One thing has become abundantly clear about Big Data: it is not just a buzz word. In fact, Big Data has become the driving force behind nearly every industry as executives realize the benefits and advantages of utilizing Big Data. But, Big Data alone is not the tool that is changing business as we know it; rather, Big Data analytics is the key to better business.
Big Data analytics is what business and IT leaders are using to gather actionable insight, in the form of trends, patterns, and other valuable information, to address their company’s needs. Big Data analytics technologies and software solutions are important in the process, but data scientists, Big Data engineers, data mining engineers, business analysts, Big Data architects, and other professionals are key to using those technologies to implement the most effective Big Data analysis projects and initiatives possible.
If you or your company are in the beginning stages of harnessing the power of Big Data and want to learn more about Big Data analysis, you have come to the right place. We have rounded up some of the most informative Big Data analysis learning resources available, and we have chosen those from top experts, industry leaders, distinguished training programs, and other thought leaders to ensure they are valuable sources of information.
The following articles, guides, blogs, courses, tutorials, videos, and other Big Data analysis learning resources will help you to better understand this most important business process. Please note, while we have listed our top Big Data analysis learning resources here, in no particular order, we have included a Table of Contents so that you can jump to the resources that interest you most.
Articles and Guides
1. What Are the Prerequisites to Learn Big Data Analytics?
Venturesity offers online and offline challenges to individuals who want to showcase their skills and talents and add to their portfolio of projects in order to become more marketable. Companies from startups to enterprises utilize Venturesity to build their talent community. Because Venturesity centers on job skills, they understand the importance of learning about and becoming adept in Big Data analytics. Their article, What Are the Prerequisites to Learn Big Data Analytics?, is a great place to start learning about Big Data analytics because it gives detailed descriptions and explanations in layman’s terms.
Three key facts we like from What Are the Prerequisites to Learn Big Data Analytics?:
- Big Data analytics requires aptitude in mathematics, as it is the arithmetic of adequacy
- To work in Big Data analytics, it is helpful to have a knowledge of Hadoop, SQL, R, Python, and other programming language
- Big Data analytics is driving employment development, as individuals with data mining and machine learning methods, information visualization tools, and information warehousing knowledge and experience are required for Big Data analytics
2. Healthcare Big Data Analytics: From Description to Prescription
As part of Xtelligent Media, HealthITAnalytics provides real-world tips and news for everyone involved in healthcare analytics. Jennifer Bresnick’s Big Data analytics article for HealthITAnalytics explores the impacts of Big Data analytics on healthcare and points out that too many providers “continue to struggle to understand just how huge their big data is, not to mention how to collect and use it most effectively.”
Three key points we like from Healthcare Big Data Analytics: From Description to Prescription:
- Big Data analytics is entrenched in the healthcare industry, from alerting about drug interactions to modeling emergency department use
- Big Data analytics capabilities fall into three major categories: descriptive, predictive, and prescriptive
- Prescriptive analytics is the future of healthcare Big Data, and the Internet of Things is helping to make it become a reality
3. The Ultimate Guide to Big Data for HR
Blogging4Jobs is a Forbes-recognized HR and recruiting blog that provides insights in HR, recruiting, work, leadership, and technology. Heather R. Huhman’s Blogging4Jobs article serves as a guide to Big Data for HR, and reminds readers that considering Big Data is a key to ensuring success in talent acquisition and employee retention.
Three key points we like from The Ultimate Guide to Big Data for HR:
- Big Data helps companies control their bottom line and avoid costly employee turnover
- Big Data is useful for making hiring decisions and determining new hires’ compensation rates
- Implementing Big Data involves creating a dedicated analytics team, gamifying the hiring process, and motivating current employees
4. Guide to Big Data Analytics Tools, Trends and Best Practices
Tech Target’s resource for business leaders and business intelligence and analytics professionals, SearchBusinessAnalytics offers this guide to Big Data analytics tools, trends, and best practices. This Big Data analytics learning resource features experts’ perspectives and best practices for Big Data analytics projects, through links to various features, tips, and news sources.
