Big Data: How Data Analytics Is Transforming the World

Course No. 1382
Professor Tim Chartier, Ph.D.
Davidson College
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46 Reviews
65% of reviewers would recommend this product
Course No. 1382
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What Will You Learn?

  • numbers Learn how to put "big" data to work in your own life - from your cell phone use to your exercise routine.
  • numbers Delve into artificial intelligence and discover how computers are programmed to think and make decisions like humans.
  • numbers See how a mathematical approach called bracketology can help you pick winners during March Madness.
  • numbers 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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Also By This Professor


Big Data: How Data Analytics Is Transforming the World is rated 3.7 out of 5 by 46.
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
Rated 5 out of 5 by from Good Survey of the Data Analytics Arena This is a very good survey course on how data is analyzed and used. The section on Networks was very interesting and a separate course could be developed in this area. I was impressed with the professor's background and research studies. I teach university classes in data analysis and modeling and found that the approach used in this course provides a solid background in the field and stimulates further exploration and research in specific areas of interest. The material on data security is especially informative. I served as a internet privacy manager in a corporation and believe that the introduction provided in the course is thought provoking.
Date published: 2015-05-20
Rated 2 out of 5 by from Simplified so far as to be totally uninformative I gleaned a couple of things, but for someone with a computer science background, this wasn't really worth the time spent. I wouldn't recommend it for someone _without_ a computer science background, because the explanations of some CS fundamentals are a bit dubious, and might confuse more than elucidate (e.g., binary number prefixes are normally powers of 1024, while 1000 is only used in special cases; binary search relies on sorted data, so to search through a book, it would first need to be sorted). I'm sure the lecturer just wanted to make the content very accessible, but I would have appreciated more rigor, and in a Big Data lecture I was hoping for thorough coverage of MapReduce!
Date published: 2015-04-12
Rated 3 out of 5 by from Not for a Possible Big Data Professional to Be So far the course appears to be aimed at not very smart high school students. Certainly anyone who has a programming course with any introduction to algorithms will find this course to be incredibly slow, condescending and unfortunately somewhat boring. The time spent to explain how to sum an arithmetic series or a quick sort is appropriate for someone very young or someone who would never make it as a professional in big data. Also as regards his presentation style, have you every heard a speech where every word is important and emphasized? The result is that no word is important. Unfortunately Professor Chartier emphasizes almost every word. I would suggest that he watch the presentations by Professor Laird Close in his course "Life in Our Universe". Professor Close's lectures are packed with information and his laid back presentation style is very effective. Professor Chartier's style appears more appropriate for admonishing a young child. In summary, it is a shame that Professor Chartier, who is very smart, has chosen to aim his course at such a low level. Some of the courses at the Great Courses have 2nd editions. I would suggest that Professor Chartier and the Great Courses consider one. However if they do - all parties should make sure that the 2nd edition really is significantly better.
Date published: 2015-02-25
Rated 3 out of 5 by from Big Data for Ball Games I expected some on manufacturing, retailing but nothing. Breeze thru some IT but not significantly interesting or informative, lack substance. All bones no meat. Very disappointed. OK. Not as good as I have hope.
Date published: 2015-02-21
Rated 5 out of 5 by from Timely and Engaging Course This course started off a bit slow for me and I skipped a few lectures near the beginning, but from about the seventh lecture onwards I was very engaged with the material. The course provided much useful context for recent developments in the technological world that I had vaguely heard of but knew little about. It has stimulated me to explore data analytics in more depth and perhaps use it in my work. Highly recommended.
Date published: 2015-02-12
Rated 5 out of 5 by from Fantastic view of data analytics This course was really timely for me because I'm learning data analytics for my work, and it's an area I've been interested in. This course is really great at giving a great overview on how analytics are being used and the many areas it's being used in. The professor expresses enthusiasm and makes the content interesting with the knowledge he has. It's obvious he has experience in the field. I highly recommend this course to anyone interested in data analytics. When you are done with this course, watch Mathematical Decision Making: Predictive Models and Optimization, as it's a perfect companion!
