Data Analysis part 1

Data Literacy

The ability to explore, understand, and communicate with data.

Four steps of the Math and Data Biography Exercise

1. List experience with math, data, research, and statistics; 2. Identify which experiences were positive and what worked well; 3. Describe any challenges in those experiences; 4. Identify Key Takeaways.

Key habit for developing data literacy skills

Create space and time for practice!

Carl Friedrich Gauss (1777–1855) quote on mathematical truths

"If others would but reflect on mathematical truths as deeply and continuously as I have, they would make my discoveries."

Tableau Community Forums

An online hub with over 150,000 users where you can ask questions on the Forums, connect with User Groups locally and online, and submit product Ideas.

Four ways students can use Tableau (Academic Student Page)

1. In class (analyzing/presenting class assignments); 2. At home (learning at your own pace via eLearning); 3. For fun (visualizing passions like Instagram data or the Marvel Cinematic Universe); 4. For good (promoting social causes).

VizForSocialGood

A hashtag used on Tableau Public to share visualizations that promote a social cause, such as the International Coastline Cleanup or Gender Equality.

TUGs

Tableau User Groups, which connect users through local and online events and hubs (such as the Seattle User Group).

STUGs

Student Tableau User Groups (such as the UW Tableau Student User Group, @UWTSUG).

Student Viz Contest

A Tableau competition showcasing student data visualizations (such as analyses of the Premier League Season or NBA Triple Doubles).

Albert Einstein (1879–1955) quote on asking questions

"If I had an hour to solve a problem and my life depended on it, I would use the first 55 minutes determining the proper questions to ask"

John Tukey (1915–2000) quote on questions in data analysis

"Far better an approximate answer to the right question, which is often vague, than the exact answer to the wrong question, which can always be made precise."

Steve Few's 7 Aptitudes and Attitudes of Effective Analysts

Interested, Curious, Self-motivated, Open-minded, Imaginative, Skeptical, and Aware.

Florence Nightingale (1820–1910)

Historical figure highlighted for asking great questions and visualizing mortality data from the Crimea and Bulgaria (1854–1855).

John Snow (1813–1858)

Historical figure who investigated the London cholera outbreak by mapping cholera deaths alongside water pump sites around Broad Street and Golden Square.

Not a data analysis (Data Analytic Question Flowchart)

Occurs when you do not summarize the data ("Did you summarize the data?" -> No).

Descriptive Data Analysis

Summarizing the data and reporting the summaries without interpretation ("Did you summarize the data?" -> Yes; "Did you report the summaries without interpretation?" -> Yes).

Exploratory Data Analysis

Summarizing and interpreting the data without quantifying whether the discoveries are likely to hold in a new sample ("Did you report the summaries without interpretation?" -> No; "Did you quantify whether your discoveries are likely to hold in a new sample?" -> No).

Inferential Data Analysis

Quantifying whether discoveries are likely to hold in a new sample ("Yes"), without trying to predict measurements for individuals ("No") or determining how changing the average of one measurement affects another ("No").

Predictive Data Analysis

Quantifying whether discoveries are likely to hold in a new sample ("Yes") while trying to predict measurement(s) for individuals ("Yes").

Causal Data Analysis

Figuring out how changing the average of one measurement affects another ("Yes") when looking for an average effect ("Average").

Mechanistic Data Analysis

Figuring out how changing the average of one measurement affects another ("Yes") when looking for a deterministic effect ("Deterministic").