Three key resources we like from Guide to Big Data Analytics Tools, Trends and Best Practices:
5. Strategic Guide to Big Data Analytics
CIO.com offers news, analysis, video, blogs, tips, and research for IT professionals. Their Big Data analysis learning resource is the Strategic Guide to Big Data Analytics, which serves as an eBook for learning how to begin “exploiting the power of Big Data analytics, which can provide your organization with a competitive advantage.” Specifically, this Big Data analysis guide shares five analytics trends, key questions for getting started, data security reminders, and the top five Big Data challenges.
Three key points we like from Strategic Guide to Big Data Analytics:
- Big Data technologies are part of a larger trend toward faster analytics
- Big Data analytics and business intelligence (BI) are going mobile, and employees and executives are taking advantage of accessing BI from anywhere, any time
- It is important to include social media in the Big Data analysis mix
6. Data Science 101
Ryan Swanstrom is a software engineer and data science blogger who pens the Data Science 101 blog. Data Science 101 is one of the most popular data science blogs on the internet, and it’s easy to see why, once you start reading some of Swanstrom’s posts. Informative and easy to understand while being full of insight and other resources, Data Science 101 is a Big Data analysis learning resource that is not to be missed.
Three posts we like from Data Science 101:
7. Facebook Research Blog
The Facebook Research Blog features posts from the researchers at Facebook. Posts feature AI research, data science, systems, and user experience. It shouldn’t surprise any of us that Facebook has teams of researchers who gather data about users and online behavior, or that they produce so much data that they are experts in Big Data analytics.
Three posts we like from Facebook Research Blog:
8. The Shape of Data
Jesse Johnson is a mathematician and software engineer at Google who authors the Shape of Data blog. His posts “explore and explain the basic ideas that underlie modern data analysis from a very intuitive and minimally technical perspective: by thinking of data sets as geometric objects.” Johnson’s goal is to show how anyone gan understand modern data analysis.
Three posts we like from The Shape of Data:
9. The Unofficial Google Data Science Blog
Kay Brodersen, Amir Najmi, and Diane Tang are data scientists at Google who share stories of interest to data scientists in posts to The Unofficial Google Data Science Blog. The goal of the authors is write a practitioners’ blog, with an intended audience of data scientists and students pursuing data science careers, making it a valuable Big Data analysis learning resource.
Three posts we like from The Unofficial Google Data Science Blog:
10. Becoming a Data Scientist
Renee M. P. Teate is becoming a data scientist, and she is writing about her adventures along the way in her blog, Becoming a Data Scientist. Teate also shares data science articles and tutorials through her Flipboard magazine and a Data Science Learning Directory, plus a second data science blog though Becoming a Scientist. This Big Data analysis learning resource is fairly new, but quickly is becoming robust.
Three key resources we like from Becoming a Data Scientist:
11. Rocket-Powered Data Science
Kirk Borne, principal data scientist at Booz Allen and a top Big Data influencer, shares his data reflections on his data science blog, Rocket-Powered Data Science. Borne’s posts cover Big Data and data science, plus some products and training information, and all of his posts showcase his Big Data analysis and data science expertise.
Three posts we like from Rocket-Powered Data Science:
12. Diving into Data
Ando Saabas blogs about applied machine learning, data mining, and visualizations at Diving into Data. Employed by Microsoft and part of a Skype/Lync data team, Saabas posts fairly regular Big Data news and information to Diving into Data.
Three posts we like from Diving into Data:
13. Big-Ish Data
Programmer Jack Schultz shares his “musings about data” on his Big-Ish Data blog. While his posts are not strictly about Big Data, he does share strategies and insight for practical results that can be scaled and utilized by those interested in learning more about Big Data analysis. While not updated as regularly as some of our other top Big Data analysis blogs, Big-Ish Data does contain detailed, in-depth tutorials on data analytics.
Three posts we like from Big-Ish Data:
14. Simply Statistics
Simply Statistics is a blog by Jeff Leek, Roger Peng, and Rafael Irizarry that covers statistics, data, and science. Leek, Peng, and Irizarry are biostatistics professors who are finding inspiration in the new era of data and data scientists. This Big Data analysis learning resource shares posts about data science, advice, and strategies, especially for future statisticians and data scientists.
