Data Scientists 4.0
Data Scientists 4.0 The 4th Industrial Revolution was publicly announced in 2011 at the Hannover Fair (1). Since then, many resources have been appeared around the so cal...
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Finance basics for data scientists
I picked up a little book called “Finance Basics” published by Harvard Business Review Press, for a short in-flight reading. This tiny book isn’t going to make ...
Data Scientist's Book of Quotes
Insights and Advice from Data Science Leaders and Key Influencers. Paperback – July 13, 2018. By Matt Corey. The Data Scientist’s Book of Quotes includes over 300 ins...
Data Scientist's Book of Quotes
Insights and Advice from Data Science Leaders and Key Influencers. Paperback – July 13, 2018. By Matt Corey. The Data Scientist’s Book of Quotes includes over 300 ins...
Importance of Metadata in a Big Data World
Updated from original posted on April 17, 2014 The importance of metadata only continues to grow as organizations are realizing that to fully exploit the business and ope...
Practical Apache Spark in 10 minutes. Part 3 – DataFrames and SQL
Spark SQL is a part of Apache Spark big data framework designed for processing structured and semi-structured data. It provides a DataFrame API that simplifies and accele...
3 Reasons to Learn the Expected Value Framework for Data Analysis
One of the most difficult and most critical parts of implementing data science in business is quantifying the return-on-investment or ROI. In this article, we highligh...
2018: the year data delivers
2018 is set to be the year data finally delivers for both businesses and consumers. Alex Comyn, chief strategy officer at Amaze, explores 8 key trends that are set to imp...
5 major sensor data analytics challenges: deadly or curable?
A smoothly running sensor data analytics tool may be just as difficult to manage as a symphony orchestra. Because every musician in an orchestra – and every part of an ...
DSC Webinar Series: Harness the Power of Big Data Analytics
From BI to AI, the need for Big Data and analytics is pervasive and transformational. However, Big Data technologies such as Hadoop or Spark are still quite complicated ...