Python 강좌

Python은 대규모 및 소규모 애플리케이션에 자주 사용되는 범용 프로그래밍 언어입니다. Python을 사용하면 웹 개발 및 데이터 분석을 연결하는 방법을 알아낼 수 있습니다. Python이 널리 채택된 이유는 대형 표준 라이브러리를 갖추고 있으며, 읽기 쉽고, 기능, 절차 및 객체 지향 프로그래밍 스타일과 같은 여러 패러다임을 지원하기 때문입니다. Python 모듈은 다양한 데이터베이스와 상호 작용하므로 대규모 데이터 분석에 사용하기에 매우 좋습니다. Python 프로그래밍 언어는 데이터 과학 및 머신 러닝의 입문 강좌에 적합한 경우가 많습니다. 경력을 발전시키기 위해 Python을 온라인으로 배우는 방법을 찾고 계셨다면 잘 찾아오신 것입니다.

파이썬은 Coursera 2020년 GSI(글로벌 기술 지수)에서 인기 있는 직업 기술 중 하나입니다. 2020년 GSI 보고서 다운로드.

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We recommend these courses for those who wish to learn Python without prior computer programming experience. You might be interested in learning how to automate accounting processes, or ways to bring efficiency to day-to-day marketing analytics and data mining. Python is used in large-scale functions and software engineering jobs, such as game development, machine learning, database management, and more. Regardless of your end goals, this collection covers the fundamentals of programming in Python.‎

A quick search of Indeed.com returns over 40,000 job openings with Python programming skills or experience listed as a requirement. The wide adoption of the language across many industries results in a large number of job opportunities. Common job titles include Python Developer, Python Software Engineer, Full Stack Developer and Python Database Programmer. Of the Python-specific jobs listed, 43% of offered salaries are above $100K per year, and some senior-level Python programming engineer positions offer over $200K per year. It’s very fair to say that Python skills and experience can lead to a lucrative and secure career. The proper Python certification can influence hiring managers looking to fill specific roles on their team.‎

Following is an excerpt from a Coursera Community forum topic about what programming languages our Community members use.

"I've been in software development for 50 years (now retired) and languages come and go. If you're into software development, keep active in several, and strive to learn a new and different culture language every year or two. In simple terms today I like Python and JavaScript. For longevity in marketable skills, C and C++ are essential base skills. I used C for over 30 years. Learn Haskell, it'll improve all your other programming skills even if you never use it in a project." - Gordon

"For data science it is most important to understand and implement algorithms. Python is one of the languages that is really self describing. And one of the major reasons why I use it for data science projects especially machine learning is that it is very light. Anyway, for all my projects that have complicated algorithms, I use OCTAVE for trying out algorithms (since I have all datascience codes stored there). Mainly by breaking it into simpler problems and then in the end I convert it into desired language and join the simpler and shorter algorithms." - Hardi‎

Both Python and R are free, open-source languages that can run on Windows, macOS, and Linux. Both can handle just about any data analysis task, and both are considered relatively easy languages to learn, especially for beginners. So which should you choose to learn (or learn first)? Before we dig into the differences, here’s a broad overview of each language. Read this article about the difference between Python and R.‎