Keynote 4: IES 통계 분석 능력의 강화 - JMP와 Python의 시너지

The document discusses JMP and Python for statistical analysis and data science. It provides a SWOT analysis comparing the strengths, weaknesses, opportunities, and threats of each. While JMP has strengths in its user-friendly GUI and built-in statistical tools, Python has advantages in its open source nature, scalability, and versatility. The document argues that both JMP and Python are needed - JMP for common statistical analyses and visualization, and Python for tasks like data preprocessing, machine learning, and automation. It proposes training to help users integrate the two platforms.

Engineering

Copyright © JMP Statistical Discovery LLC. All Rights Reserved.
KOREA

Competitors or Collaborators?
⚫ Many companies and colleges are choosing open sources
⚫ Is it a matter of cost or capability?
⚫ What choices are effective for companies?
⚫ What would be helpful for students?

SWOT Analysis: JMP vs. Python
STRENGTH WEAKNESS
OPPORTUNITY THREAT
• User-friendly GUI
• Statistical Analysis
• Visualization
• Integration with SAS
• Education & Training
• Industry-specific
Application
• Cost
• Scalability
• Handling
• Competition from
Open Source
• Technological
Advancements
STRENGTH WEAKNESS
OPPORTUNITY THREAT
• Variety of Libraries
• Open Source(Free)
• Scalability
• Versatility
• Growth in AI & ML
• Growing Community
• Learning Curve
• Speed
• Competition from
Other Languages
• Complexity

Need more high-level data analyst
In-house
Data
Analyst(%)
1980s 2000s 2030s 2040s 2050s
High-Level
Low-Level

In what capacity do I primarily serve
professionally?
Python Code…
Data Pre-
processing
Missing Values
Outlier
Engineering
skills
Machine Learning
AI etc.
I am “엔지니어”

Waste Time and Money
⚫ Training data analysts like Six Sigma deployments?
⚫ Sending engineers to the university to train a data analyst?
⚫ Everyone expects to be a Python expert, but not everyone
Most of engineers are low-end data analyst, so…

You can succeed by either
Overcoming the fear of the unknown
or
Stimulating the curiosity of the familiar

What to do something with Python
Everything! But Python is NOT a statistical solution
⚫ Web Development: 23%
⚫ Data Analysis: 16%
⚫ DevOps/System Administration/Writing Automation Scripts: 14%
⚫ Other fields, such as Machine Learning, Software Testing, and Educational Purposes,
have lower percentages, each below 10%
From Python Developers Survey 2022 results

Why still need JMP®
⚫ Many analyses using sample(small) data are still being done by
businesses(SPC, DOE etc.)
⚫ Accuracy and Time save
⚫ Insightful visual examination continues to be essential
⚫ Cost-effective convenience

Easier to use Python in JMP®
Python init();
Python Connect();
dt = open(“C:example.jmp”);
python send(dt);
Python Submit(“[
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
python command…
]”);
Python Submit(“[
mydt = jmp.DataTable(“jmp_dt”)
Python Submit(“[
jmp.run_jsl(‘JSL_script’)
python command…
]”);
No configuration, No declaration
# Open jmp data table
# Execute JMP scripts

Reference
https://youtu.be/q47TbEExJ2U?si=19oidfWdRB2I_bRa
https://youtu.be/QLQQXFzjufQ?si=PI8UZT_7JgrPUiWJ
https://youtu.be/QUz46etYpik?si=kAz9LhsIFsy8jCMY
https://youtu.be/jLAbQk9iEEE?si=kmWpRfJMgh7ITz-J

Training course to join JMP and Python
⚫ Most statistical analysis techniques are taught by JMP®
⚫ Use Python to analyze special techniques related to machine
learning
⚫ For integrating these two parts, focusing on the data types
exchanging data with each other
⚫ Automating analytics with JMP add-in

Harmony
Pilot Test and Analysis
Module
Module
Module
Pilot Test and Analysis
Pilot Test and Analysis
:
:
Pilot Test and Analysis
Analytic Product
(JMP Add-in or Python installer)

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Ai progress = leaderboards compute data algorithms 20180817 v3

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