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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.