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Big Data: How Data Analytics Is Transforming the World

Investigate the information revolution-which is changing everything from marketing to medical care-and learn how to put big data to use in your business and personal life.
Big Data: How Data Analytics Is Transforming the World is rated 3.7 out of 5 by 48.
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Rated 3 out of 5 by from Very bad data subject I don't know where be got this info and definition on big data.but the gov used it in psychology and got burned with it trying to make avpsyche case and it exposed massive execution initiated by them
Date published: 2021-05-06
Rated 5 out of 5 by from Great overview This is the review I needed. It provided more breadth than depth. Well done.
Date published: 2020-12-05
Rated 5 out of 5 by from Practical Information I was very happy with the course material. Well worth the time for learning about the importance of data analysis in the world today.
Date published: 2020-06-28
Rated 5 out of 5 by from Excellent This is an excellent survey of big data for those who are not in the field. I suspect that it would not be useful for someone who is already in the field. However, for the rest of us who are always hearing the terms (“big data” and “analytics”), it adds meaning and insight into what the experts are doing and saying. This course tackles the impossible task of explaining the advanced technical fields of “big data” and “analytics” without using technical tools such as mathematics. Somehow, it succeeds. I came out of the course with a better understanding of what Google does, how analytics affects sports (e. g., the movie “Moneyball”), and how the internet knows more about me than I thought it did. Dr. Chartier has a good speaking style. He presents a very complex subject in terms that a layperson can understand. I used the video version. I think that the visual aids are important to the course.
Date published: 2020-04-02
Rated 5 out of 5 by from A broad survey with just enough depth I bought the course mostly because the term "Big Data" seems to be everywhere. As it turned out, much of what I had done professionally as an analyst in financial services fell within the content, but the course brought in so much more (e.g. social network analysis) AND opened a window for me in terms of how the various tools can work together. It really whet my appetite for further study. The pace and level of detail was just right, and the professor's enthusiasm for the topics came through on every lecture.
Date published: 2020-02-07
Rated 4 out of 5 by from Good Introduction to Important Subject Matter Prof. Chartier does an admirable job of introducing this important subject matter to a broad audience through this course. His topics are well-chosen, and his examination of the topics is interesting and thought-provoking. That said, Prof. Chartier's sing-song lecturing voice and his frequent stumbles in reading the teleprompter diminish his effectiveness and result in a lower score for the course overall. I also share the view of other reviewers who have complained that his examples are overly focused on sports scenarios; it would have been nice for him to drawn from a broader range of applications. Nonetheless, this is a worthwhile course (even though it is now almost six years old), and I recommend it despite these flaws.
Date published: 2019-11-08
Rated 5 out of 5 by from Great Course!!! I really enjoyed this course! Professor Chartier is very articulate and engaging throughout the course. Before embarking into the mathematical mechanics of Data Science I would recommend all new students and data analytics enthusiasts to take this course first.
Date published: 2019-02-24
Rated 3 out of 5 by from It was just ok for me I was on the fence for quite some time about whether or not to purchase this course. Even though data analyst was never my job title, I did do quite a bit of data analysis over a 30 year period. I think my overall opinions of this course is somewhat of a mixed bag. Certainly there is some good information for a novice in this area, but very limited new information for people who have worked with data- until maybe the last 6 lectures or so. I think the customers would get a clearer picture of the course content by calling it “An Introduction to Data Analytics”. The title used set me up for a much higher expectation for the content than what was delivered. I’d say something like the first 16 to 18 lectures were fairly boring to me. I was more interested in the later lectures on topics, such as how companies such as Google, Netflix, Amazon, and Pandora attempt to use algorithms to improve recommendations or how policing agencies try to use data analysis to improve their job effectiveness. However, my own experience with recommendations from many such companies have been incredibly lackluster and frustrating. I also recently watched a documentary somewhere that showed how analysis on data from sources such as the internet or security cameras have produced many false “leads” for the police and FBI, and clearly many crimes still go unsolved. Honestly, I don’t know if all of this mathematical massaging of data will ever provide perfect or even good solutions for many applications (particularly when relying on passive data). Some situations are better suited for data analysis than others, and putting too much confidence in data analysis can perhaps cause more problems than help in many situations. But the professor is very clear about the limitations of data sets and analysis techniques, and stresses that number crunching alone won’t lead you to good results. I am a huge fan of using real world examples in lectures. However, not being much of a sports fan, I felt there was too much focus on sports data. He does use non-sports examples, but there was just way too much focus on sports, especially in the earlier lectures. If you love sports, you probably wouldn’t notice this. The professor did stumble around his words quite a bit and had somewhat of an awkward cadence. Perhaps he was not comfortable in presenting in front of a camera. There was also some noticeable dubbing. While this was somewhat distracting at times, he isn’t the worst I have ever seen at the TGC or elsewhere. At the end of the day, this course is probably best suited for those interested in how data is used, have some technical background, but no or little experience with data analysis.
Date published: 2019-02-03
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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. 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. See the big picture of big data, and the crucial role of data analytics in today's world. You need no expertise in mathematics to follow this exciting story. Learn the basic computational techniques used in data analytics, but his focus is on how these ideas are applied and the amazing results they achieve. With Big Data, you discover tools that are transforming the world and that you can use to transform your own life.


Tim Chartier

Within the bits and bytes lies great potential to understand our past and predict future events. And this potential is being realized. Organizations of all kinds are devoting their energies to combing the ever-growing stores of high-quality data.


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.

By This Professor

Big Data: How Data Analytics Is Transforming the World
Big Data: How Data Analytics Is Transforming the World


Data Analytics-What's the

01: 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.

36 min
Got Data? What Are You Wondering About?

02: 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....

32 min
A Mindset for Mastering the Data Deluge

03: 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....

32 min
Looking for Patterns-and Causes

04: 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.

29 min
Algorithms-Managing Complexity

05: 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.

31 min
The Cycle of Data Management

06: 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.

29 min
Getting Graphic and Seeing the Data

07: 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.

28 min
Preparing Data Is Training for Success

08: 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.

30 min
How New Statistics Transform Sports

09: 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.

33 min
Political Polls-How Weighted Averaging Wins

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.

32 min
When Life Is (Almost) Linear-Regression

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.

30 min
Training Computers to Think like Humans

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.

31 min
Anomalies and Breaking Trends

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.

32 min
Simulation-Beyond Data, Beyond Equations

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.

30 min
Overfitting-Too Good to Be Truly Useful

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.

31 min
Bracketology-The Math of March Madness

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.

31 min
Quantifying Quality on the World Wide Web

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.

30 min
Watching Words-Sentiment and Text Analysis

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.

34 min
Data Compression and Recommendation Systems

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.

33 min
Decision Trees-Jump-Start an Analysis

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.

31 min
Clustering-The Many Ways to Create Groups

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.

32 min
Degrees of Separation and Social Networks

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.

31 min
Challenges of Privacy and Security

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.

33 min
Getting Analytical about the Future

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!

35 min