20180412【充实计划】第675期

昨天阅读2小时，累计阅读63小时

昨日阅读1小时，累计阅读21小时

昨日阅读1小时，累计阅读134小时

昨日阅读1小时，累积阅读155小时

DAY #12

1.主题
Data_Visualization_101_How_to_Design_Charts_and_Graphs.pdf

2.摘要
FINDING THE STORY IN YOUR DATA ——>挖出3大亮点
When you start to work with your data, it’s important to identify and understand the story you are trying to tell and the relationship you are looking to show.
When analyzing data, search for patterns or interesting insights that can be a good starting place for nding your story, such as:
TRENDS
——>Example: Ice cream sales over time
CORRELATIONS
——>Example:Ice cream sales vs. temperature
OUTLIERS
——> Example:Ice cream sales in an unusual region

KNOW YOUR DATA ——>熟知4大类别&7大关系
You must understand the types of data that can be visualized and their relationships to each other.
①.DATA TYPES : 4大类
QUANTITATIVE
——>Data that can be counted or measured; all values are numerical.
DISCRETE
——>Numerical data that has a nite number of possible values.
——>Example: Number of employees in the office.
CONTINUOUS
——>Data that is measured and has a value within a range.
——>Example: Rainfall in a year.
CATEGORICAL ——>Data that can be sorted according to group or category.
——>Example: Types of products sold.

②.DATA RELATIONSHIPS : 7大关联
NOMINAL COMPARISON
* This is a simple comparison of the quantitative values of subcategories.
* Example: Number of visitors to various websites.
DEVIATION
* This examines how data points relate to each other, particularly how far any given data point differs from the mean.
* Example: Amusement park tickets sold on a rainy day vs. a regular day.
TIME-SERIES
* This tracks changes in values of a consistent metric over time.
* Example: Monthly sales.
DISTRIBUTION
* This shows data distribution, often around a central value.
* Example: Heights of players on a basketball team.
CORRELATION
* This is data with two or more variables that may demonstrate a positive or negative correlation to each other.
* Example: Salaries according to education level.
PART-TO-WHOLE RELATIONSHIPS
* This shows a subset of data compared to the larger whole.
* Example: Percentage of customers purchasing specific products.
RANKING
* This shows how two or more values compare to each other in relative magnitude.
* Example: Historic weather patterns, ranked from the hottest months to the coldest.

CHART TYPES & DESIGN BEST PRACTICES ：——>7类图形 & 最佳设计指南
BAR CHART——>Space between bars should be 1⁄2 bar width.
PIE CHART——>visualize no more than 5 categories per chart  + order slices correctly clockwise or counterclockwise
LINE CHART——>don’t plot more than 4 lines + use solid lines only + label the lines directly + use the right height so that the line chart
takes up approximately two-thirds of the y-axis
AREA CHART——>don’t display more than 4 data categories + use transparent colors + don’t use area charts to display discrete data
SCATTER PLOT——>Use size and dot color to encode additional data variables + use trend lines to draw correlation  but no more than 2 lines
BUBBLE CHART——>All labels should be unobstructed and easily identified with the corresponding bubble.
HEAT MAP——>Some colors stand out more than others, giving unnecessary weight to that data. Instead, use a single color with varying shade or a spectrum between two analogous colors to show intensity.

3.心得感悟
要做到纲举目张，让数据讲故事，就必然首先做到眼前浮现一副图！始终为故事提供图形化指向。

可视化的终极意义就是化繁为简+化抽象为具体，从而达到化平庸为神奇。

牢记顶层架构 == 3要点+ 4数据 + 7关联 + 7图表

逻辑流程i.e.
数据 ——> 关联 ——> 图表 ——> 要点

4.时间统计
昨日阅读5小时，累计460小时

昨日阅读0.5小时，累计阅读217小时

昨日阅读1小时，累计阅读107小时。

日读0.5小时  累计35.5小时

昨日阅读时间1小时，总阅读时间247小时

昨日阅读2.5小时，本月累计阅读32.5小时