데이터 과학 부문의 경력을 시작하세요. Gain foundational data science skills to prepare for a career or further advanced learning in data science.

4.7

별점

10,009개의 평가

Rav Ahuja +6명의 강사

56,328명이 이미 등록했습니다.

제공자:

데이터 과학 소개 특화 과정IBM

배울 내용

Describe what data science and machine learning are, their applications & use cases, and various types of tasks performed by data scientists

Gain hands-on familiarity with common data science tools including JupyterLab, R Studio, GitHub and Watson Studio

Develop the mindset to work like a data scientist, and follow a methodology to tackle different types of data science problems

Write SQL statements and query Cloud databases using Python from Jupyter notebooks

귀하가 습득할 기술

Data Science
Relational Database Management System (RDBMS)
Cloud Databases
Python Programming
SQL
Deep Learning
Machine Learning
Big Data
Data Mining
Github
Jupyter notebooks
Rstudio

이 전문 분야 정보

최근 조회 41,420회

Interested in learning more about data science, but don’t know where to start? This 4-course Specialization from IBM will provide you with the key foundational skills any data scientist needs to prepare you for a career in data science or further advanced learning in the field. This Specialization will introduce you to what data science is and what data scientists do. You’ll discover the applicability of data science across fields, and learn how data analysis can help you make data driven decisions. You’ll find that you can kickstart your career path in the field without prior knowledge of computer science or programming languages: this Specialization will give you the foundation you need for more advanced learning to support your career goals. You’ll grasp concepts like big data, statistical analysis, and relational databases, and gain familiarity with various open source tools and data science programs used by data scientists, like Jupyter Notebooks, RStudio, GitHub, and SQL. You'll complete hands-on labs and projects to learn the methodology involved in tackling data science problems and apply your newly acquired skills and knowledge to real world data sets. In addition to earning a Specialization completion certificate from Coursera, you’ll also receive a digital badge from IBM recognizing you as a specialist in data science foundations. This Specialization can also be applied toward the IBM Data Science Professional Certificate.

응용 학습 프로젝트

You will utilize tools like Jupyter, GitHub, R Studio, and Watson Studio to complete hands-on labs and projects throughout the Specialization. Using new skills and knowledge gained through the program, you’ll also work with real world data sets and query them using SQL from Jupyter notebooks.

학습자 경력 결과

12%

이 특화 과정을(를) 수료한 후 새로운 경력을 시작함

공유 가능한 수료증

완료 시 수료증 획득

100% 온라인 강좌

지금 바로 시작해 나만의 일정에 따라 학습을 진행하세요.

유연한 일정

유연한 마감을 설정하고 유지 관리합니다.

초급 단계

사전 경험이 필요하지 않습니다.

완료하는 데 약 4개월이 걸립니다

권장 학습 속도: 주 3시간

영어

자막: 영어, 아랍어, 프랑스어, 포르투갈어 (유럽), 이탈리아어, 베트남어, 독일어, 러시아어, 터키어, 스페인어, 페르시아어

학습자 경력 결과

12%

이 특화 과정을(를) 수료한 후 새로운 경력을 시작함

공유 가능한 수료증

완료 시 수료증 획득

100% 온라인 강좌

지금 바로 시작해 나만의 일정에 따라 학습을 진행하세요.

유연한 일정

유연한 마감을 설정하고 유지 관리합니다.

초급 단계

사전 경험이 필요하지 않습니다.

완료하는 데 약 4개월이 걸립니다

권장 학습 속도: 주 3시간

영어

자막: 영어, 아랍어, 프랑스어, 포르투갈어 (유럽), 이탈리아어, 베트남어, 독일어, 러시아어, 터키어, 스페인어, 페르시아어

특화 과정 이용 방법

강좌 수강

Coursera 특화 과정은 한 가지 기술을 완벽하게 습득하는 데 도움이 되는 일련의 강좌입니다. 시작하려면 특화 과정에 직접 등록하거나 강좌를 둘러보고 원하는 강좌를 선택하세요. 특화 과정에 속하는 강좌에 등록하면 해당 특화 과정 전체에 자동으로 등록됩니다. 단 하나의 강좌만 수료할 수도 있으며, 학습을 일시 중지하거나 언제든 구독을 종료할 수 있습니다. 학습자 대시보드를 방문하여 강좌 등록 상태와 진도를 추적해 보세요.

실습 프로젝트

모든 특화 과정에는 실습 프로젝트가 포함되어 있습니다. 특화 과정을 완료하고 수료증을 받으려면 프로젝트를 성공적으로 마쳐야 합니다. 특화 과정에 별도의 실습 프로젝트 강좌가 포함되어 있는 경우, 다른 모든 강좌를 완료해야 프로젝트 강좌를 시작할 수 있습니다.

