About this 전문분야
최근 조회 19,354

100% 온라인 강좌

지금 바로 시작해 나만의 일정에 따라 학습을 진행하세요.

탄력적인 일정

유연한 마감을 설정하고 유지 관리합니다.

초급 단계

완료하는 데 약 1개월 필요

매주 12시간 권장

영어

자막: 영어, 아랍어, 한국어

귀하가 습득할 기술

Data SciencePython ProgrammingRstudioSQL

100% 온라인 강좌

지금 바로 시작해 나만의 일정에 따라 학습을 진행하세요.

탄력적인 일정

유연한 마감을 설정하고 유지 관리합니다.

초급 단계

완료하는 데 약 1개월 필요

매주 12시간 권장

영어

자막: 영어, 아랍어, 한국어

How the 전문분야 Works

강좌 수강

Coursera 전문 분야는 기술을 완벽하게 습득하는 데 도움이 되는 일련의 강좌입니다. 시작하려면 전문 분야에 직접 등록하거나 강좌를 둘러보고 원하는 강좌를 선택하세요. 하나의 전문 분야에 속하는 강좌에 등록하면 해당 전문 분야 전체에 자동으로 등록됩니다. 단 하나의 강좌만 수료해도 됩니다. — 학습을 일시 중지하거나 언제든 구독을 종료할 수 있습니다. 학습자 대시보드를 방문하여 강좌 등록 상태와 진도를 추적해 보세요.

실습 프로젝트

모든 전문 분야에는 실습 프로젝트가 포함되어 있습니다. 전문 분야를 완료하고 수료증을 받으려면 프로젝트를 성공적으로 마쳐야 합니다. 전문 분야에 별도의 실습 프로젝트 강좌가 포함되어 있는 경우 각 강좌를 완료해야 프로젝트를 시작할 수 있습니다.

수료증 취득

모든 강좌를 마치고 실습 프로젝트를 완료하면 취업할 때나 전문가 네트워크에 진입할 때 제시할 수 있는 수료증을 취득할 수 있습니다.

how it works

이 전문분야에는 4개의 강좌가 있습니다.

강좌1

What is Data Science?

4.6
9,689개의 평가
1,515개의 리뷰
The art of uncovering the insights and trends in data has been around since ancient times. The ancient Egyptians used census data to increase efficiency in tax collection and they accurately predicted the flooding of the Nile river every year. Since then, people working in data science have carved out a unique and distinct field for the work they do. This field is data science. In this course, we will meet some data science practitioners and we will get an overview of what data science is today. LIMITED TIME OFFER: Subscription is only $39 USD per month for access to graded materials and a certificate....
강좌2

Open Source tools for Data Science

4.6
5,550개의 평가
681개의 리뷰
What are some of the most popular data science tools, how do you use them, and what are their features? In this course, you'll learn about Jupyter Notebooks, RStudio IDE, Apache Zeppelin and Data Science Experience. You will learn about what each tool is used for, what programming languages they can execute, their features and limitations. With the tools hosted in the cloud on Cognitive Class Labs, you will be able to test each tool and follow instructions to run simple code in Python, R or Scala. To end the course, you will create a final project with a Jupyter Notebook on IBM Data Science Experience and demonstrate your proficiency preparing a notebook, writing Markdown, and sharing your work with your peers. LIMITED TIME OFFER: Subscription is only $39 USD per month for access to graded materials and a certificate....
강좌3

Data Science Methodology

4.6
4,175개의 평가
388개의 리뷰
Despite the recent increase in computing power and access to data over the last couple of decades, our ability to use the data within the decision making process is either lost or not maximized at all too often, we don't have a solid understanding of the questions being asked and how to apply the data correctly to the problem at hand. This course has one purpose, and that is to share a methodology that can be used within data science, to ensure that the data used in problem solving is relevant and properly manipulated to address the question at hand. Accordingly, in this course, you will learn: - The major steps involved in tackling a data science problem. - The major steps involved in practicing data science, from forming a concrete business or research problem, to collecting and analyzing data, to building a model, and understanding the feedback after model deployment. - How data scientists think! LIMITED TIME OFFER: Subscription is only $39 USD per month for access to graded materials and a certificate....
강좌4

Databases and SQL for Data Science

4.7
3,077개의 평가
310개의 리뷰
Much of the world's data resides in databases. SQL (or Structured Query Language) is a powerful language which is used for communicating with and extracting data from databases. A working knowledge of databases and SQL is a must if you want to become a data scientist. The purpose of this course is to introduce relational database concepts and help you learn and apply foundational knowledge of the SQL language. It is also intended to get you started with performing SQL access in a data science environment. The emphasis in this course is on hands-on and practical learning . As such, you will work with real databases, real data science tools, and real-world datasets. You will create a database instance in the cloud. Through a series of hands-on labs you will practice building and running SQL queries. You will also learn how to access databases from Jupyter notebooks using SQL and Python. No prior knowledge of databases, SQL, Python, or programming is required. Anyone can audit this course at no-charge. If you choose to take this course and earn the Coursera course certificate, you can also earn an IBM digital badge upon successful completion of the course. LIMITED TIME OFFER: Subscription is only $39 USD per month for access to graded materials and a certificate....

강사

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Alex Aklson

Ph.D., Data Scientist
IBM Developer Skills Network
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Polong Lin

Data Scientist
IBM Developer Skills Network
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Rav Ahuja

Data Science Program Manager
IBM

IBM 정보

IBM offers a wide range of technology and consulting services; a broad portfolio of middleware for collaboration, predictive analytics, software development and systems management; and the world's most advanced servers and supercomputers. Utilizing its business consulting, technology and R&D expertise, IBM helps clients become "smarter" as the planet becomes more digitally interconnected. IBM invests more than $6 billion a year in R&D, just completing its 21st year of patent leadership. IBM Research has received recognition beyond any commercial technology research organization and is home to 5 Nobel Laureates, 9 US National Medals of Technology, 5 US National Medals of Science, 6 Turing Awards, and 10 Inductees in US Inventors Hall of Fame....

자주 묻는 질문

  • 네! 시작하려면 관심 있는 강좌 카드를 클릭하여 등록합니다. 강좌를 등록하고 완료하면 공유할 수 있는 인증서를 얻거나 강좌를 청강하여 강좌 자료를 무료로 볼 수 있습니다. 전문 분야 과정에 있는 강좌에 등록하면, 전체 전문 분야에 등록하게 됩니다. 학습자 대시보드에서 진행 사항을 추적할 수 있습니다.

  • 이 강좌는 100% 온라인으로 진행되므로 강의실에 직접 참석할 필요가 없습니다. 웹 또는 모바일 장치를 통해 언제 어디서든 강의, 읽기 자료, 과제에 접근할 수 있습니다.

  • The specialization consists of 4 courses. Suggested time to complete each course is 3-4 weeks. If you follow recommended timelines it would take 3 to 4 months to complete the entire specialization.

  • This specialization is intended for learners wanting to build foundational skills in Data Science. No prior background in data science or programming is required.

  • In order to get the most out of this specialization, it is recommended to take the courses in the order they are listed.

  • No University credit will be earned.

  • In this Specialization learners will develop foundational Data Science skills to prepare them for a career or further learning that involves more advanced topics in Data Science.

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