202312 Exploration of Data Analysis Visualization
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This document provides a tutorial on data visualization and analysis using Orange 3. It discusses different types of charts like pie charts, line charts, histograms, bar charts, scatter plots, box plots, and pivot tables. It demonstrates how to visualize survival rates from the Titanic dataset based on features like sex, passenger class, age, and fare paid. Key findings are that women and higher class passengers had higher survival rates, and survival rates also depended on combinations of these features.
202312 Exploration of Data Analysis Visualization
- 2. About me • Education • NCU (MIS)、NCCU (CS) • Work Experience • Telecom big data Innovation • AI projects • Retail marketing technology • User Group • TW Spark User Group • TW Hadoop User Group • Taiwan Data Engineer Association Director • Research • Big Data/ ML/ AIOT/ AI Columnist 2
- 3. Tutorial Content 3 Data Science Process Exploration Data Analysis and Visualization Home work
- 4. Code • Download materials: • https://drive.google.com/drive/folders/1KPC3K19_vJgRb5Op5M9bqQYJEh2zv rnr?usp=sharing 4
- 5. Get ready to your Orange 3 • Version: 3.36.1 5
- 6. 6 The Pyramid of Data Needs (and why it matters for your career) | by Hugh Williams | Medium
- 7. 7 The Pyramid of Data Needs (and why it matters for your career) | by Hugh Williams | Medium
- 8. Static chart • There are generally THREE STEPS in drawing a chart: • Observing the data, determine the relationship, and select the chart. • What type of data it is, and what content you want to express. • Category • Numeric • Text • Datetime • After clarifying the content to be expressed, you can choose which chart to use to express it. 8
- 9. Pie chart • You must have some kind of whole amount that is divided into a number of distinct parts. • Your primary objective in a pie chart should be to compare each group’s contribution to the whole. 9
- 10. Line chart • Line charts provide the clearest graphical representation of time- related variables and are the preferred mode for representing trends or variables over time. 10
- 11. Histogram chart • It is used to summarize discrete or continuous data that are measured on an interval scale. • It is often used to illustrate the major features of the distribution of the data in a convenient form. 11
- 12. Bar chart • It provides a way of showing data values represented as the comparison of multiple data sets side by side. 12
- 13. Differences between histogram and bar chart Comparison terms Bar chart Histogram Usage To compare different categories of data. To display the distribution of a variable. Type of variable Categorical variables Numeric variables Rendering Each data point is rendered as a separate bar. The data points are grouped and rendered based on the bin value. The entire range of data values is divided into a series of non- overlapping intervals. Space between bars Can have space. No space. Reordering bars Can be reordered. Cannot be reordered. 13
- 14. Scatter Plot • It uses dots to represent values for two different numeric variables and observe relationships between variables. 14
- 15. Box plot • Q1: The first quartile (25%) position. • Q3: The third quartile (75%) position. • Interquartile range (IQR) • Lower and upper 1.5*IQR whiskers: These represent the limits and boundaries for the outliers. • Outliers: Defined as observations that fall below Q1 − 1.5 IQR or above Q3 + 1.5 IQR. 15
- 16. Dataset description • Using this dataset to predict whether passengers will survive the Titanic accident 16
- 17. Data Summary • Load titanic.csv • Data description • Names, Types, Role, Values • Change the Columns 17
- 18. Data Summary • Missing values • Using Features Statistics Widget • How about those missing ratios? 18
- 19. Preprocess (Remove or Impute columns) • Remove columns 19
- 20. Preprocess (Remove or Impute columns) • Impute columns • For Default Method • For each column 20
- 21. Pie chart • Orange 3 has deprecated Pie chat widget • Use python script instead • Find the output file 21
- 22. Line chart • Typically, trend analysis charts are presented together with time-based data 22
- 23. Distribution chart • Used to present by sorting frequency • In Orange 3, both of numeric or category data can be presented here • Bar chart widget is not used much compared to others 23
- 24. Scatter plot • It used to observe the degree of correlation between features • positive correlation • negative correlation • noncorrelation 24
- 25. Box plot • Comparing multiple features with each other 25
- 26. Pivot Table • It summarizes the data of a more extensive table into a table of statistics. • The statistics can include sums, averages, counts, etc. 26
- 27. 1. Show me top 10 data rows • Hint: Use Data Sampler widget 27
- 28. 2. Show me dataset info • How many Rows? • How many Features? • All information like this! 28
- 29. 3. Get a count of the number of survivors 29
- 30. 4. Survival Conclusion • For features, SEX, PCLASS, SIBSP, PARCH, EMBARKED • Women had a higher chance of survival than men. • First-class passengers had a higher chance of survival. • Passengers with siblings, spouses had a higher chance of survival. • Passengers with children and parents had a higher chance of survival. • Departing from the S terminal may lead to lower cabin class and lower chances of survival. 30
- 31. 5. Show me sex survival rate 31
- 32. 6. Look at survival rate by SEX and PCLASS • Women in first class had a survival rate as high as 96.8%. In contrast, men in economy class only had a 13.54% chance of survival 32
- 33. 7. Look at survival rate by SEX, AGE and PCLASS • In the event of a disaster, women in first class or business class have a 90% chance of survival regardless of age. • On the other hand, if a man is in economy class and older than 18, the chance of survival is only 13.36%. • To summarize, in a disaster scenario, girls and women have a higher chance of survival compared to boys and men. • Additionally, the higher the class (such as first class), the higher the chances of survival. 33
- 34. 8. The price paid of each class • Try to plot Pclass and Fare chart to visualize data • Every seat had someone board for free, while others spent over 500 pounds for a first-class ticket. It's quite an interesting observation! 34