このコースについて
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次における6の2コース

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。

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5時間で修了

推奨:This course requires 7.5 to 9 hours of study....

英語

字幕:英語

習得するスキル

Data ScienceInformation EngineeringArtificial Intelligence (AI)Machine LearningPython Programming

次における6の2コース

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。

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5時間で修了

推奨:This course requires 7.5 to 9 hours of study....

英語

字幕:英語

シラバス - 本コースの学習内容

1
3時間で修了

Data Analysis

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
3時間で修了

Data Investigation

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

講師

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Mark J Grover

Digital Content Delivery Lead
IBM Data & AI Learning
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Ray Lopez, Ph.D.

Data Science Curriculum Leader
IBM Data & Artificial Intelligence

IBMについて

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....

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

  • 修了証に登録すると、すべてのビデオ、テスト、およびプログラミング課題(該当する場合)にアクセスできます。ピアレビュー課題は、セッションが開始してからのみ、提出およびレビューできます。購入せずにコースを検討することを選択する場合、特定の課題にアクセスすることはできません。

  • コースに登録する際、専門講座のすべてのコースにアクセスできます。コースの完了時には修了証を取得できます。電子修了証が成果のページに追加され、そこから修了証を印刷したり、LinkedInのプロフィールに追加したりできます。コースの内容の閲覧のみを希望する場合は、無料でコースを聴講できます。

  • 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. 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.

さらに質問がある場合は、受講者向けヘルプセンターにアクセスしてください。