Kickstart your career in data science & ML. 데이터 과학 기술을 배우고, Python 및 SQL을 학습하며 데이터 분석 및 시각화를 해보고 기계 학습 모델을 구축하세요. 학위나 이전 경험은 필요하지 않습니다.

4.6

별점

46,588개의 평가

Rav Ahuja +10명의 강사

25,375명이 이미 등록했습니다.

제공자:

IBM 데이터 과학 전문 자격증IBM

배울 내용

Learn what data science is, the various activities of a data scientist’s job, and methodology to think and work like a data scientist

Develop hands-on skills using the tools, languages, and libraries used by professional data scientists

Import and clean data sets, analyze and visualize data, and build and evaluate machine learning models and pipelines using Python

Apply various data science skills, techniques, and tools to complete a project and publish a report

귀하가 습득할 기술

Data SciencePython ProgrammingData AnalysisPandasNumpyIpythonCloud DatabasesRelational Database Management System (RDBMS)SQLPredictive ModellingData Visualization (DataViz)Model Selection

이 전문 자격증 정보

최근 조회 547,041회

Data science is one of the hottest professions of the decade, and the demand for data scientists who can analyze data and communicate results to inform data driven decisions has never been greater. This Professional Certificate from IBM will help anyone interested in pursuing a career in data science or machine learning develop career-relevant skills and experience. It’s a myth that to become a data scientist you need a Ph.D. Anyone with a passion for learning can take this Professional Certificate – no prior knowledge of computer science or programming languages required – and develop the skills, tools, and portfolio to have a competitive edge in the job market as an entry level data scientist. The program consists of 9 online courses that will provide you with the latest job-ready tools and skills, including open source tools and libraries, Python, databases, SQL, data visualization, data analysis, statistical analysis, predictive modeling, and machine learning algorithms. You’ll learn data science through hands-on practice in the IBM Cloud using real data science tools and real-world data sets. Upon successfully completing these courses, you will have built a portfolio of data science projects to provide you with the confidence to plunge into an exciting profession in data science. In addition to earning a Professional Certificate from Coursera, you'll also receive a digital badge from IBM recognizing your proficiency in data science.

응용 학습 프로젝트

This Professional Certificate has a strong emphasis on applied learning. Except for the first course, all other courses include a series of hands-on labs in the IBM Cloud that will give you practical skills with applicability to real jobs, including:

Tools: Jupyter / JupyterLab, GitHub, R Studio, and Watson Studio

Libraries: Pandas, NumPy, Matplotlib, Seaborn, Folium, ipython-sql, Scikit-learn, ScipPy, etc.

Projects: random album generator, predict housing prices, best classifier model, battle of neighborhoods

학습자 경력 결과

39%

이 특화 과정을(를) 수료한 후 새로운 경력을 시작함

17%

급여 인상 또는 승진하기

공유 가능한 수료증

완료 시 수료증 획득

100% 온라인 강좌

지금 바로 시작해 나만의 일정에 따라 학습을 진행하세요.

유동적 일정

유연한 마감을 설정하고 유지 관리합니다.

초급 단계

사전 경험이 필요하지 않습니다.

완료하는 데 약 11개월이 걸립니다

권장 학습 속도: 주 4시간

영어

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

학습자 경력 결과

39%

이 특화 과정을(를) 수료한 후 새로운 경력을 시작함

17%

급여 인상 또는 승진하기

공유 가능한 수료증

완료 시 수료증 획득

100% 온라인 강좌

지금 바로 시작해 나만의 일정에 따라 학습을 진행하세요.

유동적 일정

유연한 마감을 설정하고 유지 관리합니다.

초급 단계

사전 경험이 필요하지 않습니다.

완료하는 데 약 11개월이 걸립니다

권장 학습 속도: 주 4시간

영어

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

전문 자격증이란 무엇인가요?

기술을 쌓아서 실무에 대비

새로운 분야에서 커리어를 찾고 있거나 현재 커리어에 변화를 주고 싶다면, Coursera의 전문 자격증을 취득하여 준비된 인재로 거듭날 수 있습니다. 가장 편리한 시간과 장소에서 나에게 맞는 속도로 배워보세요. 지금 바로 등록하고 7일 무료 평가판을 통해 새로운 진로를 탐색해보세요. 언제든지 학습을 일시 중지하거나 구독을 종료할 수 있습니다.

실습 프로젝트

실습 프로젝트에서 기술을 적용해보고, 미래 고용주에게 실무적으로 준비된 인재임을 보여주는 포트폴리오를 만들어보세요. 자격증을 취득하려면 프로젝트를 성공적으로 완료해야 합니다.

경력 자격 증명 취득

프로그램의 모든 강좌를 완료하면 전문가 네트워크에서 공유할 수 있는 자격증을 얻게 되며, 새로운 커리어를 시작하는 데 도움이 되는 커리어 지원 리소스에 액세스할 수 있게 됩니다. 많은 전문 자격증은 해당 전문 자격증의 자격 증명을 인정해주는 채용 파트너가 있거나, 자격증 시험을 준비하는 데 도움이 됩니다. 해당하는 경우 개별 전문 자격증 페이지에서 자세한 내용을 알아볼 수 있습니다.

