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.
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
The amount of customer data available to brands has absolutely exploded in the past decade, giving brands an opportunity to create personalized experiences. But with… Read More »DSC Webinar Series – Its Time to Turn Data Chaos into Order.mp4
Data informs everything in a digital-first world. It is the fuel for enterprise intelligence that helps organizations create new business opportunities and deal with competitive… Read More »DSC Webinar Series: Building Trust in Your Data to Fuel Breakthrough Innovation
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
How to fix various data issues in a few simple steps? In this first part, I discuss missing, outdated and unobserved data, data that is… Read More »15 Data Issues and How to Fix Them
Data scientists’ time is valuable. Computing resources are expensive. With only 87% of projects ever making it to production (Source: VentureBeat), organizations often overcommit to… Read More »DSC Webinar Series: How to Gauge Project Feasibility With Rapid Prototyping Using Code-First Data Science
In a recent study, The impact of increased digitization on the data science field, the results show that data science professionals spend most of their… Read More »DSC Podcast Series: Ready. Set. Cortex – How a SAS Analytics Simulation Game Fuels Learning
