このコースについて

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次における6の2コース
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上級レベル
約6時間で修了
英語
字幕:英語

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Data ScienceInformation EngineeringArtificial Intelligence (AI)Machine LearningPython Programming
共有できる証明書
修了時に証明書を取得
100%オンライン
自分のスケジュールですぐに学習を始めてください。
次における6の2コース
柔軟性のある期限
スケジュールに従って期限をリセットします。
上級レベル
約6時間で修了
英語
字幕:英語

提供:

IBM ロゴ

IBM

シラバス - 本コースの学習内容

1

1

3時間で修了

Data Analysis

3時間で修了
6件のビデオ (合計26分), 12 readings, 4 quizzes
6件のビデオ
Introduction to Data Visualizations3 分
Data Visualizations7 分
Introduction to Missing Values4 分
Missing Values4 分
Case Study Introduction2 分
12件の学習用教材
Why is exploratory data analysis necessary?3 分
Data Visualization: Through the eyes of our Working Example3 分
Getting Started / Unit Materials2 分
Data visualization in Python3 分
Missing Data: Introduction2 分
Strategies for missing data3 分
Categories of missingness2 分
Simple imputation2 分
Bayesian imputation10 分
Case Study: Getting started2 分
Build a deliverable1 時間 30 分
Summary/Review5 分
4の練習問題
Check for Understanding: EDA2 分
Check for Understanding: Data Visualization4 分
Check for Understanding: Missing Data4 分
Data Analysis Module Quiz5 分
2

2

3時間で修了

Data Investigation

3時間で修了
3件のビデオ (合計16分), 14 readings, 3 quizzes
3件のビデオ
Hypothesis testing10 分
Case Study Introduction2 分
14件の学習用教材
TUTORIAL: IBM Watson Studio dashboard10 分
Hypothesis Testing: Through the eyes of our Working Example10 分
Overview2 分
Statistical Inference2 分
Business scenarios and probability3 分
Variants on t-tests2 分
One-way Analysis of Variance (ANOVA)4 分
p-value limitations10 分
Multiple Testing4 分
Explain methods for dealing with multiple testing3 分
Getting Started3 分
Import the Data4 分
Data Processing (Includes Assessment)2 時間
Summary/Review4 分
3の練習問題
Check for Understanding: Hypothesis Testing4 分
Check for Understanding: Hypothesis Testing Limitations2 分
Data Investigation Module Quiz5 分

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AI WORKFLOW: DATA ANALYSIS AND HYPOTHESIS TESTING からの人気レビュー

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IBM AI Enterprise Workflow専門講座について

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

よくある質問

  • 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.

  • When you enroll in the course, you get access to all of the courses in the Specialization, and you earn a certificate when you complete the work. Your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile. If you only want to read and view the course content, you can audit the course for free.

  • 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.

  • Yes, Coursera provides financial aid to learners who cannot afford the fee. Apply for it by clicking on the Financial Aid link beneath the "Enroll" button on the left. You'll be prompted to complete an application and will be notified if you are approved. You'll need to complete this step for each course in the Specialization, including the Capstone Project. Learn more.

  • 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.

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