Date published: 2015-02-08
Rated 5 out of 5 by from Big Data Big View This course brings the Big view of Big Data in to focus. The ubiquitous nature of data being messaged into information and finally into directions on how to efficiently construct or lives is tantamount to the dream of converting base metal into gold. One cannot take this course and walk away feeling uninformed. Immediately following the course one can see data everywhere... Big Data!!! Big Data is Gold Odell Green
Date published: 2015-02-08
Rated 2 out of 5 by from Too Basic for the Informed Viewer Unless you've been living under a rock for the past 10 years, I must agree with other reviewers that you will not learn anything you don't already know about data management and analysis in this 24 lecture offering. While I give the lecturer, Tim Chartier, high marks for his enthusiastic presentation skills, there's simply not enough meat in this course for the informed viewer which is, for the most part, taught at the 35,000 foot level. More than once during my review of the course lectures, as Dr. Chartier made one obvious point after another, I caught myself saying "So what?" With a couple of exceptions, most of the course topics are severely lacking in concrete examples. The exceptions? Lecture 16 on NCAA basketball Bracketology and lecture 20 on Decision Trees are fairly interesting with good examples. As a alternative I highly recommend Scott Stevens' course Mathematical Decision Making, which contains dozens of examples of data analysis and manipulation that have a direct impact on the business world.
Date published: 2015-02-08
Rated 3 out of 5 by from Too many examples and not enough analysis I bought this course hoping for a good review of data analytics. Fortunately TC subsequently released Mathematical Decision Making which addresses this issue. Big Data, though interesting, makes it's points early on (eg the gigantic size of the data pool, the applications of big data, etc) and then one has to listen to one lecture after another on big data application in the real world: sports, health, etc. The course is not bad, but 24 lectures was far too long. All the points could have easily been made in 12 lectures. The professor is enthusiastic and very knowledgable about his field. Many examples of how big data are used in business, sports, healthcare, etc are given, but rarely do we ever find out just how this data is manipulated to arrive at the specific conclusions. The highlight of the course for me is the lecture where the professor tackles how handwriting can be analysed by big data. In this particular lecture, he actually zooms on the individual alphabets and describes how the computer determines what exactly is that letter likley to be. Overall, I did not find this course very useful, and frankly too long. I tried to watch it a second time but it was not that stimulating. For those of you wanting a course on data analytics, try Mathematical Decision Making which actually describes how big data is used in multiple regression, time framing, etc.
Date published: 2015-01-26
Rated 1 out of 5 by from Don't waste your money This has to be the worst Teaching Company course I have ever bought. The professors style, he talks very slowly and makes exaggerated unnatural gestures are distracting. The content is incredibly elementary, if you know what a database is or have had the most basic stats course you will be bored silly. The first few lectures can be summarised as 'gee, isn't big data amazing' at no point are we actually given any insight into how to analyse data. A total disappointment. I will be asking for my money back.
Date published: 2015-01-04
Rated 2 out of 5 by from Read a book about Big Data instead Just so you know where I'm coming from, I've got a Bachelors in Computer Science and a Masters in Industrial Engineering. I also did some data mining about 15 years ago. Since Big Data is kind of the next generation of data mining and is a hot field right now, I figured I should buy the course to see what's recently been going on. Of the 30 or so Great Courses that I've viewed and listened to, I'd rate this one third or fourth worst. I didn't expect a highly technical course, but this was so elementary as to be monotonous. Several lectures in, I started thinking about asking for a refund, though I never did and ultimately made it through all 24 lectures. (I read the notes and watch each lecture several times.) It does get more substantial about ten lectures in, but those were still not very deep. The professor has a strange lecturing style. He talks rather slowly, and makes exaggerated, unnatural gestures to emphasize what he's saying. The speech and gestures weren't quite in sync, and that made the video very difficult to watch. And while it's a nit, his continued use of the word "codes," as in "I wrote computer codes," instead of "I wrote computer code," also drove me up the wall. While I learned a few things, I feel it wasn't worth the time and money spent. Instead of purchasing this course, I would recommend that people instead buy a book on Big Data.
Date published: 2014-12-30
Rated 1 out of 5 by from GREAT!, if you never heard of "Big Data". If you have been working at least 12 months in Data, Statistics, then skip out on this DVD. It is very elementary & vague. Dont expect specific case studies & methodology. Very high-level for CEO/CMO/College Freshman that lack experience in this area. Data people, skip this item.
Date published: 2014-12-27
Rated 5 out of 5 by from 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.
Date published: 2014-12-09
Rated 5 out of 5 by from 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.
Date published: 2014-12-02
Rated 2 out of 5 by from 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.
