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import networkx as nx
import matplotlib.pyplot as plt

G = nx.DiGraph()
G.add_node("user", pos=(550, 500))
G.add_node("system", pos=(-550, 500))
G.add_node("program", pos=(-2000, 960))
G.add_node("syntax", pos=(2000, 950))
G.add_node("ado", pos=(-3000, 550))
G.add_node("do", pos=(3000, 550))
G.add_node("command", pos=(-1900, 150))
G.add_node("queue", pos=(1900, 150))
G.add_node("results", pos=(0, 0))
G.add_node("dta", pos=(0, -475))
G.add_node("log", pos=(-1900, -475))
G.add_node("excel", pos=(-4000, -475))
G.add_node("word", pos=(1900, -475))
G.add_node("html", pos=(4000, -475))
G.add_node("publish", pos=(0, -950))

G.add_edges_from([("program", "ado"), ("syntax", "do")])
G.add_edges_from([("ado", "command"), ("do", "queue")])
G.add_edges_from([("command", "results"), ("queue", "results"), ("results", "excel"), ("results", "word"), ("results", "html")])
G.add_edges_from([("results", "dta"), ("results", "log")])
G.add_edges_from([("dta", "publish"), ("log", "publish"), ("excel", "publish"), ("word", "publish"), ("html", "publish")])

pos = nx.get_node_attributes(G, 'pos')
labels = {"user": "user",
          "system": "system",
          "program": "program",
          "syntax": "syntax",
          "ado": "ado",
          "do": "do",
          "command": "command",
          "queue": "queue",
          "results": "results",
          "dta": ".dta",
          "log": ".log",
          "excel": ".xlsx",
          "word": ".docx",
          "html": ".html",
          "publish": "publish"}

plt.figure(figsize=(8, 8))
nx.draw(G, pos, with_labels=False, node_size=3000, node_color="lightblue", linewidths=3)
nx.draw_networkx_labels(G, pos, labels, font_size=10, font_weight='bold')
plt.xlim(-5000, 5000)
plt.ylim(-1000, 1000)
plt.axis("off")
plt.show()

1. CoursePlus - Post questions, dropoff homework, receive feedback

2. User - Understanding user interface in Stata

3. System - Understanding the Stata system environment

4. Syntax - Basics of Stata syntax

5. Command - Mastering Stata commands

6. Do-file - Learning to write and execute Do-files

7. Ado-file - Creating and using Ado-files

8. Program - Building your own Stata programs

9. Queue - Managing tasks and operations with Queue

10. Dataset-formats - Understanding and working with different Stata dataset formats

11. Excel - Importing and exporting Excel data in Stata

12. Log-file - Generating and understanding log files in Stata

13. Microsoft Word - Exporting Stata output to Microsoft Word

14. Results-window - Understanding and interpreting the Results window in Stata

15. Iterating - Using loops and other iterative constructs in Stata

16. Version-control - Managing Stata code and versions effectively

17. Collaboration - Understanding and leveraging collaboration in Stata

    • Sub-topic: Collaborative Coding in Stata

18. HyperText & Markups - Using HTML and other markup languages with Stata output

19. Publishing - The process of publishing Stata output

    • Sub-topic: Publishing in Stata

20. Teaching Assistants - How to make use of teaching assistants for your course

21. Labs & Homeworks - Overview of labs and homeworks in the course

22. Grades & Feedback - Understanding the grading rubric and feedback process

23. OpenAI & ChatGPT - Using artificial intelligence tools to expedite learning

24. Troubleshooting & FAQs - Common issues and solutions in Stata programming

25. Hands-on Practice - Practice problems and exercises to apply what you’ve learned

26. Additional Resources - Further reading and additional resources for learning Stata

27. Career Guidance - Potential career paths and tips for job interviews in Stata-related fields

28. Work Opportunities - Opportunities for work and internships in Stata-related fields

29. Course Evaluation - Understanding and giving feedback through course evaluation

30. Recommendations - Recommendations for further learning and improvement

31. Mentorship & More - Opportunities for mentorship and additional learning resources

See also

Last update 4:48 AM July 18, 2023

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