Anthropological Data Analysis

Course Description

Anthropological Data Analysis focuses on the methods and techniques of analysis of anthropological data, which is an essential step in the interpreting of data. Analytical techniques applied to anthropological data include construction and use of spreadsheets, digital image development and manipulation, map making and presenting data, and database construction, management, and querying. Geographic Information Systems (GIS) will also be introduced.

Within the SCNS taxonomy, ANT is the Anthropology prefix and the C suffix marks a combined lecture-and-laboratory course. UWF publishes this at 3 semester hours; the contact-hour figure applies the repository's combined-course convention of 20 per credit and is derived.

This is a practical computing course wearing an anthropology label, and it is one of the most transferable things in the prefix. Spreadsheets, databases, image handling, mapping and GIS are the tools that turn a season's records into a result — and they are equally the tools that make an anthropology graduate employable outside anthropology. A student who can build and query a properly structured database is useful to almost any organisation, and very few humanities and social science graduates can.

Learning Outcomes

Required Outcomes

• Describe the data types anthropology produces and their structure.
• Structure data so that it can be sorted, filtered, and analysed.
• Apply one row per record and one column per field consistently.
• Construct and use spreadsheets for anthropological data.
• Write formulas using correct and appropriate references.
• Use lookup functions to relate datasets.
• Build and interpret pivot tables and summaries.
• Recognise and correct common spreadsheet errors.
• Describe the limits of spreadsheets and when a database is needed.
• Design a relational database schema for a research problem.
• Define tables, keys, and relationships correctly.
• Enter and validate data with appropriate controls.
• Query a database to answer a research question.
• Manage and document a dataset for reuse.
• Describe digital image capture, formats, and resolution.
• Develop and manipulate digital images appropriately.
• Describe the ethical limits of image manipulation in research.
• Produce maps that communicate a spatial argument.
• Describe cartographic conventions, scale, and projection.
• Describe the fundamentals of geographic information systems.
• Join attribute data to spatial data.
• Present data honestly in tables, charts, and figures.
• Avoid misleading graphical presentation.
• Document analysis so that it can be reproduced.

Optional Outcomes

• Describe statistical analysis of anthropological data.
• Describe spatial analysis in greater depth.
• Describe data management planning and archiving.
• Describe photogrammetry and three-dimensional recording.
• Describe scripting and automation for repetitive analysis.
• Describe open data and reproducible research practice.

Major Topics

Required Topics

• Anthropological data types
• Data structure for analysis
• One row per record
• Spreadsheet construction
• Formulas and references
• Lookup functions
• Pivot tables
• Common spreadsheet errors
• When a database is needed
• Relational schema design
• Tables, keys, relationships
• Data entry and validation
• Querying
• Dataset management and documentation
• Digital image capture and formats
• Image development and manipulation
• Ethics of image manipulation
• Map making
• Cartographic conventions, scale, projection
• GIS fundamentals
• Joining attribute to spatial data
• Honest presentation
• Avoiding misleading graphics
• Reproducible documentation

Optional Topics

• Statistical analysis
• Spatial analysis in depth
• Data management planning
• Photogrammetry
• Scripting and automation
• Open data and reproducibility

Resources & Tools

• QGIS (qgis.org) — free and open source, fully capable, and what you can keep using after you graduate and lose the university licence.
• Spreadsheet and database software — institutionally provided; and SQLite is free and is enough to learn relational design properly.
• Open data portals — Census, NOAA, Florida agencies, and the Florida Master Site File; free, real, messy data, which is far better practice than tidy exercises.
• Build something and keep it. A working database and a good map are portfolio pieces, and they are what makes this skill visible to an employer.

Career Pathways

• Anthropologists and archaeologists — SOC 19-3091; and the data skills are what get you hired within the discipline as well as outside it.
• GIS technician and analyst — a genuinely marketable skill on its own, and in demand across planning, environment, utilities, and government.
• Cartographers and photogrammetrists — SOC 17-1021.
• Data analyst and research support roles — in any sector; the ability to structure and query data is rare among social science graduates.
• Cultural resource management — where database and mapping skills make a field technician far more useful.
• Collections and museum information management.
• Local government planning and environmental agencies.
• ⚠ This is the most transferable course in the prefix. Say so explicitly on a resume — "built and queried a relational database, produced GIS analysis" reads to an employer very differently from "anthropology degree".

Special Information

⚠⚠ Structure the data first — almost every analysis problem is a structure problem

• Studies of working spreadsheets consistently find errors in a large majority of them, and the root cause is almost always structure rather than formulas.
• ⚠⚠ One row per record, one column per field, no merged cells, no blank rows as separators, and no formatting carrying meaning. A dataset built that way can be sorted, filtered, pivoted, joined, and checked; one that is not cannot be, and fixing it later costs more than building it correctly.
• ⚠ Never let colour be the only record of something. If a highlighted row means "verified", put "verified" in a column — formatting is invisible to every formula and is lost the moment anyone copies the data.
• Keep raw data raw. Never overwrite the original; do the cleaning in a copy or, better, in a documented script — so the transformation can be checked and repeated.
• Record units, codes, and definitions in the file. A column called "length" with no unit is unusable in a year, and a coding scheme in someone's head is not a coding scheme.
• ⚠ Use lookups rather than aligning two lists by hand. Sorting one and not the other is a silent, devastating error and it is very common.
• Move to a database when the spreadsheet starts repeating itself. The same entity appearing in several sheets will eventually disagree with itself, and nothing in a spreadsheet prevents that.
• Design the schema before entering anything. Restructuring after data entry is expensive and error-prone.
• ⚠ Back up, and keep the backup somewhere else. A season's records exist in one place more often than anyone admits.

⚠ Presenting data honestly — including images and maps

• ⚠ Do not truncate a chart axis to exaggerate a difference. A bar chart not starting at zero makes a small change look dramatic; do it deliberately and label it, or do not do it.
• Choose the chart for the question. Pie charts compare badly, dual axes invite false correlation, and three-dimensional effects distort area and add nothing.
• Show the denominator, and say what the data is and when it was collected.
• ⚠⚠ Image manipulation has an ethical line. Adjusting brightness and contrast across a whole image is normal; selectively altering, adding, or removing content in a research image is falsification, and it has ended careers in other disciplines. Keep the original and record what you changed.
• Scale bars and north arrows are not decoration. A published archaeological photograph or plan without a scale is not evidence.
• ⚠ Map projection and scale change what a map appears to say. Choose deliberately and state the projection.
• Do not map site locations precisely in a public document. Publishing coordinates invites looting, and generalising or omitting them is the professional norm.
• Do not use colour as the only carrier of meaning, and check contrast — figures are read by people with colour vision deficiency and printed in greyscale.
• Document how a figure was produced so someone else could reproduce it.

How Florida course levels affect transfer

The first digit of an SCNS number denotes the year of offering, not transferability. Courses at the 1000 and 2000 levels transfer transparently between Florida public institutions, and 3000 to 4000 is unproblematic since both are upper division. The boundary that actually matters is 2000 to 3000, where lower-division credit generally cannot satisfy an upper-division requirement.

ANT4191C is 3 semester hours at the University of West Florida. ⚠ The contact-hour figure is derived at the combined-course convention.

The most transferable course in this prefix — and worth naming explicitly on a resume.