データ分析のためのPython：入門から蜂蜜のヒントまで
datarian
インフラの累積受講生10,000人以上、豊富なオン/オフライン講義経験を持つデータリアンのPython基礎講義。非専攻者の方々も簡単に学習できるように、プログラムのインストールなどくちばしを大胆に省略し、Pythonの核心だけを教えます。データ分析を目的にPython学習をしている方に最適な講義です。
입문
Python, Data Engineering
BEST
Introduction to Python for Programming and Data Science
The best course to learn programming! This is a good course to learn programming and improve your skills, from solid concept explanations to quizzes and homework to review, using the easy and versatile 'python'. This is a good course for anyone who wants to become a data scientist, a programmer, or use programming for repetitive tasks.
Reviews from Early Learners
What you will learn!
Introduction to Programming
Introductory knowledge for computer engineering and data science
Python grammar, programming
Solving Python grammar problems through various examples
0. Publication of textbooks
This course has a published textbook based on its contents.
1. Course Introduction
This lecture is the first lecture of the data science course developed by TEAMLAB and Inflearn, "Introduction to Python for Data Science." This lecture was created based on the content of the K-MOOC: Introduction to Python for Data Science (YouTube) course, which was produced with the support of the Ministry of Education. This lecture was created with the support of 249 people through crowdfunding prepared by TEAMLAB and Inflearn. We plan to develop additional lectures on the list below in the future.
- Introduction to Python for Data Science - Main Course
- Machnine Learning from Scratch with Python Part I
- Machnine Learning from Scratch with Python Part II
Please also refer to the list below for existing K-MOOC courses.
Python is currently the most widely used language for data analysis, development, artificial intelligence, and office automation. Through this course, you can build a foundation for understanding how to use Python, programming concepts, and specialized lectures that will be added in the future.
| Learning Objectives | Helpful people |
|---|---|
| Acquire basic knowledge of basic programming language grammar and data handling. | Anyone who wants to get started with programming, a beginner who wants to learn data science, anyone who wants to build a foundation before starting machine learning, anyone who is preparing for a job in the data science field |
2. Course Features
A rich curriculum consisting of lectures, quizzes, and practical exercises with certified instructors
This Python programming course is structured as a lecture-quiz-practical assignment for each chapter.
If you count 1 chapter as 1 week, it's 15 weeks of study.
Professor Seong-cheol Choi, who has experience in both corporate and academic settings and has received much support from previous K-MOOCs, and Inflearn have prepared this with great care.
3. Why Python?
#1 most popular programming language
The grammar is concise and easy to learn. Since it is open source, there are many useful libraries.
Can be used in a variety of ways in one language!
Python is a popular language used in various fields such as programming, data analysis, and the Internet of Things. Learn basic Python and improve your skills through various advanced courses such as programming or data analysis!
4. References
Jump to Python , by Park Eung-yong,
2014 Hello Python Programming, by Warren Sande and Carter Sande / Translated by Seungbeom Kim and Junpyo Park,
2014 Ha Yong-ho, How should we view startup data ?
2014 Choi Seong-cheol, Introduction to Programming for Industrial Engineering Part 1 (w/Python) ,
2014 Seongcheol Choi, Introduction to Programming for Industrial Engineering Part 2 (w/Python) ,
2014 Choi Seong-cheol, Introduction to Programming for Industrial Engineering Code (w/Python) ,
2014 Code Assignment Analysis Technical Support: Lablup (www.lablup.com)
5. Instructor Introduction
Choi Seong-cheol (Director of TEAMLAB )
- Email: teamlab.gachon@gmail.com
