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.
In the first part here, I discussed missing, outdated and unobserved data, data that is costly to produce, as well as dirty, unbalanced and unstructured… Read More »15 Data Issues and How to Fix Them – Second Part
Technical Debt describes what results when development teams take conscious actions to expedite the delivery of a piece of functionality or a project which later needs to be remediated via refactoring.
According to Forbes, data scientists spend about 80% of their time on data collection, cleansing, and preparation, while only 20% of it is left for… Read More »Data cleansing for reliable analytics and business intelligence
30-50% of businesses experience gaps between their data expectations and reality. They have the data they need, but due to the presence of intolerable defects,… Read More »What is a Data Quality Framework and How to Implement it?
Building machine learning projects can give you a much more comprehensive education about how they work.
An average consumer uses various marketing channels while interacting with a brand. The numbers were calculated in Upland BlueVenn’s latest Digital Divide Report. They examined… Read More »A Single Source of Truth: The 360 Customer View
As this century progresses, businesses are discovering that the most incredible way to gain the best customer service is to know them deeply. With AI… Read More »Enriching Customer Service Using Sentiment Analysis
Data Science is a growing field that has emerged in many key areas of our world. Data Science has become a global phenomenon and has… Read More »Education Trends 2022: Data Science in schools