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Learner Reviews & Feedback for AI Workflow: Business Priorities and Data Ingestion by IBM

4.3
stars
173 ratings

About the Course

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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26 - 38 of 38 Reviews for AI Workflow: Business Priorities and Data Ingestion

By BHAVANA g

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Aug 17, 2020

Really nice... I have never automated the process of loading data..

This is new and business oriented when compared to other courses.

Although, prior knowledge of playing around with ml is required.

By Shen H

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Dec 15, 2020

everything is good for the class except the notebook for Watson can not get data correctly into the dataframe.

By Sourav D

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May 28, 2020

This is a great course .Lectures and materials are excellent.case study is not organised properly.

By Pravin P

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Feb 9, 2021

The theory details are good. also the assignment gives us the complete understanding & practise

By khalil e

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Jul 8, 2020

very interesting to learn good practices for data digestion

By Mahjube C

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May 13, 2020

The Data Ingestion notebook was such a great experience.

By 1200_AdityaSharma

•

Sep 7, 2025

good

By Shubham K

•

Sep 7, 2024

good

By Erwin F

•

Jul 1, 2020

Some of the answers did not work. Location of files not consistent with code. Some bits very detailed.

Overtesting

By lorenzo s

•

Oct 8, 2020

Fine, even if a good knowledge of python and related packages is required

By Alberto P

•

Nov 9, 2020

Not so interesting , a little slow

By Matthew D

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Jul 7, 2020

I decided not to stick with it. It just wasn't coherently put together.

By Dennis L

•

Aug 29, 2020

Grader problem unfixed by IBM