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Big Data: How Data Analytics Is Transforming the World

Big Data: How Data Analytics Is Transforming the World

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Big Data: How Data Analytics Is Transforming the World

Big Data: How Data Analytics Is Transforming the World

Professor Tim Chartier Ph.D.
Davidson College
Course No.  1382
Course No.  1382
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Course Overview

About This Course

24 lectures  |  31 minutes per lecture

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.

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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!

View Less
24 Lectures
  • 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

Lecture Titles

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Tim  Chartier
Ph.D. Tim Chartier
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). He is the author of Math Bytes: Google Bombs, Chocolate-Covered Pi, and Other Cool Bits in Computing and coauthor (with Anne Greenbaum) of Numerical Methods: Design, Analysis, and Computer Implementation of Algorithms. As a researcher, he has worked with both Lawrence Livermore National Laboratory and Los Alamos National Laboratory, and his research was recognized with an Alfred P. Sloan Research Fellowship.

Dr. Chartier is a member and past chairperson of the Advisory Council for the National Museum of Mathematics, and was named the first Math Ambassador of the Mathematical Association of America. He fields mathematical questions for ESPN’s Sport Science program and has served as a resource for the CBS Evening News, National Public Radio, The New York Times, and other major news outlets.

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Reviews

Rated 3.9 out of 5 by 14 reviewers.
Rated 5 out of 5 by Good overview of data analysis I recently started a masters program in predictive analytics and watched this course for an overview of what lies ahead for me. I bought the course when it first came out, before any reviews, and now that I have finished all the classes it has been interesting for me to go back and read the subsequent reviews. There seems to be a bi modal distribution here with some folks loving the overview, others finding it too superficial. My take is that data analysis is an incredibly deep and broad topic combining applied mathematics and computer science and that this course does a good job providing an overview of the topic. The course won't teach you the mathematical steps to solve a differential equation, but it will point out the areas in data analysis where differential equations are used. I think that is an appropriate level for an introduction to this topic and I personally found the course quite useful and thought provoking. December 9, 2014
Rated 5 out of 5 by How Data Analytics Is Transforming the World I have purchased over a dozen courses from "The Great Courses" and this one is by far the best. I have already went through this course twice . It has proven valuable to me in the Big Data space as I am preparing a large presentation on Big Data for my company. Many of the data points i found in this course were accurate and timely. I highly recommend this course. December 2, 2014
Rated 2 out of 5 by Important Topic; Very Superficial Treatment "Big Data" (a.k.a. data analytics) is not just a description, it's an academic field (you can get a Ph.D. in it) and a crucial part of modern computer technology. It's already affecting many areas of all of our lives, and it will only grow in importance. The course description notes that "data analytics is unraveling the fascinating secrets hidden in numbers, patterns, relationships, and information of every kind" and that it is "mathematical magic that turns undigested information into life-transforming insights and advances." it promises that the course "introduces you to the key concepts, methods, and accomplishments of this versatile approach to problem solving." So as someone who knows nothing about Big Data beyond what can be picked up from the newspaper, I looked forward to deepening my understanding of how data analytics works, at least at a basic mathematical and computational level, as well as in its applications. Instead, the primary approach of the course is the presentation of example after example of how Big Data is being used. A good overview of these areas can be obtained from the course description. Most of the applications are discussed at a remarkably superficial level. It would not be exaggerating to say that almost all of this course could be understood by a reasonably intelligent middle schooler. Just a few of many examples include: - Several minutes and photos are devoted to illustrating the fact that size is relative, by comparing a large kid to a small one, and then to Michael Jordan (lecture 3). - A modified version of "Old MacDonald," including multiple E-I-E-I-Os, is used recursively to demonstrate exponential growth; the point is important, but could have been explained easily in many fewer words (lecture 5). - Literally one quarter (7 of 28 minutes) of lecture 7 is devoted to discussing the historically important but quite straightforward graph which Florence Nightingale used to demonstrate various aspects of the casualties in the Crimean War. - After a discussion of the "mail for Santa" program and the origin of the U.S. Postal Service's unofficial motto (actually of interest as a bit of ancient history), we are informed that "an important part of getting the mail delivered is knowing where it goes" (lecture 8). - In lecture 15 we learn that "statistical formulas can pop out the statistical significance of something." (I do realize that I may be accused of cherry-picking these examples, but I honestly feel they reflect the general level of the course.) There are occasional moves toward depth, but they do not get far. Lecture 17, on Google's algorithms, was for me the most interesting and enlightening of the course. Lectures 16 and 18 actually discuss a few simple matrix equations (a component of linear algebra, which is apparently an essential part of data analytics), but the description is minimal and little understanding of the underlying mathematical concepts is developed. Many categories of data analysis are certainly mentioned, including linear regression, bracketology, sentiment and text analysis, data compression, decision trees, clustering, and neural networks. But the level of explanation provides little understanding beyond what you might gather from the name of the method alone. And a great deal of lecture 22 on "Degrees of Separation and Social Networking" is given over to multiple and entirely unhelpful demonstrations of connections between movie stars (the original idea of 6 degrees of separation popularized as "Six Degrees of Kevin Bacon") and between sports figures. Professor Chartier speaks clearly and is well-organized. He is unfailingly enthusiastic about his topic - but his enthusiasm is of the unvarying, mechanical sort that you might hear when an elementary school teacher is reading to her first grade class. I found this difficult to listen to. While one might have thought a video version of the course would have a great advantage over audio only, few of the visuals actually add to the educational experience. These are mostly some simple graphs and occasional equations. The great majority of the visuals are the silly and useless professional shots of obviously posed models which TGC seems so fond of, illustrating things like a guy learning he has won the lottery (hand slapping forehead, open-mouthed grin of astonishment, wide-eyed stare at lottery ticket held in other hand.) The Course Guidebook is relatively good, all things considered, and provides nice summaries of the lectures. It has an annotated bibliography, but - astonishingly - no glossary. (Every recent course that I have seen is lacking a glossary, a major deficiency, for no good reason. I can only guess that some new TGC management type must have hated doing these in school, and is now taking it out on us.) While there is no glossary, there is a four-page appendix on "creating your own personal bracket" for March Madness. This is the most in-depth discussion of the course. So - I cannot recommend this course. If you are interested in delving into the concepts, computation, and math involved in Big Data, this is not found in any significant depth. If you are only seeking an overview of the areas in which Big Data is being used in today's world, you will find it here, but you could get almost as complete an overview by simply reading all of the information in the course description. November 19, 2014
Rated 1 out of 5 by The Inarticulate Professor I was really looking forward to learning something about this new and important field of analysis. But the instructor speaks so painfully slow, I lost interest in the first lecture. He speaks as though he is addressing a class of elementary school students, or seniors with hearing or mental limitations. That "The Great Courses" allowed this course to be published raised doubt in my mind regarding the screening process they put on their products. I won't buy another. FYI: I am an 80 year young senior with an MS in engineering, and a career rich in analysis, mathematics and the use of computers. November 5, 2014
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