Opiniones y comentarios de aprendices correspondientes a AI Workflow: Business Priorities and Data Ingestion por parte de IBM
4.2
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22 calificaciones
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4 revisiones
Acerca del Curso
This is the first course of a six part 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.
This first course in the IBM AI Enterprise Workflow Certification specialization introduces you to the scope of the specialization and prerequisites. Specifically, the courses in this specialization are meant for practicing data scientists who are knowledgeable about probability, statistics, linear algebra, and Python tooling for data science and machine learning. A hypothetical streaming media company will be introduced as your new client. You will be introduced to the concept of design thinking, IBMs framework for organizing large enterprise AI projects. You will also be introduced to the basics of scientific thinking, because the quality that distinguishes a seasoned data scientist from a beginner is creative, scientific thinking. Finally you will start your work for the hypothetical media company by understanding the data they have, and by building a data ingestion pipeline using Python and Jupyter notebooks.
By the end of this course you should be able to:
1. Know the advantages of carrying out data science using a structured process
2. Describe how the stages of design thinking correspond to the AI enterprise workflow
3. Discuss several strategies used to prioritize business opportunities
4. Explain where data science and data engineering have the most overlap in the AI workflow
5. Explain the purpose of testing in data ingestion
6. Describe the use case for sparse matrices as a target destination for data ingestion
7. Know the initial steps that can be taken towards automation of data ingestion pipelines
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 you 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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1 - 4 de 4 revisiones para AI Workflow: Business Priorities and Data Ingestion
por Don W
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Feb 17, 2020
The course goes over practical considerations relevant to applying data science in the real world, but the final case study focuses more on data ingestion. It would have been nice if there was some component dedicated to practicing the 'empathize' stage and gaining business problem awareness.
por Tracy P
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Feb 23, 2020
Great course; would be better if the case study file was not broken (missing files, missing table in db, etc.)
por Luis L
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Jan 10, 2020
Basic introduction to data ingestion pipeline.
por Yifan Z
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Feb 16, 2020
For all of these courses, no data source has been provided 100%. All of them have an issue and can not be used for the case study. Also for the second module, even no solution has been provided in the case-study-solution notebook. To be honest, the lectures didn't provide us enough material to deal with the course and I totally learn nothing from this course. It just wastes our time.
