Proposed Usecase: Data Science

Keywords for data science

terms are linked to their Wikipedia articles

• data science: using scientific methods, algorithms, and systems to extract knowledge and insights from data
• decision science: for business problems, data science combined with behavioral science and design thinking to understand end users
• business intelligence (BI): analyzing and reporting historical data, like sales statistics and operational metrics, to guide strategic decision-making
• data analysis: inspecting, cleansing, transforming, and modeling data, with the goal of discovering useful information
• data mining: discovering patterns in data with methods and tools like machine learning, statistics, and database systems
• exploratory data analysis (EDA): summarizing a dataset’s main characteristics and informing the development of more complex models or logical next steps
• data engineering: building infrastructure with which data are gathered, cleaned, stored, and prepped for data science
• DataOps: automated, process-oriented methodologies to improve quality and reduce cycle time in data analytics — akin to DevOps for data, with these key differences
• artificial intelligence (AI): computer systems that can perform tasks that normally require human intelligence, using human reasoning as a model
• AIOps: DataOps at the intersection of AI and big data, often using machine learning with the intent to feed continuous insights into continuous improvement, and often including collaborative automation, performance monitoring, and event correlations
• machine learning (ML): A subset of AI in which a system learns from input by identifying patterns in that data, then applies those patterns to new problems or requests, allowing data scientists to teach a computer to carry out tasks rather than programming it step-by-step
• supervised learning: a subset of ML with a data scientist guiding or teaching the desired conclusion to the algorithm, such as a system learning to identify problems by being trained on a dataset of correctly labeled and characterized problems
• deep learning: advanced machine learning systems with multiple input/output layers, as opposed to shallow systems having one round of data input/output
• MLOps: akin to DevOps or DataOps, collaboration and communication between data scientists and operations professionals to manage the production ML lifecycle, with increased automation and improved quality per business and regulatory requirements

Keywords related to data science

terms are linked to their Wikipedia articles

The message house provides a structure to describe and discuss value and differentiators for data science.

We will not have a Positioning Statement for this usecase, at this time. Generally, this would describe how GitLab fits and is differentiated in the market for this usecase.

| Proof points | (list specific analyst reports, case studies, testimonials, etc.) |