Introduction to Data Science Concepts

Aug 19, 2024

Data Science: An Introduction

Overview of Data Science

  • Instructor: Barton Poulson
  • Course Goal: Provide a brief, creative overview of Data Science, emphasizing that it is not just technical but also creative.
  • Key Insight: Data Science seeks insight from all data, including data that doesn’t fit traditional analysis.

Defining Data Science

Demand for Data Science

  • Definition 1: Data Science is coding, math, and statistics in applied settings.
  • Definition 2: Analysis of diverse data.
  • Definition 3: Inclusive analysis, gathering insights from all available information.

High Demand for Data Science

  • Harvard Business Review: Data Scientist labeled as the "sexiest job of the 21st century."
  • Projected Demand:
    • 140,000 - 190,000 deep analytical talent positions needed.
    • 1.5 million data-savvy managers required.
  • Job Market: Data Scientist ranked among the best jobs in the US with a median salary of over $116,000.

Data Science Venn Diagram

  • Components:
    • Coding: Hacking.
    • Statistics: Quantitative skills.
    • Domain Expertise: Knowledge in specific fields (business, health, etc.).

Interaction of Components

  • Machine Learning/Machine Learning: Coding and stats without domain expertise.
  • Traditional Research: Stats and domain knowledge without coding.
  • Danger Zone: Coding and domain expertise without math/statistics.

Data Science Pathway

Main Steps in Data Science Projects

  1. Planning: Define goals, organize resources, coordinate people, schedule project.
  2. Data Preparation: Gather, clean, explore, and refine data.
  3. Modeling: Create, validate, evaluate, and refine statistical models.
  4. Follow-Up: Present findings, deploy models, revisit for updates, archive for future use.

Roles in Data Science

  • Engineers: Focus on hardware and software infrastructure.
  • Big Data Specialists: Create data products using algorithms.
  • Researchers: Conduct domain-specific research.
  • Analysts: Perform daily data tasks, often with structured data.
  • Business People: Frame questions, manage projects.
  • Entrepreneurs: Combine data and business skills.
  • Full Stack Unicorn: Hypothetical expert who can do everything.

Data Science vs. Other Fields

Big Data vs. Data Science

  • Big Data: Focus on volume, velocity, and variety.
  • Data Science: Focus on analysis and insights from various data.

Coding vs. Data Science

  • Coding: Task instructions to machines.
  • Data Science: Analysis and drawing insights from data.

Statistics vs. Data Science

  • Statistics: Focused on data analysis and inference.
  • Data Science: Broader field that includes statistics but encompasses more.

Business Intelligence (BI) vs. Data Science

  • BI: Applied, focuses on internal operations and decision-making.
  • Data Science: Involves deeper analysis and exploration.

Ethics in Data Science

Key Ethical Issues

  • Privacy: Handling private data responsibly.
  • Anonymity: Ensuring data does not reveal identities.
  • Copyright: Respecting ownership of data.
  • Data Security: Protecting data from unauthorized access.
  • Bias: Being aware of algorithmic biases in data.
  • Overconfidence: Avoiding absolute certainty in analysis.

Data Science Methods Overview

Categories of Methods

  1. Sourcing: Methods to obtain relevant data.
  2. Coding: Programming for data manipulation.
  3. Math: Mathematical foundations for data analysis.
  4. Stats: Statistical methods for data interpretation.
  5. Machine Learning: Data-driven methods for prediction and classification.

Data Sourcing Methods

  • Existing Data: In-house, open data, third-party data.
  • APIs: Application Programming Interfaces for data access.
  • Scraping: Extracting data from web pages.
  • Making Data: Techniques like surveys, interviews, experiments.

Coding in Data Science

R, Python, SQL

  • R: Language designed for statistical analysis.
  • Python: General-purpose programming language suitable for data tasks.
  • SQL: Language for database management and data extraction.

Additional Tools

  • C, C++, Java: Foundational languages for data science.
  • Bash: Command line interface for data manipulation.
  • Regex: Regular expressions for searching and data filtering.

Conclusion: Tools and Next Steps

  • Know your tools: Choose tools that match your needs.
  • Focus on meaning: Always prioritize extracting insights from data.
  • Get started: Don't hesitate to engage with data and coding.

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