Three posts we like from Simply Statistics:
15. Statistical Modeling, Causal Inference, and Social Science
Authors Andrew Gelman, Bob Carpenter, Aleks Jakulin, Phil Price, Michael Betancourt, and Aki Vehtari present their Big Data analysis blog, Statistical Modeling, Causal Inference, and Social Science. The blog is highly technical and informative and covers categories such as economics, administrative, Bayesian statistics, and statistical computing, among others.
Three posts we like from Statistical Modeling, Causal Inference, and Social Science:
16. Probably Overthinking It
Professor at Olin College and author of Think Python, Allen Downey writes the Probably Overthinking It blog. This Big Data analysis learning resource dates back to 2011 and features posts centering on statistics and data analysis.
Three posts we like from Probably Overthinking It:
17. Storytelling with Data
Cole Nussbaumer is on a mission to “rid the world of ineffective graphs, one exploding, 3D pie chart at a time!” Her Big Data analysis bog, Storytelling with Data, shares her expertise in data visualization and the experience she gained through analytical roles in banking, private equity, and at Google. Nussbaumer focuses on giving meaning to data and using it to communicate with audiences through simplicity and ease of interpretation.
Three posts we like from Storytelling with Data:
Dataconomy covers Big Data, financial tech, and the Internet of Things (IoT) while considering how data science and connected devices change technology today. A terrific Big Data analysis learning resource for Big Data news, events, and expert opinion, Dataconomy is comprehensive and always offers new Big Data analysis content.
Three posts we like from Dataconomy:
19. Data School
Kevin Markham, data science instructor at General Assembly DC and data scientist specializing in R, Python, machine learning, and Big Data, founded Data School. Data School is a Big Data analysis learning resource that is appropriate for data scientists of all knowledge levels and experience, and it especially is appropriate for beginners.
Three posts we like from Data School:
Certifications and Courses
20. Mining Massive Data Sets Graduate Certificate – Stanford University
Stanford Center for Professional Development (SCDP) delivers Stanford courses, certificates, and degrees for qualified professionals. Their Mining Massive Data Sets Graduate Certificate requires four courses, Social and Information Network Analysis, Machine Learning, Mining Massive Data Sets, and Information Retrieval and Web Search. Prerequisites include basic knowledge of computer science principles and skills at a sufficient level for writing reasonably nontrivial computer programs. A background in computer systems, AI, statistics, and database systems with familiarity in algorithms, data structures, basic probability theory, and linear algebra is helpful.
- Begin certificate in any academic quarter that a course if offered
- Master efficient, powerful techniques and algorithms for extracting information from large datasets
- Boost your career with skills that will give your company a more competitive edge
- Suited to software engineers, statisticians, predictive modelers, market research and analytics professionals, and data miners
Cost: $13,440 – $19,800 (12-15 units) to complete the certificate
21. Certification of Professional Achievement in Data Sciences – Columbia University
Columbia University’s Data Science Institute strives to train the next generation of data scientists and develop innovative technology that will serve society. They offer the Certification of Professional Achievement in Data Sciences, which prepares students to expand their career prospects or change careers by developing foundational data science skills.
- Non-degree, part-time program
- Requires a minimum of 12 credits, including four required courses: Algorithms for Data Science, Probability and Statistics, Machine Learning for Data Science, and Exploratory Data Analysis and Visualization
- Certification students who are admitted to and enrolled in the Master of Science in Data Science program will forego their certification in order to allow three of the four required courses to count toward their Master of Science
Cost: $1,782/credit + $85 non-refundable application fee
22. Big Data and Hadoop Essentials
A trusted source for online courses, Udemy offers Big Data and Hadoop Essentials. This Big Data analysis learning resource provides essential knowledge for anyone associated with Big Data and Hadoop and has enrolled more than 46,550 students. Big Data and Hadoop Essentials is best suited for beginners.