수료증 취득

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

이 전문 분야에는 4개의 강좌가 있습니다.

강좌1

강좌 1

데이터 과학이란 무엇인가요?

4.7

별점

51,303개의 평가

•

9,670개의 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.

강좌2

강좌 2

Tools for Data Science

4.5

별점

23,087개의 평가

•

3,633개의 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, JupyterLab, RStudio IDE, Git, GitHub, and Watson Studio. 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 Skills Network 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 Watson Studio and demonstrate your proficiency preparing a notebook, writing Markdown, and sharing your work with your peers.

강좌3

강좌 3

Data Science Methodology

4.6

별점

17,141개의 평가

•

2,092개의 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!

강좌4

강좌 4

Databases and SQL for Data Science with Python

4.6

별점

14,410개의 평가

•

1,785개의 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

제공자:

IBM

IBM is the global leader in business transformation through an open hybrid cloud platform and AI, serving clients in more than 170 countries around the world. Today 47 of the Fortune 50 Companies rely on the IBM Cloud to run their business, and IBM Watson enterprise AI is hard at work in more than 30,000 engagements. IBM is also one of the world’s most vital corporate research organizations, with 28 consecutive years of patent leadership. Above all, guided by principles for trust and transparency and support for a more inclusive society, IBM is committed to being a responsible technology innovator and a force for good in the world.

자주 묻는 질문

환불 규정은 어떻게 되나요?
구독하는 경우, 취소해도 요금이 청구되지 않는 7일간의 무료 평가판을 이용할 수 있습니다. 해당 기간이 지난 후에는 환불이 되지 않지만, 언제든 구독을 취소할 수 있습니다. 전체 환불 정책 보기.
하나의 강좌에만 등록할 수 있나요?
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.
재정 지원을 받을 수 있나요?
예, Coursera에서는 수업료를 낼 수 없는 학습자를 위해 재정 지원을 제공합니다. 왼쪽에 있는 등록 버튼 아래 재정 지원 링크를 클릭하면 지원할 수 있습니다. 신청서를 작성하라는 메시지가 표시되며 승인되면 알림을 받습니다. 성취 프로젝트를 포함하여 전문 분야의 각 강좌에서 이 단계를 완료해야 합니다. 자세히 알아보기.
해당 강좌를 무료로 수강할 수 있나요?
강좌를 등록하면 전문 분야의 모든 강좌에 접근할 수 있으며 강좌를 완료하면 인증서가 발급됩니다. 강좌 내용을 읽고 보기만 원한다면 강좌를 무료로 청강할 수 있습니다. 수업료를 지급하기 어려운 경우, 재정 지원을 신청할 수 있습니다.
이 강좌는 100% 온라인으로 진행되나요? 직접 참석해야 하는 수업이 있나요?
이 강좌는 100% 온라인으로 진행되므로 강의실에 직접 참석할 필요가 없습니다. 웹 또는 모바일 장치를 통해 언제 어디서든 강의, 읽기 자료, 과제에 접근할 수 있습니다.
How can I earn my IBM Badge?
Upon completion of the program, you will receive an email from Acclaim with your IBM Badge recognizing your expertise in the field. Some badges are issued almost immediately after completion of the badge activities, while others may take 1-2 weeks before they are issued. Once issued, you will receive a notification email from admin@youracclaim.com with instructions for claiming the badge. Learn more about IBM Badges
What is data science?
Data science is the process of collecting, storing, and analyzing data. Data scientists use data to tell compelling stories to inform business decisions. Learn more about what data science is and what data scientists do in the IBM Course, "What is Data Science?"
What are some examples of careers in data science?
An understanding of data science and the ability to make data driven decisions is useful in any career, but some careers specifically require a data science background. Some examples of careers in data science include:
- Business Intelligence Analyst
- Data Analyst
- Data Architect
- Data Engineer
- Data Scientist
- Machine Learning Engineer
- Marketing Analyst
- Operations Analyst
- Quantitative Analyst
How long does it take to complete this Specialization?
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.
What background knowledge is necessary?
This Specialization is intended for learners wanting to build foundational skills in data science. No prior background in data science or programming is required.
Do I need to take the courses in a specific order?
In order to get the most out of this Specialization, it is recommended to take the courses in the order they are listed.
전문 분야를 완료하면 대학 학점을 받을 수 있나요?
No, there is no university credit associated with completing this Specialization.
What will I be able to do upon completing the Specialization?
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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