Date published: 2014-11-19
Rated 1 out of 5 by from 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.
Date published: 2014-11-05
Rated 3 out of 5 by from First 6 or 8 lectures too basic and repetitive A full lecture on association and causation is over kill Latter lectures much better Good presenter though clearly reading. The whole course can be much improved by trimming content and de-emphasizing sport as the ubiquitous example and finding other examples from the world of science, politics etc
Date published: 2014-10-30
Rated 5 out of 5 by from HIGHLY RECOMMEND this course I am currently in a career transition and was looking for a comprehensive overview of the big data and predictive analytics field. This course was outstanding! The information was presented in a way that was both engaging and easy to understand. The examples brought to life the application of big data, statistical methods and its use in business. I was impressed with the breadth of the topics covered and they built nicely upon each other. I would HIGHLY RECOMMEND this to anyone wanting to quickly learn about the exciting ways that big data and analytics will change our world.
Date published: 2014-10-06
Rated 5 out of 5 by from Great purchase I am digging into Sports and analytics at the high school level and these videos have helped me better understand the world of data analytics. This is a quickly growing industry and one of the most important aspects of sports and marketing. The true value in the videos of this series that I have watched to date is understanding the massive industry of data analytics and how to begin using data points to bring more value to my business and everyday life. Thanks for sharing this series with the public!
Date published: 2014-10-06
Rated 3 out of 5 by from Big Data, Slim Content Video Review: The Great Courses markets this as: "A Course for Data Users at All Levels". Unfortunately, that isn't the case. The course is listed under topic "Science and Mathematics" and sub-topic "Mathematics". Yet there is very little mathematics in this course. In fact, it is as if the instructor is purposely trying to avoid presenting too much mathematics on the assumption that the audience can't handle it. I suspect that he is being coached by The Great Courses in this regard as he seems to struggle at times to hold back an urge to present more depth in his presentation. I find this disappointing as it was clear from feedback in a customer survey that customers wanted more depth in TGC's science and mathematics courses. Anyone with any significant background in the sciences, mathematics, or computer science is already likely a user of data analytics at or near the level of this course. The first dozen or so lectures of this 24 lecture course cover some very basic concepts devoting a long time to their explanation. For example, and entire lecture is spent to explain that "correlation does not equal causation" and 20mins of another is used to explain "Garbage In Equals Garbage Out". Not that these concepts aren't important, but clearly the time to explain these rather simple and obvious concepts could be compressed. The content of the course as it stands now, could easily be compressed into 18 or fewer lectures. The real meat of this course finally comes through in the last nine lectures. Here is where Dr. Chartier explains (conceptually) how various data algorithms work such as: Google's search engine link analysis, text searching and analysis, data compression and recommendation analyses, decision trees, clustering/ranking, and degrees of separation/social networks. The conceptual explanations are great, but Dr. Chartier misses an opportunity to put more meat on the bone by not explaining the mathematics and statistical analyses behind these further. My recommendation to make this a truly great course would be to shorten the first half of the course considerably and add more depth to the algorithms and math in the later half. Dr. Chartier uses sports examples such as Oakland A's "money-ball", NCAA Final Four basketball "bracketology", and Davidson basketball team performance analyses. Apparently this is Dr. Chartier's area of research. I'm a sports fan, and these are interesting, but they don't present a fair representation of the real world problems that data analytics is being used to solve daily. For awhile, I thought that this was course was an adaptation of a "Math for Jocks" course that enabled the NCAA Division I athletes at Davidson to satisfy their mathematics requirement without having to really take math. But since no such course exists on the Davidson website, I suspect this emphasis is more a reflection of Dr. Chartier's own research area. As someone who spent an entire career in the electronics and computer industry, I think there are much better examples of the utility of data analytics for "Transforming the World" than sports statistics. To his credit, Dr. Chartier does mention several of these: Political Polling, the Human Genome Project, and a new Materials Genome Initiative, but with far less emphasis. Of course, he also shows several examples using social media (Twitter and Facebook) which have become business and cultural tools. A "March Mathness" appendix is included in the Course Guidebook to show the math behind "Bracketology" of NCAA basketball's March Madness. The math depth here is a welcome addition but similar mathematics should be included in the lectures and/or course guidebook for other algorithms introduced in the course.. Despite some of the above comments, I found Dr. Chartier to be a capable presenter who