WEEK 5 OF MY DATA ANALYTICS JOURNEY

I am in the 5th phase of Data Analytics which is the SHARE PHASE
 Recall
➡️ ASK PHASE
 ➡️ PREPARE PHASE
 ➡️ PROCESS PHASE
 ➡️ ANALYZE PHASE
 ⭕ SHARE PHASE (VISUALIZATION PHASE)
 ➡️ ACT PHASE
 
 DATA VISUALIZATION is the graphical representation and presentation of your data.
 FRAMEWORKS help organize your thoughts about data visualization and gives you a useful checklist to reference as you plan and evaluate your data visualization.
 There are 2 frameworks that employ slightly different techniques. Both are intended to improve the quality of your visuals.
 - The McCandles Method: It has 4 elements of good data 
 visualization. 
 💠 Information: The data which you're working with.
 💠 Story: A clear and compelling narrative or concept.
 💠 Goal: A specific objective or function for the visual.
 💠 Visual Form: An effective use of metaphor or visual 
 expression.
 - Kaiser Fung's Junk Charts Trifecta Checkup: This approach is a set of questions that can help consumers of data visualization critque what they are consuming and determine how effective it is.
You can also use these questions to determine if your data visualization is effective.
 💠 What is the practical question?
 💠 What does the data say
 💠 What does the visual say
The essential building blocks that make visuals immediately understandable are called MARKS and CHANNELS.
 ➡️ MARKS are basic visual objects such as points, lines and shapes. Every
 mark can be broken down into qualties
 ⭕ Position: Where is a specific mark in space relative
 to a scale or to other marks?
 ⭕ Size: How big, small, long or tall is a mark?
 ⭕ Shape: Does the shaoe of the object communicate 
 something about it?
 ⭕ Color: What color is the mark?
 
 ➡️ CHANNELS are visual aspects or variable that represent characteristics
 of the data in a visualization. They are basically visualized marks that 
 have been used to visualize data.
 ⭕ Accuracy: Are channels helpful to accurately estimate
 the values being represented?
 ⭕ Pop out: How easy is it to distinguish certain 
 values from others?
 ⭕ Grouping: How effective is a channel at communicating 
 groups that exist in data?
the different types of visuals to use when you want to visualize your data.
 
 ➡️ BAR GRAPHS: Use size contrast to compare two or more values.
 ➡️ PIE CHARTS: Shows how much each part of something makes up a
 whole.
 ➡️ MAPS: Helps organize data geographically.
 ➡️ HISTOGRAM: A chart that shows how often data values fall into certain
 range.
 ➡️ LINE CHART: Is used to track changes over short and long periods of 
 time. When smaller changes exists, lines charts are better to use than 
 BAR CHARTS.
 ➡️ COLUMN CHARTS: Use size to contrast and compare two or more
 values. Using height or lenghts to represent the specific values.
 ➡️ HEAT MAP: Similar to BAR CHART, heat maps also use color to compare
 categories in a dataset. 
 ➡️ SCATTER PLOT: Show relationships between different variables. 
 Scatterplots are typically used for 2 variables for a set of data
 although additional variables can be displayed.
 ➡️ DITRIBUTION GRAPH: Displaysy the spread of various outcome 
 in a dataset.
 ➡️ CORRELATION CHARTS: Shows relationship among data. It is the
 measure of the degree to which 2 variables move in relation to each 
 other. If one variable goes up and the other one goes down, it
 is a NEGATIVE or INVERSE CORRELATION. If one variable goes up and the 
 other one goes up, it is a POSITIVE CORRELATION. If one variable goes 
 up and the other values stays thesame , there is no CORRELATION.
 CAUASATION refers to the idea that an event leads to a specific outcome
 STATISTIC VISUALIZATION do not change over time unless they are edited
 DYNAMIC VISUALIZATION are interactive and they change overtime.
 There are meaningful patterns in data visualization, and they can take many forms
 
