課程信息
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第 2 門課程(共 6 門)

100% 在線

立即開始,按照自己的計劃學習。

可靈活調整截止日期

根據您的日程表重置截止日期。

高級

完成時間大約為6 小時

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

英語(English)

字幕:英語(English)

您將獲得的技能

Data ScienceInformation EngineeringArtificial Intelligence (AI)Machine LearningPython Programming

第 2 門課程(共 6 門)

100% 在線

立即開始,按照自己的計劃學習。

可靈活調整截止日期

根據您的日程表重置截止日期。

高級

完成時間大約為6 小時

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

英語(English)

字幕:英語(English)

教學大綱 - 您將從這門課程中學到什麼

1
完成時間為 3 小時

Data Analysis

6 個視頻 (總計 26 分鐘), 12 個閱讀材料, 4 個測驗
6 個視頻
Introduction to Data Visualizations3分鐘
Data Visualizations7分鐘
Introduction to Missing Values4分鐘
Missing Values4分鐘
Case Study Introduction2分鐘
12 個閱讀材料
Why is exploratory data analysis necessary?3分鐘
Data Visualization: Through the eyes of our Working Example3分鐘
Getting Started / Unit Materials2分鐘
Data visualization in Python3分鐘
Missing Data: Introduction2分鐘
Strategies for missing data3分鐘
Categories of missingness2分鐘
Simple imputation2分鐘
Bayesian imputation10分鐘
Case Study: Getting started2分鐘
Build a deliverable1 小時 30 分
Summary/Review5分鐘
4 個練習
Check for Understanding: EDA2分鐘
Check for Understanding: Data Visualization4分鐘
Check for Understanding: Missing Data4分鐘
Data Analysis Module Quiz5分鐘
2
完成時間為 3 小時

Data Investigation

3 個視頻 (總計 16 分鐘), 14 個閱讀材料, 3 個測驗
3 個視頻
Hypothesis testing10分鐘
Case Study Introduction2分鐘
14 個閱讀材料
TUTORIAL: IBM Watson Studio dashboard10分鐘
Hypothesis Testing: Through the eyes of our Working Example10分鐘
Overview2分鐘
Statistical Inference2分鐘
Business scenarios and probability3分鐘
Variants on t-tests2分鐘
One-way Analysis of Variance (ANOVA)4分鐘
p-value limitations10分鐘
Multiple Testing4分鐘
Explain methods for dealing with multiple testing3分鐘
Getting Started3分鐘
Import the Data4分鐘
Data Processing (Includes Assessment)2小時
Summary/Review4分鐘
3 個練習
Check for Understanding: Hypothesis Testing4分鐘
Check for Understanding: Hypothesis Testing Limitations2分鐘
Data Investigation Module Quiz5分鐘

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

關於 IBM

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....

關於 IBM AI Enterprise Workflow 專項課程

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

常見問題

  • 注册以便获得证书后,您将有权访问所有视频、测验和编程作业(如果适用)。只有在您的班次开课之后,才可以提交和审阅同学互评作业。如果您选择在不购买的情况下浏览课程,可能无法访问某些作业。

  • 您注册课程后,将有权访问专项课程中的所有课程,并且会在完成课程后获得证书。您的电子课程证书将添加到您的成就页中,您可以通过该页打印您的课程证书或将其添加到您的领英档案中。如果您只想阅读和查看课程内容,可以免费旁听课程。

  • 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.

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