Data Science: A creative field utilizing coding, statistics, and math to gain insights from data. It involves using all available data to be inclusive in analysis.
Goal: To extract meaningful insights from data.
Definitions of Data Science
Coding, Math, and Statistics in Applied Settings: Uses a combination of these fields to analyze data and derive insights.
Analysis of Diverse Data: Includes non-standard data to find insightful and compelling answers.
Inclusive Analysis: Involves all data to maximize understanding and answer research questions.
Demand for Data Science
High Demand: Significant demand for data scientists and managers who understand data analysis. Evidence from Harvard Business Review, McKinsey Global Institute, LinkedIn, and Glassdoor reflects a booming job market with high salaries.
Skills Required: Technical, analytical, and domain-specific knowledge.
Salaries: High-paying compared to other professions.
Components of Data Science
Venn Diagram
Coding: Hacking/Computer Programming
Statistics: Math/Statistical analysis
Domain Expertise: Knowledge of a specific field (e.g., business, health, education)
Machine Learning: Intersection of coding and statistics
Traditional Research: Intersection of statistics and domain knowledge
Danger Zone: Intersection of coding and domain knowledge without strong statistical backing
Coding Skills
Languages: R and Python for statistical coding; SQL for databases; Bash for command-line; Regex for data searching
Math Skills: Probability, algebra, and regression are fundamental
Domain Expertise: Business, health, education, science-specific methods and goals
Data Science Pathway
Planning: Define goals, organize resources, coordinate people, and schedule the project
Data Preparation: Gather, clean, explore, and refine data
Modeling: Create, validate, evaluate, and refine statistical models
Follow-up: Present, deploy, revisit models, and archive assets for future use
Roles in Data Science
Engineers: Server and software infrastructure
Big Data Specialists: Computer science, mathematics, machine learning
Researchers: Domain-specific research
Analysts: Day-to-day business analysis
Business People: Frame business questions, manage resources
Entrepreneurs: Data and business skills for startups
Full Stack Unicorn: Expert in all areas (rare)
Teams: Collaborative approach to leverage different skill sets (coding, statistics, domain knowledge)
Contrast with Other Fields
Big Data vs. Data Science: Big Data focuses on volume, velocity, variety, whereas Data Science applies mathematical and statistical tools to make sense of data.
Coding vs. Data Science: Coding instructions for machines vs. analyzing and extracting insights from data
Statistics vs. Data Science: Statistics focuses on data analysis while Data Science overlaps but includes additional skills such as coding and domain expertise.
Business Intelligence (BI) vs. Data Science: BI focuses on real-life data utility, Data Science aids in setting up and extending BI systems.
Ethical Issues in Data Science
Privacy and Anonymity: Ensuring confidentiality of private data
Copyright: Respecting data usage permissions
Data Security: Protecting valuable data
Potential Bias: Avoid building biases into algorithms unintentionally
Overconfidence in Analysis: Remaining cautious with data interpretations
Data Science Methods Overview
Data Sourcing: Getting and creating data (existing data, APIs, scraping, making data)
Coding: Using tools and languages for data manipulation
Math and Stats: Foundational mathematical principles and statistical methods
Machine Learning: Methods like clustering, categorization, and prediction
Communicating Results
Interpretability: Clear and meaningful explanation of analysis
Actionable Insights: Specific recommendations for clients
Presentation Graphics: Clear, non-distracting graphics to convey findings
Reproducible Research: Document and archive analysis for transparency and future use