This document discusses Python and its capabilities. It introduces the speaker as having a background in computer engineering and various software development roles. It then discusses why Python has grown in popularity due to its versatility and widespread use. It compares Python to Java and shows how Python can be used for data science with libraries like NumPy, Pandas, and SciKit-learn. It also provides recommendations for how to learn Python through online courses and ways to practice Python coding through interactive websites.

1. Python의 가능성
삼성 SDS 개발자 Conference OpeniT 5회
대안언어 축제
두산중공업 Software센터
조 인 석

2. Contents
 Speaker is..
 Python?
 Why Python?
 Java vs Python
 Data Science w/ Python
 How can I learn Python?
 How can I play w/ Python?
 Q & A
2
Table of Contents

3. 3
조 인 석 (Chris Cho)
• 세종대 컴퓨터 공학 전공
• 육군전산소 SW개발병 (2년)
• 현대정보기술 해외금융개발팀 (7개월)
• 삼성 SDS (7년 3개월)
– ENG. Methodology 팀 & SI Process 팀 (2년 1개월)
• 통합 개발 플랫폼 대내외 프로젝트 지원
– Framework 팀 & Delivery Platform 팀 (5년 2개월)
• 삼성생명 차세대 Anyframe Enterprise BTO 전담 지원 (2년)
• 삼성전자 Global MES 2.0 Anyframe Enterprise 전담 지원 (3년)
• 두산 중공업 (현, 1년 9개월)
– Software센터 SW 개발팀
• Power & Water plant를 위한 Industrial IoT 관련 SW 개발 담당
• 실무환경에 맞춘 Node.js 프로그래밍 공저 (2014, 혜지원)
• 파이썬 입문 집필 중 (예정, 2016, 혜지원)
• Facebook : https://www.facebook.com/insuk.chris.cho
Speaker is..

4. 4
Python?

5. 5
Why Python? (1/4)
2015 Programming Language Rankings by RedMonk
Reference: http://redmonk.com/sogrady/2015/01/14/language-rankings-1-15/
Popularity Rank on GitHub
PopularityRankonStackOverflow

6. 6
Why Python? (2/4)
Most Popular Coding Languages of 2015
The result is based on hundreds of thousands of data points we've collected by
processing over 600,000+ coding tests and challenges by over 2,000+ employers
Reference: http://blog.codeeval.com/codeevalblog/2015#.VZDg-PntlBc

7. Why Python? (3/4)
You can find Python everywhere..
7

8. 8
Why Python? (4/4)
You can find Python everywhere..

9. 9
Java vs Python
Reference: http://www.javascriptstyle.com/java-vs-python
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10. Python IDE – Jupyter (iPython-notebook)
Using Web Browser
Explanation
(Comments)
Source Code
Execution Results
10

11. 11
 Data volume is growing exponentially within companies. Most don't know
how to harvest its value or how to even compute on it.
 Growing mess of tools, databases, and products. New products increase
integration headaches, instead of simplifying.
 New hardware & architectures are tempting, but are avoided or sit idle
because of software challenges.
 Promises of the "Big Red Solve" button continue to disappoint.
(If someone can give you this button, they are your competitor.)
Data Science w/ Python (1/5)
※ Slide : http://www.slideshare.net/misterwang/pydata-texas-2015-keynote
※ 동영상 : https://www.youtube.com/watch?v=Pkwygl3cgmU

12. Data Science w/ Python (2/5)
12
※ Slide : http://www.slideshare.net/misterwang/pydata-texas-2015-keynote
※ 동영상 : https://www.youtube.com/watch?v=Pkwygl3cgmU

13. Data Science w/ Python (3/5)
13
※ Slide : http://www.slideshare.net/misterwang/pydata-texas-2015-keynote
※ 동영상 : https://www.youtube.com/watch?v=Pkwygl3cgmU

14. Data Science w/ Python (4/5)
14
※ Slide : http://www.slideshare.net/misterwang/pydata-texas-2015-keynote
※ 동영상 : https://www.youtube.com/watch?v=Pkwygl3cgmU

15. Data Science w/ Python (5/5)
15
 OpenOPC for Python (http://openopc.sourceforge.net/)
- OpenOPC for Python is a free, open source OPC (OLE for Process Control) toolkit
designed for use with the popular Python programming language
 MQTT Brocker - Mosquitto (http://mosquitto.org/)
- Mosquitto makes it suitable for "machine to machine" messaging such as with low power
sensors or mobile devices such as phones, embedded computers or microcontrollers
 MQTT Client – paho-mqtt (https://pypi.python.org/pypi/paho-mqtt)
- This code provides a client class which enable applications to connect to an MQTT
broker to publish messages, and to subscribe to topics and receive published
messages
 NumPy (http://www.numpy.org/, http://en.wikipedia.org/wiki/NumPy)
- an optimized python library for numerical analysis, specifically: large, multi-
dimensional arrays and matrices. Found in Introduction to Data Science
 Pandas (http://pandas.pydata.org/, http://en.wikipedia.org/wiki/Pandas_(software) )
- an optimized python library for data analysis including dataframes inspired by R.
Found in Introduction to Data Science
 SciKit-learn (http://scikit-learn.org/stable/, http://en.wikipedia.org/wiki/Scikit-learn)
- machine learning library built on NumPy, SciPy, and matplotlib. Mentioned in
Introduction to Machine Learning
 H2O (http://h2o.ai/)
- H2O is for data scientists and application developers who need fast, in-memory
scalable machine learning for smarter applications. H2O is an open source parallel
processing engine for machine learning
 Django (https://www.djangoproject.com/)
- Django is a high-level Python Web framework that encourages rapid development
and clean, pragmatic design
 Anaconda (http://continuum.io/downloads)
- a python package manager with the intent of simplifying and maintaining compatibility
between library versions. Also useful for getting started with ipython notebooks
Data Gathering
Data Distribution
& Sharing
Data Learning
& Modeling
Visualization

16. 16
How can I learn Python? –www.edx.org

17. How can I learn Python? –www.microsoftvirtaulacademy.com
17

18. 18
How can I play w/ Python? –www.codecademy.com

19. 19
How can I play w/ Python? –www.hackerrank.com

20. Q & A