Introductions To & Overviews Of Big Data  (Return Big Data Home)

Introduction To Big Data

**Big Data: A Revolution That Will Transform. . . Author Interviews

**Big Data Introduction Data Scientist Perspective (Good Overview, 22:06)

**Horizon - Age of Big Data.BBC.2013.Data Mining 

(Law Enforcement, Genetics, Financial Markets, Interplanetory Travel, Medical Treatment, Astronomy - 58:48)

Big Data Introduction Business (Hadoop) Perspective 

Big Data Data Scientist Perspective (The Hadoop Connection)

Big Data Introduction Computer Hardware Perspective

Big Data Introduction Business (Intel) Perspective

Big Data Introduction Business (Cisco) Perspective

Big Data Introduction Statistician Perspective

Big Data Introduction Computer Scientist Perspective

Big Data Literary Overviews

**The Evolution of Big Data as a Research and Scientific Topic: Overview of the Literature Bibliometrics

Big Data Overview The Economist Conference

Big Data MIT Conference Detailed Presentation of Trends

**Research Trends Special Issue On Big Data (9/2012, 40 page PDF)

Research Trends presents this Special Issue on the topic of Big Data. Big Data refers to various forms of large information sets that require special computational platforms in order to be analyzed. This issue looks at the topic of Big Data from different perspectives: grants, funding and science policy; data and computational infrastructure; arts and humanities, and bibliometrics. Prominent researchers from different institutions and disciplines were invited to write about the use of Big Data and analytics in their work, providing us with examples of tools, platforms and models of decision making processes. The Special Issue opens with an overview exploring the evolution of Big Data as a scientific topic of investigation in an article that frames the topic within the peer reviewed literature.  Other issue contributions address: (1) Grants, Science Funding & Policy, (2) Data & Computational Infrastructure, (3) Arts & Humanities, and (4)  Bibliometrics.

Big Data University Programs

Virginia Tech Division of Computational Modeling & Data Analytics (CMDA)

The CMDA program draws on expertise from four departments at Virginia Tech whose strengths are in quantitative science:  Statistics, Mathematics, Computer Science, and Physics.  By combining elements of these individual disciplines in innovative, integrated courses, with an emphasis on techniques at the forefront of applied computation, CMDA will impart a suite of quantitative skills that the workplace is demanding. The program focuses on extracting information from large data sets, as well as analyzing and solving problems by modeling, simulation, and optimization, drawing on the computational skills that make solving the complex problems of the 21st century possible. Graduates are expected to be qualified for positions in industry, business, the sciences, engineering, and more.

Salaries For Data-Savvy Professionals

                       Job Title                         Median Staff Salary                      Median Management Salary

BI/Analytics                                                  $87,000                                                           $110,000

 Data Integrating/Warehousing                $100,000                                                          $120,000

 Big Data Professionals*                               $90,000                                                          $145,000

 Data Scientists*                                          $120,000                                                          $160,000

*Sources: InformationWeek Salary Survey 2014/Burtch Works

The Ohio State University (OSU) Data Analytics Undergraduate Major

The OSU undergraduate major in data analytics is a new, interdisciplinary major in the College of Arts and Sciences. One of the first of its kind in the country, it introduces students to a diverse and rapidly growing field with data at its core. Faculty members from across the university have developed a curriculum where students explore the science behind big data. Through courses and a capstone experience, students investigate the principles of data representation and management, software design, statistical modeling and analysis, and the application of these concepts in areas such as business analytics, computational analytics and biomedical informatics.

The major is jointly administered by the Department of Computer Science and Engineering and the Department of Statistics and was developed by the Colleges of Arts and Sciences, and Engineering, and Medicine and the Fisher College of Business.

Villanova University Masters of Science in Analytics

Data Driven Evolution Of The Workplace & Marketplace

**A Face in the Crowd: Say Goodbye to Anonymity   See Leslie Stahl of 60 Minutes show us how your face is DATA, and learn how this data is being used in marketing and law enforcement.

**Pretty Pictures: Can Images Stop Data Overload? By Fiona Graham

Evolution of Technology Timeline 

David McCandless' "World's Biggest Data Breaches"

David McCandless' "Codebases"

David McCandless' "Chicks Rule?": Gender Balance On Social Networking Sites

Big Data Privacy Implications

Big Data Privacy Costs Vs. Potential Benefits

Every Little Byte Counts (News Clipping)

Researchers Study New Ways To Handle Big Data (News Clipping)

Data Visualisation Vs. Text 

  • Individuals working with visual mapping techniques used on average 19% less cognitive resources
  • They were 17% more productive and 4.5% better able to recall details than when using the equivalent traditional software
  • Groups working together on a project used on average 10% less cognitive resources
  • They were 8% more productive and recalled 6.5% more data when using visual mapping compared with traditional techniques. 

Source: Mindlab International

"The three roles that make up the complete data science triangle are:

  • Business analyst: “Working at the decision layer, this person is responsible for the final analysis and business intelligence. They present the data to the decision maker.”
  • Machine learning expert: “This person focuses on the statistics, builds data models, ensures that the data is accurate, unbiased, is easy to explain and to understand so that the analyst can interpret it effectively.” Wu counts himself in these ranks.
  • Data engineer: “This person works at the infrastructure and platform later. They ensure data quality, sale and relevance.”

Organizations that have their sights set on acquiring a data scientist in 2014 may be in for a shock, Wu indicates. The need for most will be to acquire two or three individuals from the above list, not one. At some organizations, there may even be need for more than one individual in a respective role.

Finally, Wu says the majority of those hired in 2014 to perform the above roles will probably not come from a traditional IT background."

Source: Searching For Data Scientists? They Come In Sets Of 3!  By David Welden (December 2013)

** Personal Favorites

© Andrew Nelson 2012