- More than 8 lectures and 44 minutes of content
- Delivers fundamental knowledge of Big Data and Hadoop
- Helps learners build an essential understanding of Big Data and Hadoop
- Instructed by Nitesh Jain, who has more than 7 years experience in the IT industry working with CMMi level 5 companies and providing services to clients in Fortune 15 companies
23. Intro to Hadoop and MapReduce: How to Process Big Data
Providing online courses and credentials, Udacity provides training in the skills that industry employers require today. Their Big Data analysis learning resource, Intro to Hadoop and MapReduce: How to Process Big Data, has welcomed more than 89,335 students and is for intermediate-level students who want to learn the fundamental principles behind the Apache Hadoop project and how to use it to make sense of Big Data.
- Learn how Hadoop fits into the world and solves problems
- Understand the concepts of HDFS and MapReduce and how it solves problems
- Write MapReduce programs to solve problems
- Practice solving problems independently
24. R Programming Language
An online learning destination for developers, Code School teaches students through entertaining content. R Programming Language is one of Code School’s Big Data analysis learning resources, and it is instructed by Jay McGavren, who was a Java developer before discovering Ruby in 2007.
- Learn the R programming language for data analysis and visualization
- Apply R to statistical computing and programming
- Includes a gentle introduction to R, vectors, matrices, summary statistics, factors, data frames, and real-world data
25. Machine Learning
Associate professor at Stanford University, chief scientist at Baidu, and chairman and co-founder of Coursera, Andrew Ng offers Machine Learning, an open course made available through OpenClassroom at Stanford. The course features 30 lectures and covers some of the most widely used and successful machine learning techniques, for those interested in learning Big Data analysis.
- Learn to implement algorithms yourself and gain practice with them
- Learn practical hands-on tricks and techniques
- Practical applications and apple machine learning take center stage in this Big Data analysis learning resource
26. Tackling the Challenges of Big Data
MIT Professional Education offers continuing education courses and lifelong learning opportunities for science and engineering professionals at any level. Tacking the Challenges of Big Data, a Big Data analytics learning resource, is a digital programs course that surveys the latest topics in Big Data.
- Data collection from smartphones, sensors, and the Web
- Data storage and processing, including scalable relational databases, Hadoop, and Spark
- Extracting structured data from unstructured data
- Systems issues including exploiting multicore and security
- Analytics such as machine learning, data compression, and efficient algorithms
27. Learning From Data
Learning From Data, Caltech’s online course, is a free, introductory machine learning massive open online course (MOOC) taught by Professor Yaser Abu-Mustafa. This Big Data analysis learning resource features 18 lectures on the fundamental concepts and techniques of machine learning.
- Simulates the pace of blackboard teaching
- Focuses on understanding, rather than “just knowing”
- Topics include linear model, error and noise, training versus testing, variation, kernel methods, and more
28. Data Analysis and Statistical Inference
Coursera offers Data Analysis and Statistical Inference from Duke University as part of the Reasoning, Data Analysis, and Writing Specialization. This Big Data analysis learning resource introduces learners to statistics as a science that involves understanding and analyzing data. Learn how to collect data, analyze data, and use data to make inferences and conclusions about the real world.
- Instructed by Dr. Mine Cetinkaya-Rundel of Duke University
- Recognize the importance of data collection, identify limitations in data collection methods, and determine how they affect the scope of inference
- Use R to summarize data numerically and visually in order to perform data analysis
- Critique data-based claims and evaluate data-based decisions
29. Data Science
Harvard School of Engineering and Applied Sciences (HSEAS) works to connect and integrate Harvard’s teaching and research efforts in engineering, applied sciences, and technology. HSEAS offers Data Science, a Big Data analysis learning resource that introduces methods for five key facets of investigation: data wrangling, cleaning, and sampling to get a suitable data set; data management to be able to access Big Data quickly and reliably; exploratory data analysis to generate hypotheses and intuition; prediction based on statistical methods such as regression and classification; and communication of results through visualization, stories, and interpretable summaries. Data Science uses Python for all programming assignments and projects.
- All lectures are available
- All lecture slides are available
- GitHub repositories contain course material
30. Advanced Data Structures
MIT OpenCourseWare offers free lecture notes, exams, and videos from MIT. Advanced Data Structures is instructed by MIT professor Erik Demaine and is a Big Data analysis learning resource that gives students the opportunity to interact with and understand data structures.
