Data Science Guide is a resource for individuals and companies who want to explore how to use data mining, big data, and data science to further their business goals. We will provide tools, online resources, helpful information, coaching, and more.
"Big Data" may be an over-hyped word, but the fact is that there is real business value and real opportunity to be found in the very large data sets that are available today.
The primary objective of Data Mining is to derive knowledge from raw data. This involves using computers to discover patterns in these big data sets using techniques from machine learning, statistics, artificial intelligence, as well as traditional database analysis methods. This is increasingly important today because of the vast amount of data that businesses are now able to collect and store.
Data Science takes this one step further, by incorporating techniques and theories from many different fields to extract meaning from data. This includes building on learnings from statistics, math, engineering, machine learning, pattern recognition, visualization, computing and data warehousing, and the various scientific fields. Data scientists come from many different fields including computer science, statistics, physics, math, bioinformatics, finance, and other areas.
According to job-market research firm Wanted Analytics, job advertisements for data analytics experts have increased rapidly: up by a shocking 246% since April 2009. Also, New Vantage Partners surveyed a number of Fortune500 firms recently, concluding that 85% have already started or are planning Big Data initiatives. This growth in the field prompted Harvard Business Review to call data analytics "the sexiest job of the 21st century."
Data Science Guide aims to separate the hype from reality: find data science jobs, explore training and education options, learn how to build and hire a world-class data science team, and more.
These are just some of the questions we hope to address:
Please keep checking back to learn the answers to these questions, find key resources to build your career in data mining or to build out your data science team, and more.