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7 Bewertungen

Über den Kurs

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....

Top-Bewertungen

PM

Apr 03, 2020

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

RS

Jul 07, 2020

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

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1 - 7 von 7 Bewertungen für AI Workflow: Data Analysis and Hypothesis Testing

von Pralay M

Apr 03, 2020

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

von Mahjube C

May 18, 2020

most of the content is in text format

von Olivier R

May 06, 2020

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.

von Jonathan V

May 27, 2020

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.

von Rangarajan S

Jul 07, 2020

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

von Gaurav S

Aug 03, 2020

Course should be a little more elaborative

von Vasyl R

Jul 02, 2020

Missing answers to notebooks. Not well explained concepts.