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學生對 IBM 提供的 AI Workflow: Data Analysis and Hypothesis Testing 的評價和反饋

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96 個評分

課程概述

This is the second course in the IBM AI Enterprise Workflow Certification specialization.  You are STRONGLY encouraged to complete these courses in order as they are not individual independent courses, but part of a workflow where each course builds on the previous ones.   In this course you will begin your work for a hypothetical streaming media company by doing exploratory data analysis (EDA).  Best practices for data visualization, handling missing data, and hypothesis testing will be introduced to you as part of your work.  You will learn techniques of estimation with probability distributions and extending these estimates to apply null hypothesis significance tests. You will apply what you learn through two hands on case studies: data visualization and multiple testing using a simple pipeline.   By the end of this course you should be able to: 1.  List several best practices concerning EDA and data visualization 2.  Create a simple dashboard in Watson Studio 3.  Describe strategies for dealing with missing data 4.  Explain the difference between imputation and multiple imputation 5.  Employ common distributions to answer questions about event probabilities 6.  Explain the investigative role of hypothesis testing in EDA 7.  Apply several methods for dealing with multiple testing   Who should take this course? This course targets existing data science practitioners that have expertise building machine learning models, who want to deepen their skills on building and deploying AI in large enterprises. If you are an aspiring Data Scientist, this course is NOT for you as you need real world expertise to benefit from the content of these courses. What skills should you have? It is assumed that you have completed Course 1 of the IBM AI Enterprise Workflow specialization and have a solid understanding of the following topics prior to starting this course: Fundamental understanding of Linear Algebra; Understand sampling, probability theory, and probability distributions; Knowledge of descriptive and inferential statistical concepts; General understanding of machine learning techniques and best practices; Practiced understanding of Python and the packages commonly used in data science: NumPy, Pandas, matplotlib, scikit-learn; Familiarity with IBM Watson Studio; Familiarity with the design thinking process....

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PM

2020年4月2日

More practicality and assignment should me there. Which is more helpful for the learners.

R

2020年7月6日

Very Informative and Labs for Hands-on session was useful.

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1 - AI Workflow: Data Analysis and Hypothesis Testing 的 16 個評論(共 16 個)

創建者 Olivier R

2020年5月6日

創建者 Jonathan V

2020年5月27日

創建者 Pralay M

2020年4月3日

創建者 Mahjube C

2020年5月18日

創建者 Rangarajan m

2020年7月7日

創建者 Vaibhav K

2022年9月12日

創建者 Rafail M

2020年10月5日

創建者 Théophile P

2021年4月29日

創建者 SALVADOR L M

2020年9月15日

創建者 Shoaib Q

2020年12月13日

創建者 BHAVANA g

2020年8月30日

創建者 Brunello B

2021年4月24日

創建者 Pertti V

2020年8月13日

創建者 SUPARNA C

2020年12月18日

創建者 Gaurav S

2020年8月3日

創建者 Vasyl R

2020年7月1日