이 강좌에 대하여

최근 조회 8,803
공유 가능한 수료증
완료 시 수료증 획득
100% 온라인
지금 바로 시작해 나만의 일정에 따라 학습을 진행하세요.
다음 특화 과정의 6개 강좌 중 2번째 강좌:
유동적 마감일
일정에 따라 마감일을 재설정합니다.
고급 단계
완료하는 데 약 7시간 필요
영어
자막: 영어

귀하가 습득할 기술

Data ScienceInformation EngineeringArtificial Intelligence (AI)Machine LearningPython Programming
공유 가능한 수료증
완료 시 수료증 획득
100% 온라인
지금 바로 시작해 나만의 일정에 따라 학습을 진행하세요.
다음 특화 과정의 6개 강좌 중 2번째 강좌:
유동적 마감일
일정에 따라 마감일을 재설정합니다.
고급 단계
완료하는 데 약 7시간 필요
영어
자막: 영어

제공자:

IBM 로고

IBM

강의 계획 - 이 강좌에서 배울 내용

1

1

완료하는 데 4시간 필요

Data Analysis

완료하는 데 4시간 필요
6개 동영상 (총 26분), 12 개의 읽기 자료, 4 개의 테스트
6개의 동영상
Introduction to Data Visualizations3m
Data Visualizations7m
Introduction to Missing Values4m
Missing Values4m
Case Study Introduction2m
12개의 읽기 자료
Why is Exploratory Data Analysis Necessary?3m
Data Visualization: Through the Eyes of Our Working Example3m
Getting Started / Unit Materials2m
Data Visualization in Python3m
Missing Data: Introduction2m
Strategies for Missing Data3m
Categories of Missing Data2m
Simple Imputation2m
Bayesian Imputation10m
Case Study: Getting started2m
Build a Deliverable1시간 30분
Summary/Review5m
4개 연습문제
Check for Understanding: EDA2m
Check for Understanding: Data Visualization4m
Check for Understanding: Missing Data4m
Data Analysis Module Quiz5m
2

2

완료하는 데 3시간 필요

Data Investigation

완료하는 데 3시간 필요
3개 동영상 (총 16분), 14 개의 읽기 자료, 3 개의 테스트
3개의 동영상
Hypothesis Testing10m
Case Study Introduction2m
14개의 읽기 자료
TUTORIAL: IBM Watson Studio dashboard10m
Hypothesis Testing: Through the eyes of our Working Example10m
Overview2m
Statistical Inference2m
Business Scenarios and Probability3m
Variants on t-tests2m
One-way Analysis of Variance (ANOVA)4m
p-value Limitations10m
Multiple Testing4m
Explain Methods for Dealing with Multiple Testing3m
Getting Started3m
Import the Data4m
Data Processing (Includes Assessment)2시간
Summary/Review4m
3개 연습문제
Check for Understanding: Hypothesis Testing4m
Check for Understanding: Hypothesis Testing Limitations2m
Data Investigation Module Quiz5m

검토

AI WORKFLOW: DATA ANALYSIS AND HYPOTHESIS TESTING의 최상위 리뷰

모든 리뷰 보기

IBM AI Enterprise Workflow 특화 과정 정보

This six course specialization is designed to prepare you to take the certification examination for IBM AI Enterprise Workflow V1 Data Science Specialist. IBM AI Enterprise Workflow is a comprehensive, end-to-end process that enables data scientists to build AI solutions, starting with business priorities and working through to taking AI into production. The learning aims to elevate the skills of practicing data scientists by explicitly connecting business priorities to technical implementations, connecting machine learning to specialized AI use cases such as visual recognition and NLP, and connecting Python to IBM Cloud technologies. The videos, readings, and case studies in these courses are designed to guide you through your work as a data scientist at a hypothetical streaming media company. Throughout this specialization, the focus will be on the practice of data science in large, modern enterprises. You will be guided through the use of enterprise-class tools on the IBM Cloud, tools that you will use to create, deploy and test machine learning models. Your favorite open source tools, such a Jupyter notebooks and Python libraries will be used extensively for data preparation and building models. Models will be deployed on the IBM Cloud using IBM Watson tooling that works seamlessly with open source tools. After successfully completing this specialization, you will be ready to take the official IBM certification examination for the IBM AI Enterprise Workflow....
IBM AI Enterprise Workflow

자주 묻는 질문

  • Access to lectures and assignments depends on your type of enrollment. If you take a course in audit mode, you will be able to see most course materials for free. To access graded assignments and to earn a Certificate, you will need to purchase the Certificate experience, during or after your audit. If you don't see the audit option:

    • The course may not offer an audit option. You can try a Free Trial instead, or apply for Financial Aid.
    • The course may offer 'Full Course, No Certificate' instead. This option lets you see all course materials, submit required assessments, and get a final grade. This also means that you will not be able to purchase a Certificate experience.
  • 강좌를 등록하면 전문 분야의 모든 강좌에 접근할 수 있고 강좌를 완료하면 수료증을 취득할 수 있습니다. 전자 수료증이 성취도 페이지에 추가되며 해당 페이지에서 수료증을 인쇄하거나 LinkedIn 프로필에 수료증을 추가할 수 있습니다. 강좌 내용만 읽고 살펴보려면 해당 강좌를 무료로 청강할 수 있습니다.

  • 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.

  • 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.

  • This course assumes that you are already familiar with basic data science concepts including probability and statistics, linear algebra, machine learning, and the use of Python and Jupyter. Additionally, you should have already completed the first course in this specialization: AI Workflow: Business Priorities and Data Ingestion.

  • No. The certification exam is administered by Pearson VUE and must be taken at one of their testing facilities. You may visit their site at https://home.pearsonvue.com/ for more information.

  • Please visit the Pearson VUE web site at https://home.pearsonvue.com/ for the latest information on taking the AI Enterprise Workflow certification test.

  • It is highly recommended that you have at least a basic working knowledge of design thinking and Watson Studio prior to taking this course. Please visit the IBM Skills Gateway at http://ibm.com/training/badges and "Find a Badge" related to "design thinking" or "Watson Studio". From there you will be directed to courses covering these topics.

  • No. Most of the exercises may be completed with open source tools running on your personal computer. However, the exercises are designed with an enterprise focus and are intended to be run in an enterprise environment that allows for easier sharing and collaboration. The exercises in the last two modules of the course are heavily focused on deployment and testing of machine learning models and use the IBM Watson tooling found on the IBM Cloud.

  • Yes. All IBM Cloud Data and AI services are based upon open source technologies.

  • The exercises in the course may be completed by anyone using the IBM Cloud "Lite" plan, which is free for use.

궁금한 점이 더 있으신가요? 학습자 도움말 센터를 방문해 보세요.