: Google search queries on flu symptoms have sometimes proved more accurate and up-to-date at plotting the spread of flu than reports issued by doctors and hospitals. Explore the pitfalls and enormous potential of Internet traffic for charting many different trends.
  • Online recommendations: Predictive analytics deals with forecasting the future, a task taken very seriously by companies like Netflix and Amazon that aim to predict what customers want. Learn how Netflix came up with an impressively accurate movie recommendation algorithm.
  • March Madness: A classic exercise in data analytics is predicting the playoff winners of the NCAA basketball tournament, held every March. Follow the system for filling the game brackets, designed by Professor Chartier, and see how it applies to many other problems.

But big data and data analytics can also be a mixed blessing. While the field has revolutionized fraud detection, making many kinds of transactions much more secure, it has the potential to threaten personal privacy in ways that can be hard to spot. In this course, you learn that one of the best defenses for privacy is to know how data is compiled and processed, and which activities are the most compromising.

A Tool for Everyone

Honored as the Mathematical Association of America’s first ever Math Ambassador, Professor Chartier is a champion of the fun, challenge, and breathtaking power of mathematics—qualities that are beautifully illustrated in data analytics.

He especially relishes the links between sports and math. Not only does data analytics give you deep insight into the relative qualities of players, but it can establish a theoretical limit on performance—as when you learn how to estimate the fastest possible time for the 100-meter dash.

Professor Chartier also describes how simple analysis improved his own performance as a swimmer—which illustrates a key point: data analytics can be put to use by anybody for any problem that involves a dataset, no matter what size.

With Big Data, you discover tools that are transforming the world and that you can use to transform your own life. It’s like watching a thrilling spectator sport that invites you to suit up and join the action!

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24 lectures
 |  Average 31 minutes each
  • 1
    Data Analytics—What’s the “Big” Idea?
    Sample the tremendous scope and power of data analytics, which is transforming science, business, medicine, public policy, and many other spheres of modern life. Investigate why this revolution is happening now, and look at some common misconceptions about data analysis. x
  • 2
    Got Data? What Are You Wondering About?
    Data analysis is not just for large organizations and large datasets; it’s also for the average person. Learn how to put data to work in your own life—from charting your cell phone usage to personalizing your medical care or improving your exercise routine. x
  • 3
    A Mindset for Mastering the Data Deluge
    Today’s data users often feel like they’re drinking from a fire hose of information. Investigate strategies that help manage the data deluge, and learn efficient ways to think about data that separate what’s genuinely useful from what can be strategically ignored. x
  • 4
    Looking for Patterns—and Causes
    Humans are experts at pattern recognition, which is a key skill in data analysis. But when are patterns real and when are they imagined? Study some surprising correlations between apparently unrelated phenomena, asking whether there is a cause-and-effect relation or mere coincidence is involved. x
  • 5
    Algorithms—Managing Complexity
    Algorithms—rules to follow for solving problems—are the secret of managing huge datasets. Start by looking at simple algorithms, including an amazingly effective sorting procedure that you can perform by hand. Then see how these concepts apply to more complex problems, such as web search engines. x
  • 6
    The Cycle of Data Management
    Study what happens after you gather data. It must first be stored, then organized, integrated with data from other sources, and analyzed. Now you are ready to act on the information that the data provides. Determine how this cycle works in practice, and uncover some hidden pitfalls. x
  • 7
    Getting Graphic and Seeing the Data
    Graphics have long been a compelling way to present and understand data. Survey some unusually effective graphics from the pre-computer era. Then explore the wealth of graphical tools available today. Graphics can reveal new information, but they can also obscure it when used poorly. x
  • 8
    Preparing Data Is Training for Success
    “Garbage in, garbage out” is a famous expression in computer science, underscoring the importance of starting with reliable data. Learn how data is prepared to remove errors and ambiguities. As an example, see how the US Postal Service perfected machines that can read hastily scribbled addresses. x
  • 9
    How New Statistics Transform Sports
