Python for Social Data Science

Python for Social Data Science (University of Oxford, Hillary Term 2023)

Course provider: Ashrakat Elshehawy

 

DPIR (open to other Oxford department's as well) postgraduate students are encouraged to attend a 6-week course to learn the programming language Python in Hillary Term 2023. Python programming knowledge is becoming increasingly essential for Social Scientists working on applications for computational social science research, such as machine learning and computational text analysis. 

The structure of the course will be a 2-hour Python class and 1-hour Python clinic for code de-bugging purposes and project presentations. This will take place on Tuesdays from 4pm-7pm.

Important note: Attendance of this course is obligatory if you want to attend the Computational Text Analysis Class offered in Trinity 2023 (You can get an exemption if you show previous attendance of a Python class through a transcript or similar document). These two classes can be viewed as a sequence.

The course material is publicly available. For each class you will find:

1. Lecture Slides
2. Code used in the Python Session in class + its full solutions
3. Excercises + their solutions

The class has a Slack Channel, students have been sent a link over email! Please email me if you would like to join (ashrakat.elshehawy@politics.ox.ac).

To sign up for presentations please refer to our slack channel.

Quick Access to our Python Sheets:

What did we cover:

• Week 1: Introduction to Python Link

1. GitHub
2. Differences between Google Colab and Anaconda (Jupyter Notebooks)
3. Python Interactive Shell
4. Assiginging Variables and Values
5. Understanding Different Objects in Python
6. Mathematical Operations
7. Boolean Operations
8. Transformation of Objects
9. Understanding Python Strings
10. Combining Objects

• Week 2: Python Data Structures Link

1. Data Structures:

• Lists
• Tuples
• Dictionaries
• Sets

2. Loops
3. If-Else Statements
4. List Comprehension

• Week 3: Functions and Pandas I Link

1. Appending Data into Lists
2. Compound Data Structures
3. Functions
4. Pandas I

• Week 4: Pandas II, Loading Big Data in Python, and Visualization

1. Pandas II
2. Importing Big Data
3. Exporting Big Data
4. The Power of Counting
5. Python Visualization

• Week 5: Scraping from the Web

1. Data Retrieval from the Web
2. HTML
3. BeautifulSoup
4. Messy Data Cleaning Mechanisms

• Week 6: Twitter, Natural Language Processing, and Mini Intro to Machine Learning

1. Twitter API and Data Retrieval
2. Natural Language Processing Intro
3. Named Entity Recognition
4. Pos-tagging
5. Sentiment Analysis
6. Unsupervised Machine Learning

_{Syallbus references and credits are due to Musashi Harukawa (Postdoc Princeton) - some aspects of this course are inspired by his Intro to Python course at the Department of Politics and International Relations at Oxford in 2020. Here is the course website where Musashi provided his course material: https://muhark.github.io/dpir-intro-python/index.html}