- Video lectures, lecture notes, instructor insights, assignments and solutions
- Understand the ways in which data structures serve as the building blocks in obtaining efficient algorithms
- Learn the major results and current directions of data structure research
31. Introduction to Data Science
Available through Coursera, Introduction to Data Science is a Big Data analysis learning resource from the University of Washington. Introduction to Data Science familiarizes students with the basics of data science and delivers practical experience extracting value from Big Data.
- Instructed by Bill Howe, director of research for scalable data analytics at the University of Washington eScience Institute
- Tour the basic techniques of data science, including SQL and NoSQL solutions for massive data management
- Data manipulation at scale, analytics, communicating results, and special topics
Cost: Contact for pricing
A course/Wiki, Visualization is instructed by Maneesh Agrawala, professor of computer science and director of the Brown Institute for media innovation at Stanford. Agrawala specializes in graphics, human-computer interaction (HCI), and visualization. This Big Data analysis learning resource gives students the opportunity to explore techniques and algorithms for creating effective visualizations based on principles and techniques from graphic design, visual art, perceptual psychology, and cognitive science.
- Geared toward students interested in using visualization in their own work, and those interested in building better visualization tools and systems
- Open to graduate students and advanced undergraduates
- Visualization design, exploratory data analysis, perception, interaction, color, collaborative visual analysis, identifying design principles, and more
33. Simplilearn Big Data and Analytics Training
Simplilearn Solutions is a large online certification training company that offers training courses to working professionals around the globe. They offer several courses in Big Data and analytics, covering a range of certifications and trainings that provide an in-depth overview of Big Data, data management, and Big Data analysis using various tools like SAS and R.
Three courses we like from Simplilearn Big Data and Analytics Training:
Cost: Pricing varies by course and program; Contact for a quote
34. Big Data University – Analytics, Big Data, and Data Science Courses
Big Data University offers quality education on Big Data and the UN Global Goals. Learners acquire the skills needed to change business and the world through Big Data University. Big Data U’s analytics, Big Data, and data science courses are perfect for individuals who want to being a career in data science and data engineering. These Big Data analysis learning resources cover Big Data, NoSQL, data engineering, Hadoop, Spark, Python, and much more.
Three courses we like from Big Data University – Analytics, Big Data, and Data Science Courses:
35. TM Forum Big Data Analytics
TM Forum, a global industry association, helps more than 85,000 members across nearly 1,000 companies transform and succeed in the digital economy. Their Big Data analysis learning resource, Big Data Analytics, features best practices, training and certification, use cases, and much more.
Three key resources we like from TM Forum Big Data Analytics:
Cost: Corporate A1 Membership Fees vary from $1,700 to $145,000, based on annual revenues
36. Analytics Vidhya
Analytics Vidhya is a community for learning data science, analytics, machine learning, Big Data, and data mining. As a Big Data analysis learning resource, Analytics Vidhya features a blog, links to trainings, and infographics. All of the Big Data analysis content from Analytics Vidhya is informative and contains helpful strategies, up-to-date news, and tips.
Three key resources we like from Analytics Vidhya:
Cost: FREE and paid resources are available
37. Predictive Analytics World
Predictive Analytics World is a business-focused event for predictive analytics professionals, managers, and commercial practitioners. While this Big Data analysis learning resource primarily is a conference site, it also contains articles and other resources that are useful for those interested in Big Data analysis.
Three key resources we like from Predictive Analytics World:
Cost: FREE and paid resources are available
38. Advanced Performance Institute
Bestselling author, keynote speaker, strategic performance consultant, and analytics, KPI and Big Data guru, Bernard Marr is the founder of the Advanced Performance Institute (API). API is a world-leading independent think tank and consulting organization that specializes in KPIs and metrics, Big Data and analytics, performance management, and strategy management. Their website is a comprehensive Big Data analysis learning resource containing white papers, slide decks, case studies, videos, training, and more.
