Data Science is an interdisciplinary field that uses statistical methods, machine learning, and data engineering to extract insights from structured and unstructured data. It involves data collection, cleaning, analysis, and modeling to drive decision-making, automate processes, and uncover patterns in fields like business, healthcare, and AI.

Machine Learning Operations (MLOps)

Significant MLOps (Machine Learning Operations) experience. The practice of streamlining and automating the deployment, monitoring, and maintenance of machine learning models in production. It integrates DevOps principles with machine learning workflows to ensure scalability, reliability, and continuous improvement, bridging the gap between data science and IT operations.

AI/ML frameworks are software libraries and tools that provide pre-built functions, algorithms, and infrastructure to streamline the development, training, and deployment of artificial intelligence and machine learning models. Popular frameworks like TensorFlow, PyTorch, and Scikit-learn offer capabilities for data processing, model building, and optimization, enabling efficient experimentation and productionization of AI solutions.

LLMOps (Large Language Model Operations) is the practice of managing, deploying, and optimizing large language models in production environments. It combines MLOps principles with specialized workflows for fine-tuning, monitoring, and maintaining LLMs, ensuring efficiency, scalability, and responsible AI usage. Key aspects include prompt engineering, retrieval-augmented generation (RAG), cost optimization, compliance, and continuous improvement through feedback loops.

A critical initial step in data analysis, where the main aim is to explore and understand the underlying structure and characteristics of the data. This approach involves using statistical graphics and other data visualization techniques to summarize and examine patterns, trends, and relationships in data, often in an unstructured way.

Gathering detailed data on comparable metrics, such as productivity, quality, costs, or customer satisfaction, and analyzing these in the context of industry standards or direct competitors.

Supports multiple programming paradigms, including object-oriented, procedural, and functional programming, and has a vast standard library that provides tools suited for various applications, from web development to data analysis.

Essential for managing and manipulating relational databases, SQL's power lies in its ability to handle complex queries and relationships between data, making it a fundamental tool for database management, data analysis, and backend development in various applications.