 💠 CHANGE. This is a trend or instance of observation that becomes 
 different overtime. A great way to measure change in data is through 
 a LINE CHART or COLUMN CHART.
 💠 CLUSTERING. A collection of data points with similar or differnt values. 
 This is best represented through a DISTRIBUTION GRAPH.
 💠 RELATIVITY: These are observations considerec in relation or in 
 proportion to something else. They are best represented using PIE
 CHART.
 💠 RANKING. This is a position in a scale of achievement or status. Data
 that requires ranking is best represented by a column chart .
I also learnt about DECISION TREES. Data grows on DECISION TREES.
 A DECISION TREE is a decision making tool that allows you the data analyst, to make decisionss based on key questions that you can ask youself.
 I also learnt about the 9 basic principles of design;
 💠 BALANCE. The design of a data visualization is balanced when the key visual elements like color and shape are distributed evenly.
 💠 EMPHASIS. Your data visualization should have a focal point, so that your audience knows where to concerntrate. In otherwords, your visualizations should emphasize the most important data so that users recognize it first. You can use colors and values.
 💠 MOVEMENT. It can refer to the path the viewers eye travel as they look at a data visualizations or literal movement crreated by animation.
 💠 PATTERNS. You can use similar shapes and colors to create patterns in your data visualization. You can sue patterns to highlight similarities between different datasets, or break up a pattern with a unique shape, color, or line to create more emphasis.
 💠 REPITITION. Repeating chart types, shapes, or colors adds to the effectiveness of our visualization.
 💠 PROPORTION. Using various colors and sizes helps demonstrate that you are calling attention to a specific visual over others.
 These first 6 principles of design are key considerations that you can make while you are creating your data visualization.
 These next 3 principles are useful checks once your data visualization is finished
 💠 RYTHM. This refers to creating a sense of movement or flow in your visualization. If your finished design doesnt succesffully create a flow. You might want to rearrange some of the elements to improve the rythm.
 💠 VARIETY. Your visualizations should have some variety in the chart type, lines, shapes, colors and values you use. Variety keeps the audience engaged but it is good to find balancesince too much variety can confuse people.
 💠 UNITY. Your final data visualization should be cohesive. If the visual is disjointed or not well organized, it will be confusing and overwhelming.
 ELEMENTS OF ARTS
 - LINE
 - SHAPE
 -COLOR
 -SPACE
 -MOVEMENT
 
 
 5 PHASES OF THE DESIGN PROCESS 
 ➡️ EMPHATHIZE: Thinking about the emotions and needs of the target audince for the data visualization.
 ➡️ DEFINE: Figuring out exactly what your audience needs from the data.
 ➡️ IDEATE: Generating ideas for data visualization.
 ➡️ PROTOTYPE: Putting visualization together for testing and feedback.
 ➡️ TEST: Showing prototype visualizations to people before stakeholders see them.
I learnt about data visualization with TABLEAU
 TABLEAU is a business intelligence and analytics plateform that helps people
see, understand and make decisions with data.
 There are DESIGN PRINCIPLES IN TABLEAU which are;
 - Choose the right visuals
 - Optimize the data-ink ratio
 - Use orientation effectively 
 - Number of elements
 - Avoid misleading and or deceptive charts
 Some common mistakes to avoid so that your visualizations aren't 
 accidentally misleading.
 - Cutting of the y-axis
 - Misleading use of a dual 
 - Artifically limiting the scope of your data (not showing all of the data)
 - Problematic choices in how data is binned or grouped
 - Using part to whole visuals when the totals do not sum up appropriately
 - Hiding trends in cummulative charts
 - Artificially smoothing trends
 
 Few rules about what makes a helpful data visualization.
 
💠 FIVE SECOND RULE: A data visualization should be clear, effective and 
 convincing enough to be absorbed in five seconds or less.
 💠 COLOR CONTRAST : Graphs and charts should use a diverging color 
 palette to show contrast between elements.
 💠 CONVENTIONS and EXPECTATIONS: Visuals and their organization
 should align with audience expectations and cultural conventions.
 💠 MINIMAL LABELS: Title, axis and annotations should use a few labels
 as it takes to make use. Having too many labels makes your graphs
 or charts too busy. It takes up too much space and prevents the labels 
 from being shown clearly.