Three key resources we like from Advanced Performance Institute:
Cost: FREE; Please note, some resources do require email registration to access
Tutorials, Trainings, and Videos
39. The Data Incubator
The Data Incubator trains PhDs to be data scientists. Offering data science fellowship, hiring, and training, the Data Incubator operates in New York City, Washington, DC, the Bay Area, and Kuala Lumpur. The Data Incubator site contains several Big Data analysis and data science resources, in order to prepare companies and candidates for the data science training and hiring process.
Three resources we like from The Data Incubator:
Cost: Resources are free, and other services require a fee; Contact for a quote
40. Tutorial: Big Data Analytics: Concepts, Technologies, and Applications
Hugh Watson is a professor of Management Information Systems at the University of Georgia. His Big Data analysis learning resource is a tutorial on Big Data analytics and covers its concepts, technologies, and applications. Watson recognizes that we are in the Big Data era and the value of Big Data analytics in the tutorial, and he offers explanations, examples, and a thorough analysis of Big Data analytics in this tutorial.
Three key points we like from Tutorial: Big Data Analytics: Concepts, Technologies, and Applications:
- Succeeding with Big Data analytics requires a clear business need, strong committed sponsorship, alignment between the business and IT strategies, a fact-based decision-making culture, strong data infrastructure, the right analytical tools, and people skilled in analytics
- There are significant privacy concerns relating to the use of Big Data
- The confluence of advances in computer technology and software, new sources of data such as social media, and business opportunities has created the current era of Big Data analytics
41. Online Learning for Big Data Analytics
The IEEE Big Data initiative seeks to build a community around Big Data technology. This Big Data analysis learning resource, Online Learning for Big Data Analytics, was a tutorial presentation at IEEE Big Data in Santa Clara, California, in 2013. Irwin King, Michael R. Lyu, and Haiqin Yang of the Department of Computer Science & Engineering from the Chines University of Hong Kong delivered the presentation. As a Big Data analysis tutorial, the resource includes information on Big Data and Big Data analytics, online learning algorithms, and information about Perceptron.
Three key points we like from Online Learning for Big Data Analytics:
- Big Business requires new technologies for working with data and algorithms for mining data
- Big Data analysis is being used to discover new opportunities, measure efficiencies, and uncover relationships for Big Business
- Big Data analysis provides insights from volumes of data and aims to answer questions that previously were unanswered
42. Big Data – Learning Basics of Big Data in 21 Days
A technology enthusiast and independent consultant, Pinal Dave offers this Big Data analysis resource, Big Data – Learning Basics of Big Data in 21 Days, a tutorial. The tutorial is comprised of 21 days’ worth of blog posts that focus on the basics of Big Data. With its variety of topics and information, Big Data – Learning Basics of Big Data in 21 Days provides a thorough introduction to Big Data and Big Data analysis.
Three key resourcess we like from Big Data – Learning Basics of Big Data in 21 Days:
43. ‘Big Data’ Tutorial: Everything You Need to Know
SearchStorage.com is TechTarget’s resource for data storage professionals. They offer a Big Data analysis learning resource, a Big Data tutorial, that covers Big Data technologies and architecture, vendor developments, and the challenges CIOs face when implementing Big Data in their storage environments.
Three key points we like from ‘Big Data’ Tutorial: Everything You Need to Know:
- Big Data and its technologies are evolving into real-world enterprise offerings and data center strategies
- The divide between analytics and storage is narrowing as data storage managers must design and manage Big Data infrastructures
- CIOs want to take the lead in identifying the patterns that will drive better business decisions; as such, they are increasing their companies’ Big Data skill sets by hiring data scientists, mathematicians, and information architects
44. Lynda Big Data Tutorials
Lynda, a LinkedIn company, offers skills to professionals seeking to achieve their full potential. They offer their Big Data Tutorials, a Big Data analysis learning resource, which serve as courses to show data professionals how to solve challenges with Big Data and Big Data analysis using leading IT tools and techniques.
