This rubric covers the use of statistical tools working on large datasets to create models and derive inferences, as well as coverage of the field in its entirety. This differs from machine learning primarily in that the latter focuses on functional gradient analysis or neural networks (kernels) to derive models.
One of the main requirements for modern information systems is the high data processing rate. Among the solutions to solve this problem the popular one… Read More »Scylla vs Cassandra: Performance Comparison
Business Intelligence (BI) and Business Analytics (BA) are both used to interpret business information and create data-based action plans. The two terms are frequently used… Read More »Business Intelligence vs Business Analytics
Countless hours of online courses haven’t prepared me for challenges in my first full-time position as a data scientist. Yes, I learned Python well enough… Read More »Data Science insights after a rookie year in the industry
Data scientists are disappearing. No, not in the physical sense (no rapture here), but in the job market. The term “data science” has been a… Read More »Data Science Job Titles to Look Out for in 2020
Bayesian inference is the re-allocation of credibilities over possibilities [Krutschke 2015]. This means that a bayesian statistician has an “a priori” opinion regarding the probabilities… Read More »Naive Bayes Classifier using Kernel Density Estimation (with example)
As you might realize by now, writing SQL queries is one of the essential skills any inspiring data analyst needs to master. After all, larger… Read More »A beginner’s guide to BigQuery Sandbox and exploring public datasets.
