Data Science: An Introduction

Course Overview

• Brief, accessible, non-technical overview of Data Science
• Emphasizes creativity over technicality

Key Concepts of Data Science

• Data Science Defined:
  • Coding, math, and statistics in applied settings.
  • Analysis of diverse data, including non-standard data.
  • Inclusive analysis to gain insights.

Demand for Data Science

• Data Science is seen as the "sexiest job of the 21st century" (Harvard Business Review).
• High Demand:
  • 140,000-190,000 deep analytical talent positions needed.
  • 1.5 million data-savvy managers needed in the U.S.
• Job Market:
  • LinkedIn: Statistical analysis and data mining are in high demand.
  • Glassdoor: Data Scientist ranked as the best job in America.

Data Science Venn Diagram

• Created by Drew Conway.
• Three circles:
  • Coding (hacking)
  • Math/Statistics
  • Domain Expertise
• Intersection: Data Science

Elements of the Venn Diagram

1. Coding:
   • Important for data gathering and preparation.
   • Key languages: R, Python, SQL.
2. Math:
   • Basic probability, regression analysis, etc.
3. Domain Expertise:
   • Familiarity with the field enhances practical implementation.

Data Science Pathway

• Steps to execute a project:
  1. Planning
     • Define goals
     • Organize resources
     • Coordinate people
     • Schedule project
  2. Data Preparation
     • Gather and clean data
     • Explore and refine data
  3. Modeling
     • Create statistical models (e.g., regression analysis)
     • Validate and evaluate models
     • Refine models
  4. Follow-Up
     • Present and deploy models
     • Revisit and archive assets

Roles in Data Science

• Engineers:
  • Focus on backend hardware and software
• Big Data Specialists:
  • Develop machine learning algorithms and data products
• Researchers:
  • Domain-specific focus
• Analysts:
  • Day-to-day business operation tasks
• Business People:
  • Frame relevant questions and manage resources
• Entrepreneurs:
  • Startups with data and business skills
• Full Stack Unicorn:
  • Mythical person with perfect skills in all areas

Differences Between Fields

• Data Science vs. Big Data:

  • Data Science includes analysis and insight generation
  • Big Data focuses on volume, velocity, and variety of data

• Data Science vs. Statistics:

  • Data Science is broader, includes coding and practical applications
  • Statistics is often academically focused and less applicable in many scenarios

• Data Science vs. Business Intelligence (BI):

  • BI is more straightforward and practical, focuses on operational data
  • Data Science includes deeper analyses and creative insights

Ethical Considerations

• Key Issues:
  • Privacy: Maintain confidentiality of personal data
  • Anonymity: Protect identities in datasets
  • Copyright: Ensure data used is legally sharable
  • Data Security: Protect data from unauthorized access
  • Potential Bias: Algorithms can perpetuate biases present in data
  • Overconfidence: Avoid treating analyses as absolute truth without human interpretation

Methods in Data Science

• Sourcing:
  • Use existing data, APIs, or scraping
  • Create new data through interviews, surveys, experiments, etc.
• Coding:
  • Languages: R, Python, SQL, Bash
• Statistics:
  • Descriptive and inferential statistics, hypothesis testing, estimation

Next Steps

• Get comfortable with tools (Excel, R, Python, Tableau)
• Explore open data sources and APIs
• Engage with community and participate in data projects
• Remember: Data Science is democratic - everyone can learn to work with data.