    Follow the saga of the 2002 Oakland A’s, famously depicted in the book and film Moneyball. Thanks to data analytics, the A’s made it to the major league playoffs with a roster of undervalued players. Survey the increasing role of data at all levels of sports competition. x
  • 10
    Political Polls—How Weighted Averaging Wins
    Study the role of big data in predicting election results. Contrast the disastrous 1936 presidential poll by the Literary Digest with today’s impressively accurate aggregators of polls, such as statistician Nate Silver. Analyze what makes aggregation more effective than any single poll. x
  • 11
    When Life Is (Almost) Linear—Regression
    Explore the power of regression analysis for modeling the past and future, focusing on a technique called the linear least squares method. As an example, use data from Olympic gold medal times for the 100-meter dash. Calculate a theoretical fastest possible time for the event. x
  • 12
    Training Computers to Think like Humans
    Delve into the field of artificial intelligence, discovering how computers are programmed to think and make decisions like humans. An automated version of the 20 questions game illustrates how neural networks are the key to machine learning—a technology that is now in widespread use. x
  • 13
    Anomalies and Breaking Trends
    Sometimes it is the odd bit of data—the outlier in a sea of statistics—that is crucial to solving a mystery. See how sophisticated anomaly detection has led to a significant drop in credit card fraud. The same approach helps understand cultural trends that go viral. x
  • 14
    Simulation—Beyond Data, Beyond Equations
    Enter the world of simulation, which allows researchers to model behavior that would otherwise be too dangerous or expensive to study. Investigate the history of the subject and its multiplying applications—from science and engineering to entertainment. x
  • 15
    Overfitting—Too Good to Be Truly Useful
    Learn how to avoid the perils of overfitting, which is when an overly complex model or noisy data leads to flawed conclusions. Explore object lessons in this common pitfall, including an earthquake forecast that was disastrously wrong. x
  • 16
    Bracketology—The Math of March Madness
    Every year, millions of people engage in a hugely popular data exercise called March Madness. See how a mathematical approach called bracketology helps you excel at picking winners in the playoff games of the NCAA basketball tournament. x
  • 17
    Quantifying Quality on the World Wide Web
    Internet searches used to be frustratingly hit-or-miss. See how Google changed that by creating a realistic model of the way web surfers use the Internet. Then look at attempts to hijack search results to improve page rankings and how programmers thwart these tactics. x
  • 18
    Watching Words—Sentiment and Text Analysis
    We are nearing the point where every book ever written is accessible and searchable in digital form—as already exists for the even more voluminous texts from Twitter, Facebook, and other media. Learn how data analysts mine this limitless storehouse of words for new cultural and business insights. x
  • 19
    Data Compression and Recommendation Systems
    Data compression is crucial for storing and transmitting digital images at a fraction of their original size. See how compression also improves online recommendations, as shown by the Netflix million dollar competition, which led to a new algorithm for personalized recommendations. x
  • 20
    Decision Trees—Jump-Start an Analysis
    Probe the power of decision trees by breaking down the demographics of survivors of the Titanic disaster, an analysis that tells the tragic story of events aboard the sinking ship. Then test decision trees in other applications, marveling at their ability to carve quickly through data. x
  • 21
    Clustering—The Many Ways to Create Groups
    Clustering is a powerful way to discover new relationships in data by sorting it into groups, called clusters. Explore this family of techniques by searching for clusters in the Million Song Dataset. Then try other examples that show the exceptional flexibility of clustering. x
  • 22
    Degrees of Separation and Social Networks
    Test the popular theory that six steps, at most, connect you to any person on the planet. Social networks like Facebook provide a wealth of data for quantifying our relative connectedness. See how graph theory helps you to visualize any linked phenomena. x
  • 23
    Challenges of Privacy and Security
    Big data can be a big threat to privacy. Learn how surveillance cameras, smart phones, and Internet use provide a wealth of opportunities for tracking specific individuals. Examine privacy issues raised by corporate and government activity, and review what you can do to lead a more secure life. x
  • 24
    Getting Analytical about the Future
    Focus on a branch of data analytics called predictive analytics, concerned with predicting the future. Imagine attending such a conference years from now. What can you expect? Answer the question with the tools you have learned in the course, and come up with some surprising forecasts! x

