The impact of biased data on applications such as artificial intelligence is not always theoretical, or even subtle. A famous example is Microsoft’s Tay. Tay was a chatbot released by Microsoft in 2016 that used AI technology to create and post to Twitter. Soon after going live,Tay began ...
Data is a collection of facts, numbers, words, observations or other useful information. Through data processing and data analysis, organizations transform raw data points into valuable insights that improvedecision-makingand drive better business outcomes. Organizations collect data from various sources a...
When it comes to the global trend nowadays - artificial intelligence and machine learning, the first thing we care about is data. A machine learning model's life starts with data and ends with the deployed model, and turns out that high-quality training data is the backbone of a well-perfo...
Data visualization is the graphical representation of information. It uses visual elements like charts to provide an accessible way to see and understand data.
“Information is the oil of the 21st century, and analytics is the combustion engine” –Peter Sondergaard, Senior Vice President, Gartner. 3V’s of Big Data If you want to understand big data then you have to understand the big data basics. The 3Vs of big data include the volume, veloc...
Data analysis is a process for collecting, cleansing, transforming, and modeling data to uncover actionable insights. Make data work for you.
What is data visualization? Data visualization refers to the practice of representing data using visual formats such as tables, charts, graphs, and maps. These visualizations elucidate the relationships between data points, helping users to spot trends, patterns, and anomalies in datasets. The prima...
2. Is it complete? Completeness of data identifies if anything is missing from the information. While data can be valid, it might still be incomplete if critical fields are not present that could change someone’s understanding of the information. Incomplete data can lead to biased or incorrect...
Pillar 1: Accuracy— the cornerstone of data quality. It refers to the degree to which the data is correct, reliable, and free from errors. An example of inaccurate data would be having a record about an individual that states they are 30 years old, when in reality they are 35 years ol...
A sampling error is a difference between the sampled value and the true population value. Sampling errors can occur during data collection if the sample is not representative of the population or is biased in some way. Because a sample is merely an approximation of the population from which it...