Introduction to Computational Data Science

Students look at opening questions, either at their desks or in a walk around the room. They select a question they are personally interested in, and think about the data required to answer that question. This process draws a direct line between answering questions they care about and the basics of data science.

Students may lean towards questions about individuals, instead of questions about what’s true for a group of individuals who vary from one to another. For example, instead of wondering what movie gets the highest rating, they should ask what’s the typical rating for movies in a list, or how much those ratings tend to vary.

Have students share back the different data they would gather to answer their questions. For each question, students would likely have to gather many different kinds of data. If we wanted to find out if small schools are better than big schools, for example, we might want to gather data on SAT scores, college acceptance, etc. Each of these is a variable in our dataset: any two schools we look at could vary by each of them.

What is the most popular movie of all time? Is Climate Change real? How long do quarterbacks tend to stay in the league? Is Stop-and-Frisk racially biased? We can’t survey every school in the world, get data on every movie ever made, or every police action - but we can do an analysis for a sample of them, and try to infer something about all of them as a whole. These questions quickly turn into a discussion about data — how you assess it, how you interpret the results, and what you can infer from those results. The process of learning from data is called Data Science. Data science techniques are used by scientists, business people, politicians, sports analysts, and hundreds of other different fields to ask and answer questions about data.

We’ll use a programming language to investigate these questions. Just like any human language, programming languages have their own vocabulary and grammar that you will need to learn. The language you’ll be learning for data science is called Pyret.

Datasets are useful for answering questions, but they can’t answer all the questions that we will wonder about for a given topic. In this activity students will look at a small dataset about a cyclist’s training rides and think about how they could use the table to answer the question or why they cannot answer the question from the table.

Discuss students' findings and check for questions.

Students explore the Animals Dataset, sharing observations and familiarizing themselves with the idiosyncrasies and patterns in the data. In the process, they learn about Categorical and Quantitative data.

Have students open the Animals Spreadsheet in a browser tab, or turn to The Animals Dataset (Page 2) in their Student Workbooks.

This table contains data from an animal shelter, listing animals that have been adopted. We’ll be analyzing this table as an example throughout the course, but you’ll be applying what you learn to a dataset you choose as well.

Any two animals in our dataset may have different ages, weights, etc. Each of these is called a variable in the dataset.

Data Scientists work with two broad kinds of data: Categorical Data and Quantitative Data. Categorical Data is used to classify, not measure. Categories aren’t subject to the laws of arithmetic. For example, we couldn’t ask if “cat is more than lizard”, and it doesn’t make sense to "find the average ZIP code” in a list of addresses. “Species” is a categorical variable, because we can ask questions like “which species does Mittens belong to?"

Quantitative Data is used to measure an amount of something, or to compare two pieces of data to see which is less or more. If we want to ask “how much” or “which is most”, we’re talking about Quantitative Data. "Pounds" is a quantitative variable, because we can talk about whether one animal weighs more than another or ask what the average weight of animals in the shelter is.

Have students share back their noticings (statements) and wonderings (questions), and write them on the board.

Data Science is all about using a smaller sample of data to make educated guesses about a larger population. It’s important to remember that tables are only a sample of a larger population: this table describes some animals, but obviously it isn’t every animal in the world! Still, if we took the average age of the animals from this particular shelter, it might tell us something about the average age of animals from other shelters.

Students open up the Pyret environment (code.pyret.org, or "CPO") and see the Animals Dataset reflected there.

Let’s take a look at our programming environment, and see what the Animals Dataset looks like there.

The first few lines on the lefthand side of the screen tell Pyret to import files from elsewhere, which contain tools we’ll want to use for this course. We’re importing a file called Bootstrap:Data Science, as well as files for working with Google Sheets, tables, and images:

After that, we see a line of code that defines shelter-sheet to be a spreadsheet. This table is loaded from Google Drive, so now Pyret can see the same spreadsheet you do. (Notice the funny scramble of letters and numbers in that line of code? If you open up the Google Sheet, you’ll find that same scramble in the address bar! That scramble is how the Pyret editor knows which spreadsheet to load.) After that, we see the following code:

The first line (starting with #) is called a Comment. Comments are notes for humans, which the computer ignores. The next line defines a new table called animals-table, which is loaded from the shelter-sheet defined above. We also create names for the columns: name, species, sex, age, fixed, legs, pounds and weeks. We could use any names we want for these columns, but it’s always a good idea to pick names that make sense!

Every table is made of cells, which are arranged in a grid of rows and columns. The first row and first column are special. The first row is called the header row, which gives a unique name to each variable (or “column”) in the table. The first column in the table is the identifier column, which contains a unique ID for each row. Often, this will be the name of each individual in the table, or sometimes just an ID number.

Below is an example of a table with one header row and two data rows:

After the header, Pyret tables can have any number of data rows. Each data row has values for every column variable (nothing can be left empty!). A table can have any number of data rows, including zero, as in the table below:

Pyret lets us use many different kinds of data. In the animals table, for example, there are Numbers (the number of legs each animal has), Strings (the species of the animal), and Booleans (whether it is true or false that an animal is fixed).

Once you know how to program, you can do a lot with datasets:

In this course, you’ll be learning to do all three in Pyret: Display, Filter, and Build.

What are some other examples each?