Introduction to Data Science Concepts

Aug 17, 2024

Data Science: An Introduction

Overview of Data Science

  • Instructor: Barton Poulson
  • Course Goals: Provide a brief, non-technical overview of the field of Data Science.
  • Common Misconceptions: Data Science is often viewed as overly technical; however, it is primarily a creative discipline.
  • Key Concepts:
    • Use coding, statistics, and math tools creatively to gain insights from data.
    • Listen to all data, including non-standard data, to gather comprehensive insights.

Defining Data Science

  • Definitions:
    • Definition 1: Data Science is coding, math, and statistics in applied settings.
    • Definition 2: The analysis of diverse data that doesn't fit standard analytic approaches.
    • Definition 3: Inclusive analysis that encompasses all available data to answer research questions.

Demand for Data Science

  • High Demand:
    • Data Science has been dubbed "the sexiest job of the 21st century" (Harvard Business Review).
    • McKinsey Global Institute predicts a need for 140,000-190,000 deep analytical talent positions and 1.5 million data-savvy managers in the US.
    • LinkedIn identifies statistical analysis and data mining as critical job skills.
    • Glassdoor lists data scientist as one of the best jobs in America with high salaries.

The Data Science Venn Diagram

  • Components:
    • Coding: Programming skills (R, Python, SQL).
    • Statistics/Math: Knowledge of statistical methods and mathematical concepts.
    • Domain Expertise: Familiarity with relevant fields such as business, health, education, etc.
  • Intersections:
    • Machine Learning, Traditional Research, Coding without Math (the "danger zone").

The Data Science Pathway

  • Steps in Data Science:
    1. Planning: Define goals, organize resources, coordinate people, and schedule.
    2. Data Preparation: Gather, clean, explore, and refine data.
    3. Modeling: Create a statistical model, validate, and evaluate it.
    4. Follow-up: Present findings, deploy the model, revisit and archive results.

Roles in Data Science

  • Key Roles:
    • Engineers (backend hardware/software).
    • Big Data Specialists (data processing, machine learning).
    • Researchers (domain-specific analysis).
    • Analysts (day-to-day data tasks).
    • Business People (project managers, decision makers).
    • Entrepreneurs (data-driven startups).
    • Full Stack Unicorns (rare individuals skilled in all aspects of data science).

Contrast Between Fields

  • Data Science vs. Big Data:
    • Big Data focuses on volume, velocity, and variety of data.
    • Data Science encompasses analysis and insights derived from diverse data sources.
  • Data Science vs. Coding:
    • Coding is about instructing machines, whereas Data Science focuses on extracting meaning from data.
  • Data Science vs. Statistics:
    • Data Science is broader, involving coding and domain expertise; not all data scientists are trained statisticians.
  • Data Science vs. Business Intelligence:
    • Business Intelligence focuses on practical applications and using existing tools, while Data Science involves deeper analysis and methods.

Ethical Considerations in Data Science

  • Do No Harm:
    • Privacy concerns with personal data.
    • Anonymity issues and the ability to identify individuals from datasets.
    • Copyright issues when scraping data.
    • Data security to protect valuable datasets.
    • Identifying potential biases in algorithms.
    • Remaining humble and critical when interpreting data analyses.

Methods in Data Science

  • Data Sourcing:
    • Methods of acquiring data: existing data, APIs, scraping, and creating new data.
  • Coding:
    • Importance of coding skills in R, Python, SQL, and command line interfaces (Bash).
  • Mathematics:
    • Importance of algebra, calculus, and probability theory in data science.
    • Applications of mathematics in decision-making and understanding models.
  • Statistics:
    • Use of statistics to summarize data, infer conclusions, and check model validity.

Exploratory Data Analysis

  • Graphical Exploration:
    • Use graphics (bar charts, histograms, scatter plots) to reveal data distributions and relationships.
  • Numerical Exploration:
    • Use statistical measures (mean, median, mode, standard deviation, variance) to summarize and understand data.

Inference and Hypothesis Testing

  • Hypothesis Testing:
    • Understand null and alternative hypotheses, Type I and Type II errors, and the importance of interpreting p-values correctly.
    • Use estimation methods such as confidence intervals to provide numerical values for population parameters.

Conclusion and Next Steps

  • Course Goal: Equip students with fundamental concepts in Data Science.
  • Further Learning: Explore more advanced topics (machine learning, data visualization, etc.) and practical applications.
  • Data Science Community: Encourage engagement in data science forums, competitions, and collaborative projects.