Gam Dong-geun, Kang Nam-gu, Kang Dong-hoon, Kang Min-goo, Kang Seung-hyung, Kang Shin-hyun, Kang Jeong-mo, Kang Cheon-seong, Kyeon Eun-gyeong, Ko Sang-gyu, Ko Tae-young, Ko Hyeong-ju, Kwak Byeong-woo, Kwak Jun-gyu, Kwak Hyo-eun, Kwon Ki-woong, Kwon Su-rim, Kwon Jun-ho, Kim Kang-han, Kim Ki-beom, Kim Ki-hyun, Kim Dae-hyun, Kim Dong-soo, Kim Beom-young, Kim Sang-ho, Kim Seok, Kim Seol-hwa, Kim Seong-seon, Kim Yeong-gon, Kim Yeong-bok, Kim Wan, Kim Woo-jae, Kim Won-jun, Kim Yu-jun, Kim Jae-hoon, Kim Jong-cheol, Kim Joo-ho, Kim Jun-yeop, Kim Jun-cheol, Kim Jun-tae, Kim Ji-hoon, Kim Jin-yeong, Kim Tae-il, Kim Tae-hyung, Kim Hyun-soo, Kim Hyun-il, Kim Hyun-pyo, Kim Hyung-soo, Kim Hee-jung, Nam Goong-yeong, No Dong-heun, No Jeong-cheol, No Jin-seon, No Tae-ju, Ryu Jae-guk, Ryu Ji-hwan, Mok Jeong-hwan, Moon Jong-bae, Moon Jin-sol, Moon Jin-won, Park Kyung-hwa, Park Dong-hee, Park Du-gang, Park Min-joon, Park Seon-ho, Park Se-won, Park Soo-yeon, Park Shin-young, Park Jae-ho, Park Je-min, Park Jun-hwan, Park Jin-tae, Park Chan-jin, Park Cheol-hong, Park Tae-gyun, Park Tae-wook, Park Hye-won, Park Hong-seong, Park Hoon-beom, Park Heung-joo, Bae Yoon-seong, Bae I-hwan, Bae Jin-ui, Baek Gil-ho, Baek Sang-il, Byeong-seop Byun, Ki-yong Seo, Dong-jin Seo, Dong-hwa Seo, Yoon-hee Seo, Jae-won Seo, Min-ho Seong, Ki-chang Son, Baek-mo Son, Yu-yeon Son, Jeong-hoon Son, Min-gyu Song, Eun-jeong Song, Ji-hoon Song, Dong-soo Shin, Myeong-seok Shin, Ik-soon Shin, Jae-geun Shin, Jeong-hyeon Shin, Jin-gyu Shin, Heon-seop Shin, Byeong-hun Ahn, Jung-hee Ahn, Je-yeol Yang, Seong-woo Oh, Seung-jae Oh, Jae-woo Ok, Ji-won Woo, Seon Won, Ha-ri Won, Jae-hyeok Wi, Yeong-ho Yoo, Byeong-gil Yoon, Seok-chae Yoon, Seok-pil Yoon, Yoon Sung-hyun, Yoon Jun-seo, Yoon Jin-hwan, Lee Kyung-rok, Lee Kyung-mi, Lee Kyung-eun, Lee Ki-yong, Lee Dae-gyu, Lee Deok-gi, Lee Don-joong, Lee Min-sun, Lee Sang-yeop, Lee Seong-ju, Lee Seong-han, Lee Seong-hoon, Lee Su-hwan, Lee Seung-gyu, Lee Seung-jun, Lee Shin-ae, Lee Yeon-jun, Lee Yeong-sook, Lee Yeong-il, Lee Yong-min, Lee Yu-jeong, Lee Eun-seop, Lee Ja-ho, Lee Jae-jun, Lee Jae-hyun, Lee Jeong-yeon, Lee Jeong-ho, Lee Jong-seok, Lee Ju-woong, Lee Ju-won, Lee Ji-seon, Lee Ji-o, Lee Chang-seop, Lee Hyeong-beom, Im Se-min, Im Won-gyun, Im Jong-tae, Im Ji-hong, Im Chae-hyeon, Jang Seok-won, Jang Woo-il, Jang Woo-cheol, Jang Jun-hyeok, Jang Hyeon-jeong, Jang Hong-gi, Jeon Gyeong-hwan, Jeon Yong-jin, Jeon Jong-yeol, Jeon Jin-myeong, Jeong Gwang-yoon, Jeong Gwang-ho, Jeong Dae-hwan, Jeong Dong-ryeol, Jeong Dong-min, Jeong Seong-uk, Jeong Su-jeong, Jeong Seung-hyeon, Jeong Yeong-gyo, Jeong Yun-gi, Jeong Chan-mo, Jeong Hyang-won, Jeong Hyeon-cheol, Jo Gwang-je, Jo Min-ha, Jo Su-jeong, Jo Yeong-man, Jo Yong-jun, Jo Won-seok, Jo Jae-moon, Jo Jung-hyun, Joo Jeong-seok, Jin So-ra, Cha Dong-cheol, Cha Jin-man, Chae Ho-jin, Choi Gyeong-min, Choi Woong-sik, Choi In-bo, Choi Jeong-won, Choi Je-ho, Choi Jun-sik, Choi Han-dong, Chu Jeong-ho, Ha Jun-su, Han Bo-ram, Han Seong-uk, Han Seong-hyeon, Han Hyeong-seop, Hyun Seung-cheol, Hong Mi-na, Hong Sim-hee, Hong Jun-won, Hong Tae-hwan, Hwang Dae-seong, Hwang Eui-young, Hwang Ji-young, Hyo-ju, eric, Sunghuek Park, Lablup, Shin Jeong-gyu, TeamLab, Choi Soo-kyung, Lee Se-ri
Recommended for
these people
Who is this course right for?
If you want to learn data science
Those who want to learn programming
Those who are new to coding or don't know how to solve problems
Those who want to improve their skills through assignments
Need to know before starting?
Anyone!!
Curriculum
All
118 lectures ∙ (16hr 9min)
Course Materials:
Lecture resources
Published:
Last updated:
Reviews
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93 reviews
4.5
93 reviews
hyukster90666Reviews 20
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Average Rating 4.8
- psyche2823
Reviews 7
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Average Rating 4.7
- keunjiyoo0049
Reviews 5
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Average Rating 4.0
- zestuk
Reviews 5
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Average Rating 5.0
- gmkwon7493
Reviews 1
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Average Rating 5.0
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