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AI Workflow: Data Analysis and Hypothesis Testing(으)로 돌아가기

IBM의 AI Workflow: Data Analysis and Hypothesis Testing 학습자 리뷰 및 피드백

4.2
별점
91개의 평가
15개의 리뷰

강좌 소개

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....

최상위 리뷰

PM
2020년 4월 2일

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

R
2020년 7월 6일

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

필터링 기준:

AI Workflow: Data Analysis and Hypothesis Testing의 15개 리뷰 중 1~15

교육 기관: Pralay M

2020년 4월 3일

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

교육 기관: Mahjube C

2020년 5월 18일

most of the content is in text format

교육 기관: Olivier R

2020년 5월 6일

Quizzes mark you as correct even if you're not, the answer keys are missing from notebooks, the material briefly glosses over important concepts with no depth at all. Were these issues addressed, this course would be excellent, but it sorely lacks because of it.

교육 기관: Jonathan V

2020년 5월 27일

Instructors are completely absent and ignore questions from students, vital course materials are missing, typos everywhere. This series of courses from IBM have been terrible and are of much lower quality than other e-learning offerings.

교육 기관: Rangarajan m

2020년 7월 7일

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

교육 기관: Rafail M

2020년 10월 5일

Great

교육 기관: Théophile P

2021년 4월 29일

The part on the EDA is very insightful, I learned a lot about data manipulation in Pandas. The hypothesis testing was very short so I didn't expect much. Still interesting to know about multiple hypothesis testing. The part with IBM Watson is very out of context, I don't think it makes sense in the context of this class.

Thanks for the good quality material otherwise.

교육 기관: SALVADOR L M

2020년 9월 15일

Es necesario leer las referencias en los temas, porque con sólo el contenido del tema es complicado entender

교육 기관: Shoaib Q

2020년 12월 13일

Very detailed course.

교육 기관: BHAVANA g

2020년 8월 30일

This course is more helpful for math geeks as most of the discussions on 2nd week are completely oriented around maths. It is tough to follow 2nd week module for someone who doesn't have sound math background like me.

교육 기관: Brunello B

2021년 4월 24일

The Watson Studio info are not clear or not updated.

I had to work locally!!

Would you please fix these point as soon as possible to allow the students to use the content on cloud!

교육 기관: Pertti V

2020년 8월 13일

Last excercise would need some more explanation.

There are SO MANY misspellings in the texts by the way...

교육 기관: SUPARNA C

2020년 12월 18일

more code example will be better to the person who are not from statistic or data science back ground.

교육 기관: Gaurav S

2020년 8월 3일

Course should be a little more elaborative

교육 기관: Vasyl R

2020년 7월 1일

Missing answers to notebooks. Not well explained concepts.