1. Concept
1.1. Data-inspired decision-making
1.1.1. explores different data sources to find out what they have in common
1.2. Algorithm
1.2.1. A process or set of rules to be followed for a specific task
2. Qualitative and quantitative data
2.1. quantitative data
2.1.1. specific and objective measures of numerical facts
2.1.1.1. The what?
2.1.1.2. How many?
2.1.1.3. How often?
2.1.2. visualized as charts or graphs
2.1.3. structured interviews, surveys, polls
2.2. qualitative data
2.2.1. subjective or explanatory measures of qualities and characteristics
2.2.1.1. Why?
2.2.1.2. How?
2.2.2. focus groups, social media text analysis, in-person interviews
3. Report
3.1. static collection of data given to stakeholders periodically
3.1.1. Pros
3.1.1.1. high-level historical data
3.1.1.2. easy to design
3.1.1.3. pre-cleaned and sorted data
3.1.2. Cons
3.1.2.1. continual maintenance
3.1.2.2. less visually appealing
3.1.2.3. static
4. Dashboard
4.1. monitors live, incoming data
4.1.1. Pros
4.1.1.1. dynamic, automatic, and interactive
4.1.1.2. more stakeholder access
4.1.1.3. low maintenance
4.1.2. Cons
4.1.2.1. labor-intensive design
4.1.2.2. can be confusing
4.1.2.3. potentially uncleaned data
4.2. types of dashboards
4.2.1. strategic: focus on long term goals and strategies at the highest level of metrics
4.2.2. operational: short-term performance tracking and intermedia goals
4.2.3. analytical: consists of the datasets and the mathematics used in these sets
5. Mathematical thinking
5.1. small data
5.1.1. specific
5.1.2. short time-period
5.1.3. day-to-day decisions
5.2. big data
5.2.1. large and less specific
5.2.2. long time-period
5.2.3. big decisions
5.2.4. 4 V
5.2.4.1. volume: the amount of data
5.2.4.2. variety: the different kinds of data
5.2.4.3. velocity: how fast the data can be processed
5.2.4.4. varacity: the quality and reliability of the data
6. Structured thinking
6.1. level of using data
6.1.1. descriptive
6.1.1.1. what happened?
6.1.2. diagnostic
6.1.2.1. why did it happen?
6.1.3. predictive
6.1.3.1. what will happen?
6.1.4. prescriptive
6.1.4.1. how can we make it happen?
6.2. definition
6.2.1. structured thinking
6.2.1.1. the process of recognizing the current problem or situation, organizing available information, revealing gaps and opportunities, and identifying the options
6.2.2. problem domain
6.2.2.1. the specific area of area that encompasses every activity affecting or affected by the problem
6.2.3. Scope of work
6.2.3.1. an agreed-upon outline of the work you're going to perform on a project
6.2.3.1.1. deliverables
6.2.3.1.2. timeline
6.2.3.1.3. milestone
6.2.3.1.4. report
6.2.4. context
6.2.4.1. the condition in which something exists or happens
6.2.4.1.1. context can turn raw data into meaningful information
7. Communication
7.1. 4 questions
7.1.1. who is your audience is
7.1.2. what they already know
7.1.3. what they need to know
7.1.4. how can you communicate effectively to them
8. six data analysis phases
8.1. ask
8.1.1. define the problem you're trying to solve
8.1.2. make sure you fully understand the stakeholder's expectations
8.1.3. focus on the actual problem and avoid any distractions
8.1.4. collaborate with stakeholders and keep an open line of communication
8.1.5. take a step back and see the whole situation in context
8.1.6. questions to ask yourself
8.1.6.1. 1. What are my stakeholders saying their problems are?
8.1.6.2. 2. Now that I've identified the issues, how can I help the stakeholders resolve their questions?
8.2. prepare
8.2.1. what metrics to measure
8.2.2. locate data in your database
8.2.3. create security measures to protect that data
8.2.4. questions to ask yourself
8.2.4.1. 1. What do I need to figure out how to solve this problem?
8.2.4.2. 2. What research do I need to do?
8.3. process
8.3.1. using spreadsheet functions to find incorrectly entered data
8.3.2. using SQL functions to check for extra spaces
8.3.3. removing repeated entries
8.3.4. checking as much as possible for bias in the data
8.3.5. questions to ask yourself
8.3.5.1. 1. What data errors or inaccuracies might get in my way of getting the best possible answer to the problem I am trying to solve?
8.3.5.2. 2. How can I clean my data so the information I have is more consistent?
8.4. analyze
8.4.1. perform calculations
8.4.2. combine data from multiple sources
8.4.3. create tables with your results
8.4.4. questions to ask yourself
8.4.4.1. 1. What story is my data telling me?
8.4.4.2. 2. How will my data help me solve this problem?
8.4.4.3. 3. Who needs my company's product or service? What type of person is most likely to use it?
8.5. share
8.6. act
9. Common problem types
9.1. making predictions
9.1.1. using data to make an informed decision about how things may be in the future
9.2. categorizing things
9.2.1. assigning information to different groups or clusters based on common features
9.3. spotting something unsual
9.3.1. identifying data that is different from the norm
9.4. identifying themes
9.4.1. grouping categorized information into broader concepts
9.5. discovering connections
9.5.1. finding similar challenges faced by different entities and combining data and insights to address them
9.6. finding patterns
9.6.1. using historical data to understand what happened in the past and is therefore likely to happen again
10. SMART
10.1. S: specific questions are simple, significant and focused on a single topic or a few closely related ideas
10.2. M: measurable questions can be quantified and assessed
10.3. A: action-oriented questions encourage change
10.4. R: relevant questions matter, are important, and have significance to the problem you're trying to solve
10.5. T: time-bound questions specify the time to be studied
11. fairness
11.1. ensuring that your questions don't create or reinforce bias
12. Things to avoid when asking questions
12.1. leading questions
12.1.1. questions that only have a particular response
12.2. closs-ended questions
12.2.1. questions that ask for a one-word or brief response only
12.3. vague questions
13. Spreadsheet
13.1. errors and fixes
13.1.1. #DIV/0!
13.1.1.1. a formula is trying to divide a value in a cell by O or by an empty cell
13.1.2. #ERROR!
13.1.2.1. a formula can't be interpreted as input (also known as parsing error)
13.1.3. #N/A
13.1.3.1. data in a formula can't be found by the spreadsheet
13.1.4. #NUM!
13.1.4.1. a formula or function calculation can't be performed as specified
13.1.5. #VALUE!
13.1.5.1. a general error that could indicate a problem with a formula or referenced cells
13.1.6. #REF!
14. Stakeholders
14.1. definition
14.1.1. stakeholders
14.1.1.1. people that have invested time, interest and resources into the projects you'll be working on as a data analyst
14.1.1.1.1. executive team
14.1.1.1.2. customer-facing team
14.1.1.1.3. data science team
14.1.2. turnover rate
14.1.2.1. the rate at which employees leave a company
14.2. focus on what matters
14.2.1. Who are the primary and secondary stakeholders?
14.2.2. Who is managing the data?
14.2.3. Where can you go for help?