Posts tagged ‘Data analysis’

March 21, 2013

What does the explosive growth of data mean for college students and job-seekers?

by Grace

Job opportunities continue to grow in the increasingly important field of data analysis.

The terminology can be confusing, so I turned to Wikipedia for help.

Analysis of data is a process of inspecting, cleaning, transforming, and modeling data with the goal of highlighting useful information, suggesting conclusions, and supporting decision making. Data analysis has multiple facets and approaches, encompassing diverse techniques under a variety of names, in different business, science, and social science domains.

Data mining is a particular data analysis technique that focuses on modeling and knowledge discovery for predictive rather than purely descriptive purposes. Business intelligence covers data analysis that relies heavily on aggregation, focusing on business information. In statistical applications, some people divide data analysis into descriptive statistics,exploratory data analysis (EDA), and confirmatory data analysis (CDA). EDA focuses on discovering new features in the data and CDA on confirming or falsifying existing hypotheses. Predictive analytics focuses on application of statistical or structural models for predictive forecasting or classification, while text analytics applies statistical, linguistic, and structural techniques to extract and classify information from textual sources, a species of unstructured data. All are varieties of data analysis.

Data integration is a precursor to data analysis, and data analysis is closely linked to data visualization and data dissemination. The term data analysis is sometimes used as a synonym for data modeling.

Alexander Furnas tells us that although data mining is “quite complex”, it’s also “quite comprehensible and intuitive”.

To most of us data mining goes something like this: tons of data is collected, then quant wizards work their arcane magic, and then they know all of this amazing stuff. But, how? And what types of things can they know? Here is the truth: despite the fact that the specific technical functioning of data mining algorithms is quite complex — they are a black box unless you are a professional statistician or computer scientist — the uses and capabilities of these approaches are, in fact, quite comprehensible and intuitive.

For the most part, data mining tells us about very large and complex data sets, the kinds of information that would be readily apparent about small and simple things. For example, it can tell us that “one of these things is not like the other” a la Sesame Street or it can show us categories and then sort things into pre-determined categories. But what’s simple with 5 datapoints is not so simple with 5 billion datapoints.

Data Crunchers Now the Cool Kids on Campus, according to a Wall Street Journal article that focuses on the field of statistics.

Universities have been turning out more students with stats degrees, though the totals remain small. U.S. universities conferred nearly 3,000 bachelor’s, master’s and doctoral degrees in statistics in the 2010-2011 academic year, with increases of 68%, 37% and 27%, respectively, from four years earlier, according to the federal National Center for Education Statistics. (The numbers don’t include degrees in biostatistics and business statistics.)

A positive jobs trend

In a still-soft jobs market, rising demand for statisticians also has spurred interest in the field. There were 28,305 postings for jobs in statistics, analytics and, in the trendy phrase, “big data” at the jobs website icrunchdata last month, up from 16,500 three years earlier, according to Todd Nevins, a site co-founder.

Career paths can take various routes, as these examples show.

  • Data mining: The physicist who became a data scientist . . . a doctorate in physics
  • Data visualization: The admissions officer who turned into a data wonk . . . master’s degree in higher education
  • Data analysis: The marketer who hacks code . . .  former journalism major
  • Data manipulation: The artist with the spreadsheet tattoo . . . Trained as an artist
  • Data discovery: The geek who joined the lawyer’s nest . . .  background is as a network/systems administrator

Data scientists need math skills.

“It’s never been a better time to be a data scientist,” known in the industry as quantitative jocks,says John Manoogian III, co-founder and chief technology officer at 140 Proof. “Companies want to turn this data into insights about what people like and what might be relevant to them, but they need very specialized analytical talent to do this.”…

The field has “exploded” the last 18 months, yet there is a dearth of talent because the job requires math skills that college graduates often lack, says Jim Zimmermann, director of Skillsoft, which provides online learning and training.

Lacking potential recruits, companies are “forced to home-grow their own talent … through online training,” Zimmermann says.

Related:  Is data analytics the new ‘plastics’? (Cost of College)

September 29, 2011

Is data analytics the new ‘plastics’?

by Grace

In “The Graduate”, recent college graduate Ben receives advice from an old family friend.

Mr. McGuire: I just want to say one word to you -just one word.
: : : Ben: Yes sir.
: : : Mr. McGuire: Are you listening?
: : : Ben: Yes I am.
: : : Mr. McGuire: ‘Plastics.’
: : : Ben: Exactly how do you mean?
: : : Mr. McGuire: There’s a great future in plastics. Think about it. Will you think about it?
: : : Ben: Yes I will.
: : : Mr. McGuire: Shh! Enough said. That’s a deal.

Is data analytics the new plastics?  If so, even traditionally math-phobic marketing students may be forced to confront mathematics and statistics courses.

Faced with an increasing stream of data from the Web and other electronic sources, many companies are seeking managers who can make sense of the numbers through the growing practice of data analytics, also known as business intelligence. Finding qualified candidates has proven difficult, but business schools hope to fill the talent gap.

This fall several schools, including Fordham University’s Graduate School of Business and Indiana University’s Kelley School of Business, are unveiling analytics electives, certificates and degree programs; other courses and programs were launched in the previous school year….

Data analytics was once considered the purview of math, science and information-technology specialists. Now barraged with data from the Web and other sources, companies want employees who can both sift through the information and help solve business problems or strategize. For example, luxury fashion company Elie Tahari Ltd. uses analytics to examine historical buying patterns and predict future clothing purchases. Northeastern pizza chain Papa Gino’s Inc. uses analytics to examine the use of its loyalty program and has succeeded in boosting the average customer’s online order size….

Fordham this fall will introduce a required analytics course—Marketing Analytics —for M.B.A. students on its marketing track. “Historically, students go into marketing because, they ‘don’t do numbers,'”said Dawn Lerman, director of the business school’s Center for Positive Marketing. But these days, with so much data available surrounding consumer behavior, “you can’t hide from math and statistics and be a good marketer.”

Three more words:  21st Century Skills


As the use of analytics grows quickly, companies will need employees who understand the data. A May study from McKinsey & Co. found that by 2018, the U.S. will face a shortage of 1.5 million managers who can use data to shape business decisions.

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