obviously has a passion for the subject. He does tend to read from the teleprompter but does give appropriate emphasis and inflection to his points. This course does seem to be geared toward "Liberal Arts" majors. I did major in physics, but I have a liberal arts degree and I am a trustee of a Liberal Arts college (from which TGC has drawn instructors).. Any definition of Liberal Arts instruction includes teaching mathematics and science. So why the trepidation on TGC's part to have their instructors deliver mathematical substance in a math/science course like this one? Lifelong learners who chose to take a course on "Big Data" can handle more math. I have taken several TGC math/science courses and this one seems to be "dumbed down" more than most. This is a pity given that this course was produced in 2014 following lots of customer survey feedback for more rigor in science and math courses. A good example of a TGC course which includes science and math appropriate for "Users at All Levels" is Mark Whittle's Cosmology course. Dr. Whittle gives a heads up before lectures that are more mathematically challenging and includes a detailed guide to the mathematics in the course guidebook. I would have liked to see this Big Data course more along that model. Out of more than 40 TGC courses in history, philosophy, science, social science, etc, I have taken, this is the first one that I cannot say I would recommend to a friend as most of my friends would understand much of this course's content already. Any of the people I know who have an educational background (or are getting one) or work in physical sciences, biological sciences, engineering, economics, accounting or finance would not find much new in the content of this course until perhaps the last 9 or so lectures. However, folks whose background is in marketing, political science, journalism, or humanities may find much more of this course new to them and would find it worthwhile. I guess I could also recommend this course to folks who are sports gamblers involved in things like Fantasy Football or March Madness office pools (but don't expect to get an edge).
Date published: 2014-09-15
Rated 2 out of 5 by from didn't learn much Review of Prof. Chartier's 'Big Data (2014)'. I have a lot of background in math and computer science and 20+ years of work experience in the field. I seldom order science courses as I figure I got my fill of those in school. (Normally I order history courses and I've really enjoyed most of the 20+ history series I've watched so far.) I decided to try this Big Data course however because it had a lot of new-sounding topics of interest that I did not take in school. I thought a professor might provide deeper insight than the various online articles or blogs I'd read on these subjects. Unfortunately I didn't find this to be the case. The series got off to an irritating start, telling me about the terabytes, petabytes and exabytes of data out there. There were weak examples such as a study of someone's cell phone data and finding the lack of calls from 3am-10am consistent with when he slept. In Lecture 3, I started fast-forwarding, something I'd never done before in a TTC course. I ended up skipping over a lot of Lectures 3 through 8, which seemed to cover topics I already knew, such as quadratic running time and database keys. I found the discussion of baseball analytics to be off the mark, overly emphasizing the "Pythagorean expectation formula" as a key to the success of the 2002 Oakland A's. The prof often repeated claims from industry uncritically, such as that neural networks have been used "to time when to buy and sell stocks" (quote from page 77 of the guidebook). The explanation of neural networks was not sufficient to understand why they might work. To say something positive, I thought the lecture on overfitting was the best one, with a good example of how it may have led to underestimating the likelihood of the 9.0 earthquake that caused the Fukushima nuclear disaster. Overall, if you already have some familiarity with a lot of the topics, I don't think you will get much out of this series.
Date published: 2014-08-30
Rated 4 out of 5 by from Left wanting a bit more This course is not intended to present detailed 'how to' algorithms to technically savvy students. The majority of the course can be appreciated by many high school students. There are some discussions using matrix algebra in a few lessons, but it is not enough to discourage those with limited math backgrounds. Here is my summary of the 24 lessons: Pros - The professor is a very smooth and engaging presenter. His skills in data analysis/manipulation are obvious. I suspect his more rigorous #i.e., hands-on applications# college course would be quite enjoyable for a retired technical person such as myself. These 24 lessons get progressively more interesting, particularly the last 10 or so. For example, I found the lessons on political polling, data compression, degrees of separation and privacy to certainly be worthwhile. Cons - Perhaps half of the course material was quite generic, especially the beginning lessons. It could have been condensed without compromising much of the groundwork he intended. If the course content was moved a bit more toward a college course, it would probably be somewhat more satisfying. #I am presuming that many of the technical Great Course purchases are made by people with BS degrees, at the minimum??# I will be interested to see what future courses are offered by this excellent instructor.
Date published: 2014-08-30
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