A great data scientist combines expert knowledge of various interrelated academic disciplines to help global enterprises make agile decisions for improved business performance. Data scientists use statistics, mathematics, data mining, and computer science to analyze data sets for observable trends and patterns. They are also experts in data collection and storage methods. The Bureau of Labor Statistics had predicted […]
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 […]
Transactional Data: The Future of Real-Time Data In-Store
Consumer data is being widely spoken about in the fintech industry, especially as data personalization and transparency are significant to financial data ecosystems. As a fintech founder, I have been particularly fascinated by consumer retail trends appearing from transactional data that could rapidly improve user experience and targeted marketing in-store. The best value proposition is to combine […]
A Brief History of Analytics
Historically speaking, a simple definition of analytics is “the study of analysis.” A more useful, more modern description would suggest “data analytics” is an important tool for gaining business insights and providing tailored responses to customers. Data analytics, sometimes abbreviated to “analytics,” has become increasingly important for organizations of all sizes. The practice of data […]
A Brief History of Data Literacy
Data Literacy is essentially the ability to read and understand data, much as one might read and understand a magazine article. The primary advantage of having the bulk of the staff made up of people who are data literate is that it reduces the need for data scientists. People on staff can handle many of […]
Three Things You Need to Know About Data Literacy
Click to learn more about author Eva Murray. In the data-rich working environments of today, it is crucial for people to be data literate and, therefore, proficient in working with, understanding, and communicating data effectively. Much like literacy in general, Data Literacy is becoming an essential skill without which many of us could not do […]
How Statistics Can Lead to a Successful Data Experiment
Click to learn more about Dave Karow. It’s human nature to want things to go your way; you drop little hints for your birthday presents or avoid certain topics, for example. It’s also well-documented that companies commission surveys tailored to provide the results they want. But on a more basic level, we subconsciously (and sometimes […]
How Culture, Data, and Skills Serve as Barometers for Organizational Data Science Preparedness
Click to learn more about author Steve MacLauchlan. I’ve had the good fortune over my career of working with many different organizations across many different industries. Often, I’ve been in an advisory role assisting organizations with getting a handle on their data assets and finding value in the insights those assets can provide. When data is […]
Data and Due Process: When Algorithms Go Awry
Click to learn more about author Michael D. Shaw. Data is like statistics: a matter of interpretation. The process may look scientific, but that does not mean the result is credible or reliable. How can we trust what a person says if we deny the legitimacy of what he believes? How can we know a […]
Why Data Science is Not Statistics
Click to learn more about author Alex Paretski. Statistics as a branch of applied mathematics plays an important role in identifying hidden patterns in data. That’s why it is frequently used interchangeably with broader terms such as Data Science, Data Analytics, Business Analytics, and Machine Learning. Not only is this comparison technically incorrect, but it […]