introduction

Welcome! Here's our roadmap for learning computational humanities

Watch this quick introduction to the DHcode.org series

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data types

How computers represent different kinds of data

In this first video, we’ll learn about a foundational Python programming concept called datatypes. Let's unpack how strings, integers, and floats form the basis of computational text analysis.

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strings

A deeper dive into text-based computational data

In this video, we’ll spend some more time with strings -- a foundational Python programming concept that will help you get started on analyzing text-based content for a research study.

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variables

Using an alias to store data/code snippets in a convenient way

In this video, we’re taking a tiny diversion from jumping further into data types. Here we’ll discover variables--a key concept in programming that you will use over and over again in humanities and social science research.

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lists-dictionaries

Storing data in easy to remix/reuse formats

Now, that we know some basic concepts in Python, we’re going to dive into a very brief discussion about data structures to continue on our path to writing a text analysis program.

In this video, we’re going to build on the concept of data types like strings, integers, and floats. Here, we explore data structures which are the ways that we combine data into organized units. Two particular data structures that are crucial for text analysis are called: lists and dictionaries.

There’s lots to say on this topic but we’re going to stay quite focused with just what we need to know. Check out the resources list if you’re interested in going deeper.

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booleans

Asking True/False questions of our data

In this video, we’ll take a look at a Boolean--a special form of data type that has only a TRUE or a FALSE value.

Booleans are an especially handy concept that help to control the flow of your program. It’s a similar process to a flowchart that directs you through a series of YES/NO questions.

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conditionals, loops and functions

Controlling the flow of our program

This video focuses on some fundamental computing concepts that will drive our text analysis program--conditionals, loops, and functions.

Though, in our text analysis we won’t actually be hand coding them. We’ll rely on the Pandas library to do this work--which we’ll dive in to soon.

But it’s really important to know what Pandas is doing behind the scenes in order to have confidence that the research questions are being explored appropriately and accurately.

So, this video may feel quite a bit more abstract than the previous ones. But hang in there with me!

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data structures

A quick intro to the Pandas library. Plus, tips on organizing data

Now that you have the basic Python concepts under your belt, in this video, we jump into one of the most crucial parts of text analysis programming.

Here we’ll discover the Pandas library--a free software package of data analysis tools for Python programming that you will likely use over and over again in humanities and social science research.

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data handling

Get a handle on outliers and other data mischief

Now that you have the basic Python concepts under your belt, in this video, we jump into one of the most crucial parts of text analysis programming.

Here we’ll discover the Pandas library--a free software package of data analysis tools for Python programming that you will likely use over and over again in humanities and social science research.

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missing data

What to do when our data is MIA

In this video, we continue our exploration of the Pandas library for Python. Here we focus on using Pandas for handling missing data to prep our text-based corpus for the analysis.

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standardizing data

Putting data into a common format

In this video, we continue our exploration of the Pandas library for Python. Here we focus on using Pandas to consistently format or standardize the data for our text-based corpus for the analysis.

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cleaning data

Wrangling dirty data with Pandas

In this video, we continue our exploration of the Pandas library for Python. Here we focus on using Pandas for cleaning data to prep our text-based corpus for the analysis.

This can often feel really tedious but the old adage "garbage-in, garbage-out" is very true. So let's get this data cleaned up!

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analyzing data

Try out some basic text analysis with Python, Pandas and NumPy

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text preprocessing

Prep our corpus for valid, reliable, and trustworthy results

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text analysis

How to automate sentiment analysis, language translation, and more!

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