Unit 9: Data

• Correlation does not equal Causation
    * Correlation: similarities, patterns

     
  * Causation: this thing caused that thing

• Metadata: data about data
    * It can be changed without impacting the primary data
    * Used for finding, organizing, and managing information
    * Increases effective use of data by providing extra information
    * Allows data to be structured and organized
• Fact: What does the data show?
• Opinion: Why might that be the case?
• Visualizations can help us:
    * Answer questions
    * Look at lots of data at once
    * See patterns that are "invisible" if you just look at the table

• Histograms can only be created with numeric data but can be useful when a normal bar chart may be difficult to read
• Charts and other visualizations can help both find and communicate what we've learned from data
• Bar charts and histograms are two common chart types for exploring one column of data in a table
• Cleaning Data
    * Data needs to be cleaned when:
      * Data is incomplete
      * Data is invalid
      * Multiple tables are combined into one
    * “Messy” data is caused by:
      * Users enter in different types of data ("two", 2)
      * Users use different abbreviations to represent the same information ("February", "Feb", "Febr")
      * Data may have different spellings ("color", "colour") or inconsistent capitalization ("spring", "Spring")
    * Method:
      * Look through the data manually
      * Find and fix messy data
      * Use a program to find and fix messy data
    * Filtering data:
      * Allows the user to look at a subset of the data
• A crosstab chart counts how many times combinations of values appear
    * Useful for:
      * Finding the most / least common combinations of values in two columns
      * Finding patterns across two columns Exploring two columns when one or both are strings.
    * Not useful:
      * If either column has too many values (the chart would be enormous)
• Scatter: Shows combinations of values from two columns
    * Useful for:
      * Seeing patterns and trends between two values
      * Numeric data with lots of different values
    * Not useful:
      * Lots of repeated values

• Open Data
    * "sharing data with others so they can can analyze it"
    * Open data is publicly available data shared by governments, organizations, and others
    * Making data open help spread useful knowledge or creates opportunities for others to use it to solve problems
• Citizen Science and Crowdsourcing
    * "collecting data from others so you can analyze it"
    * Crowdsourcing is the practice of obtaining input or information from a large number of people via the Internet
    * Citizen science is research where some of the data collection is done by members of the public using own computing devices which leads to solving scientific problems
    * Crowdsourcing offers new models for collaboration, such as connecting businesses or social causes with funding
    * Both are examples of how human capabilities can be enhanced by collaboration via computing
• Big data
    * "Collect huge amounts of data so we can learn even more from it"
    * The size of the datasets we analyzed impacts how much information can be extracted
    * As a result, in business, science, and many other contexts people are working with increasingly big data sets
    * When data gets too big it can no longer be processed on one computer
    * Cloud computing or parallel systems are sometimes used to help process all that information
    * In general scalability of your system is important to consider when working with big data
    * You want your system to be able to work even as you're using more and more data
• Racism - prejudice, discrimination, or antagonism directed against a person or people on the basis of their membership in a particular racial or ethnic group, typically one that is a minority or marginalized