Sampling and infrormation needs in the modern environment
The Data Science Revolution
Data comes from many sources: from the results of water analysis to the time, date and location of a customer complaint. The ability to turn data into information is determined by:
- the skill of wrangling data (also making sure the data is in an appropriate electronic format that can be used in the first place!)
- checking the data is not random and maximising its value; and
- delivering the findings in a format that is accessible and can be clearly understood by the audience.
This, in essence, is "Data Science". Data science is used increasingly to drive the decision-making process. The value of data is growing, with digital communication it is more readily collected, but it is also becoming easier to apply data science to extract understanding, insight and knowledge. This capacity to explore data has been enhanced in the last decade with innovative assessment methods, especially ones aimed at big data, and in new ways of publishing the information that are reproducible and increase workflow. Amazingly the best tools to apply data science are available through open source (free) software.
This presentation will explore the relevance of the data science revolution to water and waste management demonstrating its challenges and opportunities, from sampling information to lab derived results, introduce some of the open source and "best" tools to apply data science; with the aim of adding value to the business.
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This is a free OnDemand session
Steve Markham (Marquis & Lord)
Steve Markham is Director and Data Scientist at Marquis & Lord Ltd. He became a consultant as a post-graduate; developing and applying software applications to water management. His experience in mathematics led to developing expertise in data analysis and he now has over 30 years experience as a consultant for the commercial and public sectors. During the last 10 years, he has applied data science (statistical analysis and data management) using open source software (particularly the R environment) to a range of investigations; giving meaning to data and informing the decision-making process. BSc - Civil Engineering - University of Newcastle upon Tyne 1982 MSc - Water Resource Systems Engineering 1986 He is a Fellow of the Royal Statistical Society.
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