Research Data Management: Understanding Data

The era of Big Data is claimed to herald the end of hypothesis‐driven research in favour of sophisticated algorithms uncovering information from huge data sets. But a closer look reveals that Big Data actually enhances the testing of hypothesis and experimentation, rather than replaces them.

The 21st century has ushered in the age of big data and data economy, in which data DNA , which carries important knowledge, insights, and potential, has become an intrinsic constituent of all data-based organisms. An appropriate understanding of data DNA and its organisms relies on the new field of data science and its keystone, analytics . Although it is widely debated whether big data is only hype and buzz, and data science is still in a very early phase, significant challenges and opportunities are emerging or have been inspired by the research, innovation, business, profession, and education of data science. This article provides a comprehensive survey and tutorial of the fundamental aspects of data science: the evolution from data analysis to data science, the data science concepts, a big picture of the era of data science, the major challenges and directions in data innovation, the nature of data analytics, new industrialization and service opportunities in the data economy, the profession and competency of data education, and the future of data science. This article is the first in the field to draw a comprehensive big picture, in addition to offering rich observations, lessons, and thinking about data science and analytics.

A growing number of students are completing undergraduate degrees in statistics and entering the workforce as data analysts. In these positions, they are expected to understand how to use databases and other data warehouses, scrape data from Internet sources, program solutions to complex problems in multiple languages, and think algorithmically as well as statistically. These data science topics have not traditionally been a major component of undergraduate programs in statistics. Consequently, a curricular shift is needed to address additional learning outcomes. The goal of this article is to motivate the importance of data science proficiency and to provide examples and resources for instructors to implement data science in their own statistics curricula. We provide case studies from seven institutions. These varied approaches to teaching data science demonstrate curricular innovations to address new needs. Also included here are examples of assignments designed for courses that foster engagement of undergraduates with data and data science.

Alation recently released the results of its Q1 2022 State of Data Culture Report. The company says the report “provides a regular assessment of the progress enterprises have made in establishing a data culture within their organizations, the challenges they face in embracing data-driven decision making, and key drivers for data and analytics.” We explore some of the report’s findings with Ashley Womack, director of corporate marketing at Alation.

Designing, conducting, analyzing, reporting, and interpreting the findings of a research study require an understanding of the types and characteristics of data and variables. Descriptive statistics are typically used simply to calculate, describe, and summarize the collected research data in a logical, meaningful, and efficient way. Inferential statistics allow researchers to make a valid estimate of the association between an intervention and the treatment effect in a specific population, based upon their randomly collected, representative sample data. Categorical data can be either dichotomous or polytomous. Dichotomous data have only 2 categories, and thus are considered binary. Polytomous data have more than 2 categories. Unlike dichotomous and polytomous data, ordinal data are rank ordered, typically based on a numerical scale that is comprised of a small set of discrete classes or integers...

Third-party cookies might be going the way of the dinosaur, but organizations will still need data about their customers’ online behaviors to deliver the personalized experiences they expect. This means a shift in data strategy to focus on multiple types of first-party data, collected with customer consent. First-party data is data that customers agree to share with brands, including their preferences, online behaviors, and personal information. And while first-party data is a rich source of intelligence, it often lives in silos and is unorganized, rendering much of it useless when it comes to helping the organization drive better decisions and actions.

Data analysis is not just for large organizations and large datasets; it's also for the average person. Learn how to put data to work in your own life - from charting your cell phone usage to personalizing your medical care or improving your exercise routine.

Confront how ALL data has uncertainty, and why statistics is a powerful tool for reaching insights and solving problems. Begin by describing and summarizing data with the help of concepts such as the mean, median, variance, and standard deviation. Learn common statistical notation and graphing techniques, and get a preview of the programming language R, which will be used throughout the course.