The importance of meeting data quality standards in the context of Machine Learning (ML)
development pipelines is explored in this study. It delves deep into why good data is crucial …

The fitness of the systems in which Machine Learning (ML) is used depends greatly on good-
quality data. Specifications on what makes a good-quality dataset have traditionally been …

Causal inference is a statistical paradigm for quantifying causal effects using observational
data. It is a complex process, requiring multiple steps, iterations, and collaborations with …

Keeping up with the research literature plays an important role in the workflow of scientists–
allowing them to understand a field, formulate the problems they focus on, and develop the …

Online Learning has been in rise with Massive Open Online Courses and many other
learning platforms. With nearly two decades of digital learning, the landscape of learning …

Sensemaking is the iterative process of identifying, extracting, and explaining insights from
data, where each iteration is referred to as the “sensemaking loop.” However, little is known …

This study presents the influence of COVID-19 and the pandemic on individuals diagnosed
with hepatocellular carcinoma and intrahepatic cholangiocarcinoma, the two most common …

Jupyter notebook allows data scientists to write machine learning code together with its
documentation in cells. In this paper, we propose a new task of code documentation …

Tabular data is ubiquitous in real-world applications and abundant on the web, yet its
annotation has traditionally required human labor, posing a significant scalability bottleneck …

Nowadays, numerous industries have exceptional demand for skills in data science, such as
data analysis, data mining, and machine learning. The computational notebook (eg, Jupyter …