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)