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Your professor

Tim  Chartier

About Your Professor

Tim Chartier, Ph.D.
Davidson College
Dr. Tim Chartier is an Associate Professor of Mathematics and Computer Science at Davidson College. He holds a B.S. in Applied Mathematics and an M.S. in Computational Mathematics, both from Western Michigan University. He received his Ph.D. in Applied Mathematics from the University of Colorado Boulder. Professor Chartier is a recipient of a national teaching award from the Mathematical Association of America (MAA)....
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Big Data: How Data Analytics Is Transforming the World is rated 3.6 out of 5 by 38.
Rated 4 out of 5 by from How to think about big data I really enjoyed this course. I'm not a digital native, so a lot of the jargon was new concepts for me. I had read the negative reviews but got it anyway. Glad I did. While it in fact started out slow, things picked up quickly and the course illustrated some of the many ways in which data is being analyzed. I found some lectures to be more interesting than others, but on the whole, it was a very good learning experience. I love baseball, and so does the professor, so we had that in common. Despite what some reviewers noted, I certainly did not find sports to be the linchpin of the course (but yes, Olympic running times, baseball and March Madness are covered). A broad array of topics are also considered: the postal service and handwriting recognition, election polls, credit cards and fraud, AI, Google, Facebook, Netflix, poker, Titanic, Kevin Bacon, etc. Lecture 7 on graphing was really good. The course does exactly what it says: How Data Analytics is Transforming the World. This is pretty much a top down view of big data analytics. It is not an upper-level course on sabermetrics or anything like that. If that's your interest, look elsewhere. This is about thinking about data; it's about how finding hidden patterns in data can lead to insight and better informed business decisions. In this respect, it's a solid course. Once you get past the initial foregrounding lectures, it is fun. One negative for me: perhaps due to teleprompter-itis, the professor's bazillion false starts, hesitations, and recasts were off putting.
Date published: 2018-10-07
Rated 5 out of 5 by from You Need to Know this Stuff! Unbundles how data analytics is done. Best quote, "data analytics gives you an answer, not the only answer or the best answer" Extensive exploration of a topic we all need to know more about. Definitely recommend!
Date published: 2017-08-24
Rated 3 out of 5 by from Good Basic Information about Data Analytics I wanted to learn the basics about Data Analytics, as best i understand the field, and i that is what I learned.
Date published: 2017-02-16
Rated 3 out of 5 by from Big Data is the Future Dr. Chartier is very knowledgeable and provides a good overview of "Big Data" and some of the mathematical techniques used to derive insights from Big Data. If you are like me and keep hearing more about "big data" and want to better understand what that means, then this will be a good course to provide an overview. However, if you are a practitioner and want to learn more HOW to do these techniques, then I'd point you towards the course: Mathematical Decision Making: Predictive Models and Optimization.
Date published: 2016-06-08
Rated 4 out of 5 by from Good Course -- but missing in some areas As a former database engineer, I found this course refreshing – and interesting. Professor Chartier is thoroughly knowledgeable and well-prepared – as well as an excellent speaker – even if reading a teleprompter. Well done – thank you! Though data analytics here is discussed as a “business tool” – I’ve learned that the same concepts may apply to other areas of life & living. One that got my attention was in chapter 15, quote: “Knowing when you’ve done something WRONG can be as important as doing something RIGHT”, unquote”. In my decades of technical training, I’ve discovered that academia has its place in describing how things DO work. Alternatively, only EXPERIENCE gives one the insight of how things DO NOT work – and that is rarely taught (if any) in academia. Looking in retrospect of my 45+ year career, about 30% of my past technical decisions were based on circumventing the WRONG – of things I learned from experience. Put another way: If you had to go up and engage the enemy in air-to-air combat, would you rather go up with an ACE that has shot down 100 enemy planes – or go up with an aeronautical engineer who brags about his Magna Cuumm Janitor? Those who fail to see value in BOTH will float around in a euphoric bubble. There’s an old saying: “in academia you learn the lesson then take the test – whereas in real life, you take the test and then learn the lesson”. It may not be intuitive to most people; but learning what is WRONG (to evade it) is most CERTAINLY learning. The course perspective seems to focus primarily on PERSONAL gain of web related data rather than a BUSINESS analysis of data a company collects over time. Likely the good professor did some “analysis” about what $ell$ -- before constructing the course. Yes - I acknowledge the excellent concepts. For example, it seems significant emphasis was placed on SPORTS scores & sports characters (including card games)…which is “gee wizz” but not relevant for routine business in (say) package delivery. For example, “what gadget in our inventory sells significantly in the Midwest during April-June?” Instead, it appears that 100% of the data discussed in the course comes from discussing the web, Twitter, emails, and other social networking. Alternatively, some companies acquire and generate enormous amounts of their OWN internal data that needs analysis for decision makers. Interesting was the chapter on web search engines and weather prediction where hurricane alerts minimized the damage. I would like to have seen a similar follow-through on business statistics (various industries) – showing past technologies & statistics: how lack of data analysis caused disaster and inversely, where skillful data analysis provided success.
Date published: 2016-05-05
Rated 5 out of 5 by from Glad I ignored the other reviews I am so glad I ignored the less than flattering reviews on this course. My reasoning was that if it was that bad the Great Courses would not have put it out in the first place. My reasoning was correct. I have found the course a facinating oveiew of the subject full of tantalising insights into the would of big data that are current and relevant. eg how facebook manages it's data centres. I have a PhD in Electroinc Engineering and an undergraduate degree in Artifical Intelligence. I been have working in web programming and app development for over 20 years. There was plenty in this course that I didn't know. So I thoughly enjoyed it. However, if you want a course with reams of irrelvant equations and hours of pointless mathematical drivel then this course may not be for you.
Date published: 2016-01-10
Rated 2 out of 5 by from Unfortunately, it's a "fluff" course TGC has some excellent technical courses that are gentle but still retain a lot of "meat". These courses (anything by Art Benjamin or Scott Stevens are prime examples) present technical material in a friendly and engaging way, but still teach actual techniques by working in detail through useful example problems. Unfortunately, the Big Data course does not offer such quality. There were very few segments that actually taught anything marginally substantial (digit recognition was one, bracketology was another), which is why I gave 2 rather than 1 star. But the overwhelming majority of the material was non-technical, descriptive fluff that is simply not helpful for someone genuinely interested in the subject. Even for those who really want "fluff", the course is still not a good value. You can find better stuff for free on youtube, coursera, MIT open courseware, etc. I don't want to be redundant with other reviews, so I'll just summarize by registering my disappointment and giving a thumbs-down.
Date published: 2015-12-23
Rated 1 out of 5 by from Superficial Overview of Subject My first review, but I was so disappointed by this course, I had to say that if you already know anything about data processing there are better ways to spend 12 hours of your time. The first 8 lessons are too simple and have nothing to do with Big Data, for example, explanation of powers of tens or what a bit is. Later lectures are mostly examples of data analysis without enough depth to allow insights of how to use them. Afterwards you will know that neural networks exist, but that's it. Too many sports examples, many of which are fun to talk about over a beer, but don't particularly seem to have anything to do with big data.
Date published: 2015-05-25
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