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.