Please enable JavaScript.

Coggle requires JavaScript to display documents.

Data Literacy Fundamentals, Well-structured data - Coggle Diagram

- - - - Research question - specific, use clearly defined terms, narrow focus to a specific population. Explore data to answer question..
        
        "How do dog owners and non-dog owners compare in health outcomes among people with chronic illnesses in the United States?"
        
        Follow-up question - usually not enough after answering original question.
        If, it turns out, that health outcomes among people with chronic illnesses in the United State vary with dog ownership, the next question is "why?"
        
        Determine the cause of the outcome.
        Asking "why" of a problem that's been identified, and then continuing to ask "why" for each answer or explanation given.
        Find hidden insights in your data: Ask why and why again
        
        Too broad; no clear criteria or key metric: "Is owning a dog
        good for one's health?"
        
        Aptitudes and attitudes that inspire good questions and traits for working effectively with data
        
        Interest, curiosity, and imagination
        
        Interest- Are you genuinely passionate about a topic? Does this passion engage your mind?
        
        Curiosity- Do you enjoy figuring things out, wonder how things work, and crave information?
        
        Imagination- Do you have a knack for coming up with new things to try, over and over, until you achieve your goal?
        
        Self-motivation - Are you driven to explore and to understand? Do you pursue new questions without hesitation? Do you go beyond the scope of what's expected without waiting to be told what to do?
        
        Open-mindedness and flexibility - Are you willing to accept what you find? Are you willing to admit when you're wrong?
        
        Pattern spotting, healthy skepticism , and awareness of what's worthwhile.
        
        Pattern spotting - Can you spot patterns that are meaningful and ignore ones that are not?
        
        Awareness of what's worthwhile.- Do you know how to prioritize questions in order to see the difference between a worthwhile pursuit versus one that would require a great deal of time but yield few results?
        
        Healthy skepticism - Even when you're confident about an answer or a result, do you look at it from a different perspective to possibly learn something new?
        
        Methodicalness- Do you learn an efficient set of steps and then repeat them according to a proven method?
        
        Ability to analyze and ability to synthesize
        
        Ability to analyze - Are you able to examine something complex, and recognize its many parts and how these parts interact to form a whole? .
        
        Ability to synthesize. - Are you able to see "the big picture" by putting a collection of parts together to form a whole?
        
        Familiarity with the data - Even if you're new to working with data, do you make sure you become familiar with the facts so you don't jump to conclusions that may be inaccurate?
        
        Skills of data analysis - can be developed through training, experience, and lots of practice.
- - - - Relational databases - store data in multiple tables, with each row assigned a unique identifier. Users pull data from different tables together using Structured Query Language (SQL). The "relational" aspect indicates a logical connection between different tables.
      - Cloud Data - some users prefer to store their data in the cloud. This includes data stored in such places as Amazon Web Services or Microsoft Azure.
      - Spreadsheets - Microsoft Excel or Google sheets, organize data in a flat structure, which means the records are stored as single rows of data.