From Social Science to Data Science

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Explore how Python can be used to enhance social science research, from programming basics to advanced data techniques. This book covers data manipulation, analysis, and visualization through hands-on learning. This course equips social scientists with the essential skills to apply data science methods using Python. It bridges the gap between traditional social science research and modern computational techniques, offering practical insights through real-world examples. Designed for those new to programming, it builds a strong foundation in data manipulation, analysis, and visualization. The content emphasizes ethical and effective use of data in social research. This course is ideal for social science researchers, students, and professionals looking to integrate data science into their work. No prior Python experience is required, but a basic understanding of social science concepts is helpful. It is designed for those interested in improving their research methods through computational tools. This course takes a practical, hands-on approach to learning data science through Python. It introduces the key principles of programming, data manipulation, and social data science, ensuring readers understand both the theory and the application of these techniques in social science research. This course is based on From Social Science to Data Science, by Bernie Hogan. Copyright ©2023 by Sage Publications Limited. All rights reserved, including rights for text and data mining and training of artificial technologies or similar technologies. Published by Sage Publications Limited, London. Used by arrangement with Sage Publications Limited.

Syllabus

Introduction: Thinking of life at scale
- This module introduces learners to the principles of modular code development, the concept of data as a foundational element, and the importance of structured planning in programming. It covers topics such as the DIKW framework, cost efficiency in coding, and the value of writing clean, reusable pseudocode. Learners will gain an understanding of how to create practical and elegant code using the FREE principle.
The Series: Taming the distribution
- This module covers the fundamentals of working with the Series data structure in Python, including indexing, modifying values, and summarizing data using functions like value_counts() and unique(). Learners will also explore filtering with multiple conditions and transforming Series data effectively.
The DataFrame: Python's tabular format
- This module covers the fundamentals of working with pandas DataFrames in Python, including loading data, manipulating columns and rows, and applying functions to structured data. Learners will gain hands-on experience in using DataFrames to organize, query, and transform data effectively.
File types: Getting data in
- This module focuses on importing and parsing various data formats such as CSV, Excel, JSON, and XML. Learners will gain skills in handling structured and nested data, using Python libraries like pandas and Beautiful Soup. The content covers practical techniques for extracting and organizing data from real-world sources.
Merging and grouping data
- This module covers techniques for merging and grouping data using Python's pandas library and SQL. Learners will explore methods for combining datasets, performing joins, and aggregating data for analysis. The content equips learners with practical skills to handle data integration challenges in real-world scenarios.
Accessing data on the World Wide Web using code
- This module explores how to access and retrieve data from the web using code, focusing on understanding URLs, making web requests, and implementing ethical data collection practices. Learners will gain practical skills in parsing URLs, using Python libraries, and applying data minimisation principles. The content also covers real-world examples like collecting data from Reddit.
Accessing APIs, including Twitter and Reddit
- This module covers the essentials of working with APIs, including authentication methods, query design, and the use of wrappers to simplify data retrieval. Learners will gain practical skills in accessing data from platforms like Twitter and Reddit while considering ethical and technical limitations in data collection.
Research questions
- This module explores the role of research questions in guiding scientific inquiry, covering key distinctions between prediction and explanation, the importance of operationalisation, and how boundaries shape research focus. Learners will gain skills in formulating and refining research questions for effective data analysis.
Visualising expectations: Comparing statistical tests and plots
- This module explores how to visualize and analyze statistical distributions, compare groups using hypothesis tests like t-tests and ANOVA, and interpret patterns in data through plots and regression lines. Learners will gain practical skills in using Python libraries like matplotlib and seaborn to create informative visualizations and understand statistical relationships.
Cleaning data for socially interesting features
- This module equips learners with the skills to clean and preprocess unstructured social data, focusing on handling missing values, extracting social context, and applying data transformation techniques. It covers methods for working with numeric, textual, and temporal data, as well as strategies for organizing and reusing data cleaning workflows.
Introducing natural language processing: Cleaning, summarising, and classifying text
- This module provides an introduction to key text processing techniques in natural language processing, including text encoding, tokenization, and preprocessing. Learners will explore methods for analyzing text, such as TF-IDF and sentiment scoring, and understand how to classify and summarize text data effectively.
Introducing time-series data: Showing periods and trends
- This module introduces the fundamentals of working with time-series data, covering how to parse and manipulate time data, use datetime indexes for filtering, and resample data for analysis across different time intervals. Learners will explore techniques for handling missing data and understanding temporal patterns.
Introducing network analysis: Structuring relationships
- This module introduces the fundamentals of network analysis, covering key concepts such as network types, graph creation, and visualization. Learners will gain practical skills in building and modifying network objects using Python's networkx library and understanding how to interpret and display network structures effectively.
Introducing geographic information systems: Data across space and place
- This module introduces the fundamentals of geographic information systems (GIS), focusing on map projections, spatial data integration, and the use of geopandas for mapping. Learners will explore how geographic data is represented and visualized, and how to link geospatial data with other datasets. The course also covers practical skills in creating and interpreting maps using computational tools.
Conclusion: There (to data science) and back again (to social science)
- This module provides an in-depth look at the integration of data science techniques in social science research. It covers data collection, machine learning for prediction, and dashboard development for real-time data visualization. Learners will gain practical insights into applying data science tools to social science problems.