OpeniT is Samsung SDS's Developer Conference. This is for one session talking about Python.

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
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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