Advances in technology have led to exponential growth in the amount and complexity of data. We are at a threshold of an era in which hypothesis-driven science is being complemented with data-driven discovery. The data collected are complex in size, dimension and heterogeneity and provide unprecedented opportunities for new discoveries in theoretical and applied research. Broadly, data scientists extract meaning from this wealth of data to generate critical insights to drive decision making and innovation. They combine computing, statistics, mathematics, visualization, software development and domain knowledge to make inferences from various forms of information (including but not limited to numerical, text, audio, and visual data). Thus, data science involves engineering, reproducibility and provenance, and often includes software development and design.
DataLab leverages existing knowledge to tackle complex problems, develop new tools and methods to do so more effectively, and teach their implementation.
As a cross-university activity, DataLab fosters, promotes and facilitates data science to accelerate discovery at the frontiers of scientific, engineering and social disciplines. DataLab partners with researchers across UC Davis to push the envelope both within and across disciplines to enable qualitatively novel, interdisciplinary research. By combining techniques across domains, we problem solve to further both data science application and theory.
DataLab engages in all activities involved in working with data (i.e., identifying, acquiring/accessing, processing, transforming, exploring, modeling, summarizing, visualizing and interpreting data). In addition to this data research pipeline, we focus on generating and developing novel questions and approaches. Training activities at the DataLab focuses on the theory, methods, process and tools for working with, interpreting and applying data. To that end we focus on cultivating curiosity, creativity, communication and critical thinking skills in students and researchers across the University.
DataLab seeks to promote academic and research excellence through quality programs, engaged researchers, and an innovative research and learning environment and to meet growing industry and academic need for graduates with data science skills. To that end, the DataLab complements existing and developing educational programs to facilitate research in data science and provide a niche for students, faculty and professional researchers seeking to move beyond their individual programs to identify and exploit new opportunities.
DataLab provides training, advice and collaboration services to UC Davis researchers at all stages of their career to be skilled at working with data throughout the research pipeline, including:
- Developing research questions and proposals
- Using data science tools
- Obtaining, accessing and sharing data
- Cleaning, transforming, and structuring data for analysis
- Data management, curation, security and privacy
- Data analysis via statistical and machine learning and modeling
- Visualization for exploratory data analysis and presentation of results
- Computational issues
- Reproducibility and provenance
- Other data science education and implementation resources at UC Davis
DataLab hosts free workshops throughout the year on these and other relevant topics to help researchers (students, faculty and academics) obtain technical skills and access methods for elevating data-driven discovery. These workshops are applicable to researchers from all disciplines, and complement traditional class curricula by focusing on emerging topics.