This presentation was provided by William Mattingly of the Smithsonian Institution, for the initial session of NISO's 2023 Training Series on Text and Data Mining. Session one "Introduction to Text and Data Mining," was held on Thursday, October 12, 2023.

1. Text and Data Mining
Data, Data Structures, and Python

2. Data alone has no utility

3. Data is the new oil

4. Basic Data Types

5. Basic Data
Types
Numbers
Strings
Booleans

6. Numbers
Integers
Whole Numbers
Example: 1, 2, 3

7. Numbers
Floats
Decimal Numbers
Example: 1.0, 2.0, 3.0

8. Strings Textual Data
Example: “This is a string”

9. Strings Numerical Strings
Example: “1”, “2”, “3”

10. Booleans True or False

11. None None

12. Basic Data Structures

13. Basic Data
Structures
Lists (Arrays)
Tuples
Sets
Dictionaries

14. Lists or Arrays Lists of Data or Data Structures
Example: [1, 2, 3.0, “four”]

15. Tuples Lists that cannot be changed
Example: (1, 2, 3)

16. Sets Lists where no value can appear
more than once
Example: [1, 2, 3, 3.0, “3”]

17. Dictionaries
Relational Data with Key, Value
Pairs
Example: {“name”: “Betty”,
“age”: 20}

18. Complex Data Types and Structures

19. Time Series
Data

20. Time Series
Data

21. Spatial Data

22. Time Series
Data

23. Network Data

24. Multimedia Data
Image

25. Multimedia Data
Audio

26. Multimedia Data
Video

27. Data Files

28. Data Files
CSV
JSON
HTML
XML
Serialized Data Files

29. CSV Comma Separated Value

30. CSV

31. JSON JavaScript Object Notation

32. JSON

33. HTML HyperText Markup Language

34. HTML
<p>
Not that <span class="person">Belladonna
Took</span> ever had any adventures after she
became Mrs. <span class="person">Bungo
Baggins</span>.
<span class="person">Bungo</span>, that was
<span class="person">Bilbo</span>’s father, built
the most luxurious hobbit-hole for her
(and partly with her money) that was to be found
either under <span class="place">The Hill</span>
or over <span class="place">The Hill</span>
or across <span class="place">The Water</span>,
and there they remained to the end of their days.
</p>

35. XML eXtensible Markup Language

36. XML
<text>
<sentence>
Not that <person>Belladonna Took</person> ever had any
adventures after she became Mrs. <person>Bungo Baggins</person>.
</sentence>
<sentence>
<person>Bungo</person>, that was <person>Bilbo</person>’s
father, built the most luxurious hobbit-hole for her
(and partly with her money) that was to be found either under
<place>The Hill</place> or over <place>The Hill</place>
or across <place>The Water</place>, and there they remained to
the end of their days.
</sentence>
</text>

37. Working with Data Programmatically

38. Python
Pros:
Versatility
Deep Learning
Integration
Community Support
General Purpose
Cons:
Performance
Pros:
Statistics
Visualizations
Data Analysis Packages
Cons:
Not General Purpose
Learning curve
Integration
R