Data for Everyone: An Introduction to Spreadsheets

DATA 4 EVERYONE What do you think of when you hear “data”? Learning Objectives •Define common data terms related to health data •Prepare a spreadsheet for creating a simple data visualization •Identify further learning opportunities for working with data COMMON DATA TERMS Data is all around us Health app data: •Hours slept •Steps walked •Calories ingested Streaming platform data: •Shows watched •Reviews •Cast lists Amazon data: •Product category •Reviews •Customer data Health indicators: • Blood pressure • Cholesterol levels • # of drinks per week Wedding prep data: •Guests’ names and addresses •Food orders •Gift registry Key definitions: •Scientific Data – NIH-DMSP definition: “The recorded factual material commonly accepted in the scientific community as of sufficient quality to validate and replicate research findings, regardless of whether the data are used to support scholarly publications.” •Data Input: A way to capture or record raw data. Examples: •Enter my health indicators into a health app •Download a list of last year’s Amazon purchases •Data processing: Converting the recorded data into a more meaningful form. Examples: •Tweeze out the most important information from my list of purchases or invoices and place it into a spreadsheet. Things I may want to analyze: •Product names, prices, dates of purchases, gift or personal item Example spreadsheet based on purchases Spark-The-Definitive-Guide/data/retail-data/all/online-retail-dataset.csv at Databricks: Spark: The Definitive Guide, Copyright [2015] Databricks Inc, Bill Chambers, Matei Zaharia https://github.com/databricks/Spark-The-Definitive-Guide/blob/master/data/retail-data/all/online-retail-dataset.csv Key definitions: •Data Analysis: To extract meaningful information from data. Examples: •Calculate how much I’ve spent at an online retail site during one month •Determine whether I spend more in some months vs others •Determine how often I purchase repeat items, to see if I should “subscribe & save” •Metadata: Information that describes, explains, and otherwise contextualizes data •Variable: A symbol (be it text-based, numerical, date formatted, etc.) that represents information. It is not fixed in value, but can vary based on circumstances •Database: An organized collection of data stored electronically Demonstration: Google Sheets Step 1: collecting data using a poll Poll: What pets does this group have? A. Dogs B. Cats C. Both cats and dogs D. Other E. None Step 2: Setting up a spreadsheet. • Setting up columns. • Entering values. Step 3: Creating a data visualization. • Select data • Insert > Chart Activity: Google Sheets Question: Does this group prefer chocolate or vanilla flavor? Step 1: collecting data using a poll A. Chocolate B. Vanilla Step 2: Setting up a spreadsheet. • Setting up columns. • Entering values. Step 3: Creating a data visualization. • Select data • Insert > Chart QUESTIONS? FURTHER LEARNING OPPORTUNITIES NNLM Data Pathways You can find free NNLM classes on the new Data Pathways Guide! Introductory Classes On Demand Classes •Data Services On Demand (4 modules) •A Bird's Eye View of Health Data Standards •Common Data Elements: Standardizing Data Collection Webinars/Recordings •Making Sense of Numbers: Communicating Numerical Health Information •Health Statistics on the Web •The Research Data Services Landscape: How Do You Start And Where Does Your Library Fit In? •Greatest Crimes in Statistics Skill Building Data management •Research Data Management On Demand •Creating Data Management Plans with the DMPTool •Fundamentals of Health Sciences Research Data Management Data science •Open Tools for Data De-identification •Fundamentals of Health Data Science NIH Data Management and Sharing Policy •NIH Data Management and Sharing Policy Overview •NIH Data Management and Sharing Policy Workshop On-Demand •NIH Data Management and Sharing Requirements Series – practitioners perspectives •The NIH Data Management and Sharing Policy for non-data librarians – recording available now! Deep Dive (non-NNLM) •The Carpentries •Library Carpentry •Data Carpentry •Software Carpentry •Data Services Continuing Professional Education (DSCPE) • Cohorts run for 10 weeks in the Fall Further Resources •Data Literacy Project (free courses) •GSU Data Ready! Badges (recordings) •Top data literacy skills for becoming data literate (article)