"I don't know anything about Python or Jupyter. I want to start from the beginning!"
The absolute basics
This three-part interactive workshop series is your complete introduction to programming Python for people with little or no previous programming experience, with a focus on data science application. It covers the basics of Python and Jupyter, variables and data types, and a gentle introduction to data analysis in Pandas.
Building on the basics
This three-part interactive workshop series is a follow-up to D-Lab's Python Fundamentals. It is intended for people who want to learn about core structures of Python that underpin data analysis. We cover loops and conditionals, creating your own functions, analysis and visualization in Pandas, and the workflow of a data science project.
"I know the basics of Jupyter, Python, and Pandas. I want to learn how to retrieve online data."
Obtain data from web platforms
In this workshop, we cover how to extract data from the web with APIs using Python. APIs are often official services offered by companies and other entities, which allow you to directly query their servers in order to retrieve their data. Platforms like The New York Times, Twitter and Reddit offer APIs to retrieve data.
"I know the basics of Jupyter, Python, and Pandas. I want to learn more about data analysis and visualization."
Manipulate DataFrames using Pandas in Python
In this workshop, we provide an introduction to data wrangling with Python. We will do so largely with the pandas package, which provides a rich set of tools to manipulate and interact with data frames, the most common data structure used when analyzing tabular data. We'll learn how to manipulate, index, merge, group, and plot data frames using pandas functions.
Pandas, Matplotlib, and Seaborn
In this workshop, we provide an introduction to data visualization with Python. First, we'll cover some basics of visualization theory. Then, we'll explore how to plot data in Python using the matplotlib and seaborn packages.
"I know a fair bit about Python and Pandas. I want to learn more advanced topics like Machine Learning."
Bag-of-words, sentiment analysis, topic modeling, word embeddings, and more
This workshop is part of a loosely-coupled 4-part text analysis workshop series that will prepare participants to move forward with research that uses text analysis, with a special focus on social science applications. We explore fundamental approaches to applying computational methods to text in Python. We cover some of the major packages used in natural language processing, including scikit-learn, NLTK, spaCy, and Gensim.
Classification, regression, clustering in Python
In this workshop, we provide an introduction to machine learning in Python. First, we'll cover some machine learning basics, including its foundational principles. Then, we'll dive into code, understanding how to perform regression, regularization, preprocessing, and classification.
Analyzing geospatial data using GeoPandas in Python
Geospatial data are an important component of data visualization and analysis in the social sciences, humanities, and elsewhere. The Python programming language is a great platform for exploring these data and integrating them into your research.
Create and train neural networks using Tensorflow and Keras.
This workshop conveys the basics of deep learning in Python using keras on image datasets. Students are empowered with a general grasp of deep learning, example code that they can modify, a working computational environment, and resources for further study.