이 전문 자격증에는 10개의 강좌가 있습니다.

강좌1

강좌 1

데이터 과학이란 무엇인가요?

4.7

별점

45,580개의 평가

•

8,532개의 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

21,305개의 평가

•

3,288개의 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

16,069개의 평가

•

1,922개의 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

강좌 4

Python for Data Science, AI & Development

4.6

별점

21,416개의 평가

•

3,444개의 리뷰

Kickstart your learning of Python for data science, as well as programming in general, with this beginner-friendly introduction to Python. Python is one of the world’s most popular programming languages, and there has never been greater demand for professionals with the ability to apply Python fundamentals to drive business solutions across industries.

This course will take you from zero to programming in Python in a matter of hours—no prior programming experience necessary! You will learn Python fundamentals, including data structures and data analysis, complete hands-on exercises throughout the course modules, and create a final project to demonstrate your new skills. By the end of this course, you’ll feel comfortable creating basic programs, working with data, and solving real-world problems in Python. You’ll gain a strong foundation for more advanced learning in the field, and develop skills to help advance your career. This course can be applied to multiple Specialization or Professional Certificate programs. Completing this course will count towards your learning in any of the following programs: IBM Applied AI Professional Certificate Applied Data Science Specialization IBM Data Science Professional Certificate Upon completion of any of the above programs, in addition to earning a Specialization completion certificate from Coursera, you’ll also receive a digital badge from IBM recognizing your expertise in the field.

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

전문 분야를 완료하면 대학 학점을 받을 수 있나요?
If you subscribed, you get a 7-day free trial during which you can cancel at no penalty. After that, we don’t give refunds, but you can cancel your subscription at any time. See our full refund policy.
Can I just enroll in a single course?
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 수료증을 얻거나 강좌를 청강하여 강좌 자료를 무료로 볼 수 있습니다. 수료 과정에 있는 강좌를 구독하면, 자동으로 전체 수료 과정을 구독하게 됩니다. 학습자 대시보드에서 진행 사항을 추적할 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.
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 first course of this Professional Certificate, "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 the Professional Certificate?
The Professional Certificate requires completion of 9 courses. Each course typically contains 3-6 modules with an average effort of 2 to 4 hours per module. If learning part-time (e.g. 1 module per week), it would take 6 to 12 months to complete the entire certificate. If learning full-time (e.g. 1 module per day) the certificate can be completed in 2 to 3 months.
What background knowledge do I need for this program?
This Professional Certificate is open for anyone with any job and academic background. No prior computer programming experience is necessary, but is an asset, as are familiarity working with computers, high school math, and communication and presentation skills. For the last few courses, knowledge of calculus and linear algebra is an asset but not an absolute requirement.
Do I need to take the courses in a specific order?
Yes, it is highly recommended to take the courses in the order they are listed, as they progressively build on concepts taught in previous courses. For example the Data Analysis, Data Visualization, and Machine Learning courses require knowledge of Python covered earlier in the program.
Will I earn university credit for completing the Professional Certificate?
No, there is no university credit involved with taking these courses.
What will I be able to do upon completing the Professional Certificate?
Become job ready for a career in Data Science. Develop practical skills using hands-on labs in Cloud environments, projects and captsones.
I already completed some of the other courses in this Professional Certificate. Will I get "credit" for them?
If you have already completed some of the courses in this Professional Certificate, either individually or as part of another Specialization, they will be marked as "Complete". You do not have to take those courses again, and will be able to finish the Professional Certificate more quickly. You will only need to complete the courses that you have not yet completed.
I have already completed the Introduction to Data Science Specialization. Can I still enroll in this Professional Certificate?
Yes, absolutely. Any courses that you have already completed as part of that Specialization will be marked as "Complete". You do not have to take those courses again and will be able to finish the Professional Certificate more quickly.
Which program should I enroll in - the Introduction to Data Science Specialization, or this Professional Certificate?
This Professional Certificate consists of 9 courses. The "Introduction to Data Science" Specialization has 4 courses, all of which are also included in this Professional Certificate.
If you are unsure about your ability to commit to the level of effort and time required to complete this Professional Certificate, we recommend starting with the Introduction to Data Science Specialization, which has fewer courses. If, after completing the Specialization, you are still determined to continue building your data science skills, you can then enroll for this Professional Certificate and then just complete the courses that are not in the Specialization.
I have already completed the Applied Data Science Specialization. Can I still enroll in this Professional Certificate?
Yes, absolutely. Any courses that you have already completed as part of that Specialization will be marked as "Complete". You do not have to take those courses again and will be able to finish this Professional Certificate more quickly.
How can I access job opportunities with IBM and other organizations after completing this Professional Certificate?
As a Coursera learner who completes this Professional Certificate, you will have special access to join IBM’s Talent Network. Our Talent Network members receive all of the tools you need to land a dream job with IBM - sent directly to your inbox! You will get job opportunities as soon as they are posted, recommendations to apply matched directly to your skills and interests, and tips and tricks to help you stand apart from the crowd.

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