Three courses we like from Lynda Big Data Tutorials:
Cost: FREE trial available for 10 days; Contact for a quote
45. Introduction to Big Data by Hilary Mason – Chief Data Scientist at Bitly
Global Pulse is an innovation initiative of the United Nations Secretary-General to harness Big Data for sustainable development and humanitarian action. At the time of this Global Pulse Big Data analysis learning resource recording, Hilary Mason was chief data scientist at Bitly. She since has moved on to become the founder of Fast Forward Labs, a machine intelligence research company, and data scientist in residence at Accel. In this Big Data analysis video, Mason delivers an introduction to Big Data and discusses how it has potential applications for social good.
Three key points we like from Introduction to Big Data by Hilary Mason – Chief Data Scientist at Bitly:
- Big Data and Big Data analysis has potential that has not even been tapped yet
- The definition of Big Data is evolving as technology is evolving
- The true innovation in Big Data is a human innovation, as people ask a question and find an answer through Big Data analysis
46. Big Data, Small World: Kirk Borne at TEDxGeorgeMasonU
Dr. Kirk Borne is a multidisciplinary data scientist and former professor of astrophysics and computational science in the School of Physics, Astronomy, and Computational Sciences at George Mason University. He delivers his TEDx talk and covers some of the fundamentals of data mining in this Big Data analysis learning resource available on YouTube.
Three key points we like from Big Data, Small World: Kirk Borne at TEDxGeorgeMasonU:
- Big Data is changing the world and bringing people together in connected ways
- Discovering unknown unknowns is part of the capability of Big Data analysis
- Big Data can help us determine small world phenomena
47. Kenneth Cukier: Big Data is Better Than Data
Kenneth Cukier is The Economist’s data editor and coauthor of the New York Times bestseller, Big Data. He delivers this TEDTalk on Big Data and Big Data analysis and shares the future of machine learning and human knowledge. With more than 103,000 views, Kenneth Cukier: Big Data is Better Data is one of the most popular Big Data videos on YouTube, and it jus so happens to be one of the most insightful Big Data Analysis learning resources available online.
Three key points we like from:
- Big Data doesn’t just allow us to see more; it allows us to see new things and better things and different things
- Big Data is an extremely important tool by which society is going to advance
- To really see what people want and prefer, we need Big Data analysis because it gives us a clearer picture than small amounts of data
48. What Do We Do With All This Big Data?
An industry analyst at Altimeter Group, Susan Etlinger works to help organizations make sense of their data. She also is a writer and TED Speaker who delivered this TEDTalk, What Do We Do With All This Big Data?, in September 2014. With more than 1,043,995 total views, this is one of the most popular Big Data analysis TEDTalks of all time.
Three key points we like from What Do We Do With All This Big Data?:
- Deepening our critical thinking skills is key as we receive more and more data
- Sometimes, organizations get so caught up in the data that they forget to gain a true understanding of its insights
- Data requires context and people asking hard questions in order to understand it
49. Jennifer Golbeck: The Curly Fry Conundrum
Jennifer Golbeck is a professor at the University of Maryland and a computer scientist. She focuses on social media and the ways in which people are tracked online. In her TEDTalk, The Curly Fry Conundrum: Why Social Media “Likes” Say More Than You Might Think, Golbeck advocates for the return of the control of information to its rightful owners.
Three key points we like from Jennifer Golbeck: The Curly Fry Conundrum:
- The majority of the content on the Web is put out by average users and is much more interactive
- Computer scientists have been able to build predictive models because of the amount of personal data people have put online through sites such as Facebook
- Patterns of behavior gathered through Big Data reveal insights through analysis about people, their preferences, and much more
50. Philip Evans: How Data Will Transform Business
The Boston Consulting Group (BCG) is a global management consulting firm. Philip Evans, senior partner and managing director at the BCG and co-author of Blown to Bits, delivers this TEDTalk in which he argues that Big Data is the new force that will rule business strategy. An immensely popular Big Data analysis learning resource, this video has been viewed more than 1,174,265 times.
Three key points we like from How Data Will Transform Business:
- The concept of business strategy is changing, as technology is changing
- There are two big ideas in business strategy, increasing returns to scale and experience and the value chain encompasses heterogeneous elements, are being invalidated because of falling transaction and communication costs
- Approximately half of the world’s digital information is connected via IP addresses, and the number of patterns we can see through Big Data analysis has grown exponentially
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