In this dynamic era, the freelance economy is experiencing an unprecedented boom, significantly reshaping the work landscape. This shift is leading to the increasing prominence of freelance management, which includes sourcing, coordinating, and retaining independent talent in a strategic manner. This article particularly focuses on how to manage a freelance data science team, a trend […]
Which Data Quality Issues Are Plaguing Data Engineers Today?
We’ve all generally heard that data quality issues can be catastrophic. But what does that look like for data teams, in terms of dollars and cents? And who is responsible for dealing with data quality issues? To get to the bottom of these questions and more, we conducted a survey of 100 survey respondents, at least 63 […]
Assumptions in Regression: Why, What, and How
“Garbage in, garbage out” defines the importance of data in data science or machine learning in a nutshell. Incorrect input will yield meaningless results and screening data ensures we get comprehensible results. Before we start building models and generating insights, we need to ensure that the quality of the data we are working with is […]
Why Geospatial Data Should Be Easily Accessible for Every Employee
Unlocking the power of geospatial data can give organizations a competitive edge, from optimizing supply chain logistics and enhancing customer experience to mitigating fraud and improving public health outcomes. But despite its far-reaching benefits, many organizations fail to fully harness geospatial data’s potential. Why? Because geospatial data is voluminous, complex, and often distributed across multiple […]
Five Must-Have Characteristics of Extraordinary Data Scientists
There’s no better time than right now to be a data scientist. Despite recent large-scale layoffs in major tech firms, the future is bright for data managers, analysts, data wranglers, and consultants. In fact, the number of jobs requiring Data Science skills is expected to grow by 27.9% by 2026, according to the U.S. Bureau of Labor […]
Why Leveraging Unstructured Data and AI Will Be Key to Customer Success in 2023
Analysts like IDC and Deloitte estimate that up to 80% of the world’s data is unstructured text data, which makes getting valuable insights out of this type of data a huge challenge. Worse, customers can’t easily find the right answers to address their product and service-related questions that are hidden in large amounts of support documents. As a result, employees […]
How to Build a High-Performance Analytics Team
When most people think of analytics, they tend to picture a rigidly left-brained data scientist more at home with computer programming than creativity. But analytics leader and author John K. Thompson challenges such a notion. During his keynote presentation at DATAVERSITY’s Enterprise Analytics Online event, he proposed instead that the members of an analytics team can be […]
Why Data Quality Problems Plague Most Organizations (and What to Do About It)
For business leaders to make informed decisions, they need high-quality data. Unfortunately, most organizations – across all industries – have Data Quality problems that are directly impacting their company’s performance. Case in point: In a recent survey conducted by my company, practitioners were asked about the issues that plague their work, how much they trust their organization’s […]
Why Data Storytelling Matters to Data Scientists
In an increasingly data-driven world, companies worldwide have transformed the way they operate. Much of this is thanks to their ability to access a volume of data that was not available in the past. However, more recently, organizations have realized that simply collecting data is not enough, with many struggling to master the language of data. […]
Data Science: How to Shift Toward More Transparency in Statistical Practice
Data Science and statistics both benefit from transparency, openness to alternative interpretations of data, and acknowledging uncertainty. The adoption of transparency is further supported by important ethical considerations like communalism, universalism, disinterestedness, and organized skepticism. Promoting transparency is possible through seven statistical procedures: Data visualization Quantifying inferential uncertainty Assessment of data preprocessing choices Reporting multiple models Involving […]