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Overview of Data Science Fundamentals

May 25, 2025

Lecture Notes: An Introduction to Data Science

Key Details

  • Authors: Jeffrey S. Saltz, Jeffrey M. Stanton
  • University: Syracuse University
  • Publisher: SAGE Publications, 2018
  • Purpose: Designed as a textbook and resource for data science courses, assumes no prior programming or statistics experience.

Introduction to Data Science

  • Data science involves more than analyzing data.
  • Data in the real world is often unstructured.
  • Important to have skills in both numerical data handling and with various data formats like text, images, etc.

Steps in Doing Data Science

  1. Data Architecture: Planning data usage and storage.
  2. Data Acquisition: Collecting and representing data accurately.
  3. Data Analysis: Interpreting the data to make inferences and decisions.
  4. Data Archiving: Preserving data for future use.

Skills Needed in Data Science

  • Understanding application domain.
  • Communicating with data users.
  • Systems thinking to see the whole data picture.
  • Data representation and transformation.
  • Visualization and presentation of data.
  • Ethical reasoning and understanding of data privacy.

Understanding Data

  • Data Representation: Understanding bits and bytes as the foundation.
  • Data Storage: Using R to store and manipulate data.
  • Data Sets: Creating data sets in R using vectors.

Identifying Problems with Data

  • Communication with subject matter experts is crucial.
  • Look for exceptions and explore risks and uncertainties.

Getting Started with R

  • Installation: R is open source and requires careful syntax.
  • Vectors: Used to store and manipulate sequences of data in R.

Data Handling Techniques

  • Dataframes: Organize data in rows and columns.
  • Data Munging: Cleaning and formatting data for analysis.
  • Data Visualization: Using ggplot2 for advanced visualizations.

Statistics and Data Analysis

  • Descriptive Statistics: Mean, median, variance, and standard deviation.
  • Sampling Techniques: Understanding and applying sampling distributions.
  • Data Modeling: Creating linear regression models to infer relationships.

Advanced Topics

  • Text Mining: Analyzing text data, creating word clouds, and sentiment analysis.
  • Big Data: Understanding volume, velocity, and variety in data contexts.
  • Distributed Computing: Using tools like Hadoop for handling large datasets.
  • Web Applications: Developing interactive data applications using Shiny in R.

Ethical Considerations

  • Importance of understanding privacy issues and ethical implications in data science.

Additional Resources and Tools

  • Supplementary learning resources include Wikipedia and Khan Academy for quick insights.
  • R packages like ggplot2, Shiny, and others enhance data analysis capabilities.