Steps to Learn Python for Data Science
Data science is an interdisciplinary field of scientific processes, methods, and systems. It is used to extract insights from data in many forms, either structured or uns...
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Updated: Difference Between Business Intelligence and Data Science
I’m reposting this blog (with updated graphics) because I still get many questions about the difference between Business Intelligence and Data Science. Hope this bl...
If data science is in demand, why is it so hard to get a job?
Another question recently posted on social networks. Below is my answer, with link to the original post. The main reason is the exponential growth of data science candi...
Predict ►Prescribe ►Prevent Analytics Value Cycle
Organizations looking for justification to move beyond legacy reporting, should review this little ditty from the healthcare industry: The Institute of Medicine (IOM) est...
Getting real insights from human resources management.
Data Science is a broad discipline, even though the concept is recent, every day is evolving. According to Berkeley School of Information, the Data Science Life Cycle has...
Lots of Free Open Source Datasets to Make Your AI Better
Summary: There are several approaches to reducing the cost of training data for AI, one of which is to get it for free. Here are some excellent sources. Recently we w...
Python Vs R : The Eternal Question for Data Scientists
Python and R are the two most commonly used languages for data science today. They are both fully open source products and completely free to use and modify as required ...
Harnessing the power of data to transform asset-intensive value chains
In the twentieth century, oil was the most valuable resource – but not anymore. In today’s digital age data is the new oil. It will play a similar, perhaps bigger rol...
3 Pillars of GDPR Compliance
The Forrester report “Predictions 2018: A year of reckoning” predicted that 80% of firms affected by the GDPR will not be able to comply with the regulation by the ti...
ff and Too-Big-for-Memory Data in R — Part III
After my last blog on the use of relational databases PostgreSQL and MonetDB to help compensate for R’s RAM limitations, I received an email from a reader who ask...