Data is for Everyone

Learning outcomes:

Data can be used by people or companies to make choices about their behaviors in the future, or to look back on what choices they've made
Even things like budgets are based on data you've collected even if you didn't realize it (going back through previous bills is data collection)
The better we can understand and collect our data, the better our choices can be
The better our data the more accurate our predictions and past trends will be

Why we gather data
Data can be used in a variety of applications from predicting what a store should stock based on previous sales, to what the weather could be like next week
Is your data discrete or continuous?
How should your data be organized in general?

Group books by Genre? Or Trope? Recommended age range?
For example, Don't have a column "Money" when you could split out sales and profits.

First steps are to define the problem and outline your process to solve, Make sure to include what a "success" means!
Collect and structure your data
Visualize and analyze your data
Decide how the data can change your next step

Celsius vs Fahrenheit, or meters vs feet
Make sure to collect the same data (if it's books, collect title AND author not OR author)
Make sure format such as text, or integer or float is consistent

Data accuracy is important! Don't fudge or make things up. Blank is better than inaccurate
Measure all data in the same way (if you use a ruler make sure you are consistent with where you consider the "start")

Make sure it's in the same order so you don't have confusion of things like title vs author vs editor
If data is starting in something like a spreadsheet, rows vs columns is important!
Data labels are important, make sure each row, column or other is clearly labeled
You may want to specify the type of data expected, such as text, or single char, or integer, or float.

Data analysis is where we collect, process, define, and clean our data so that we can finally do something with it
Data analysis can be done visually by a human to see trends, or with large computer programs if our data set is too big for a person (most common)
The type of analysis we do will depend on the data

Numbers might use a statistical analysis, and would should trends like less books borrowed on a Tuesday
Text analysis might show trends such as people are less satisfied with their books in January because the reviews contain more negative words

When and how to vizualise your data
How we can take numbers, or text, or data and present it in a way it's easier for humans to see
This can be done in graphs, or charts to show trends
Data visualization is important to make sure other people can follow what's going on in your data, BUT it can be manipulated!
Data visualizations can show anything depending on bias
Data visualizations can be good or bad, confusing or clear, depending on the choices you make
Everything from colour, to size of visual, to measurements matter!
Raw data is unlikely to be helpful when looked at
Examples of Horrible data visualizations

What is your goal with your data
Do you want to show sales in the month to see when you need more hours worked? More stock ordered? More employees on call? Different kinds of stock?
Is the data you've gathered and analyzed enough or do you need more? Does monthly make sense? Yearly? Daily? What's reasonable for your goal?
Difference Between Descriptive and Predictive Data Mining

This is about how we describe and summarize our data
This type of data might show anomalies and past trends
Might be used to generate reports on previous behaviors or see if there is a potential correlation (such as people buy less books during wage downturns)

This is about what you think might happen in the future based on history
This could be predicting market trends for what to order in a store, or stock for a holiday
Might be used to try and guess what's going to happen next such as what stock we should have in a store next month

Businesses use data every day to make choices on everything from what to stock, and what to order and what they might need to pre-order way in advance
Humans use data as well for our choices

If your kids eat grilled cheese twice a week you know how much bread you might need
If in previous holiday seasons you spent $200 on presents, you know how much to save for this holiday

Suggested Activities and Discussion Topics:

In small groups or pairs, please discuss the following: In today's digital age, data surrounds us, shaping our decisions, influencing our experiences, and even impacting our society. This discussion aims to explore the pervasive role of data and why it's essential to develop data literacy skills. Share Your Experiences: Share one or two specific examples of how data has influenced your decision-making or experiences. These examples can be positive, negative, or even neutral. For instance, you might discuss how personalized product recommendations influenced a recent online purchase or how data breaches affected your perception of online security.
In small groups or pairs, Consider the following questions:
- How has the prevalence of data collection and analysis changed in recent years?
- What are some common sources of data in your daily routine?
- Do you feel that you have control over your personal data, or is it often collected without your explicit consent?
Include at least 1 article on the topic of data in modern life. In your discussion posting you should link your article, give a quick (5 sentences or less) summary, and include your opinion on the quality of the article using the CRAAP method as described HERE
Activity: Download this PDF and the sample data from here and follow the instructions from the PDF to collect your own data

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