{"id":6841,"date":"2022-04-28T20:48:16","date_gmt":"2022-04-28T20:48:16","guid":{"rendered":"https:\/\/www.tun.com\/courses\/2019\/12\/23\/ai-workflow-business-priorities-and-data-ingestion\/"},"modified":"2022-04-28T20:48:17","modified_gmt":"2022-04-28T20:48:17","slug":"ai-workflow-business-priorities-and-data-ingestion","status":"publish","type":"post","link":"https:\/\/www.tun.com\/courses\/ai-workflow-business-priorities-and-data-ingestion\/ibm\/","title":{"rendered":"AI Workflow: Business Priorities and Data Ingestion"},"content":{"rendered":"
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Description<\/h2>\n

This is the first course of a six part specialization.\u00a0 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.<\/p>\n

This first course in the IBM AI Enterprise Workflow Certification specialization introduces you to the scope of the specialization and prerequisites.\u00a0 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.\u00a0 A hypothetical streaming media company will be introduced as your new client.\u00a0 You will be introduced to the concept of design thinking, IBMs framework for organizing large enterprise AI projects.\u00a0 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.\u00a0 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.
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\nBy the end of this course you should be able to:
\n1.\u00a0 Know the advantages of carrying out data science using a structured process
\n2.\u00a0 Describe how the stages of design thinking correspond to the AI enterprise workflow
\n3.\u00a0 Discuss several strategies used to prioritize business opportunities
\n4.\u00a0 Explain where data science and data engineering have the most overlap in the AI workflow
\n5.\u00a0 Explain the purpose of testing in data ingestion\u00a0
\n6.\u00a0 Describe the use case for sparse matrices as a target destination for data ingestion\u00a0
\n7.\u00a0 Know the initial steps that can be taken towards automation of data ingestion pipelines
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\nWho should take this course?
\nThis 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.
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\nWhat skills should you have?
\nIt 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.<\/p>\n

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Price: Enroll For Free!<\/h2>\n
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View Class<\/a><\/div>\n
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Language:<\/strong> <\/em>English<\/p>\n<\/div>\n

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Subtitles<\/strong>: <\/em>English<\/p>\n<\/div>\n<\/div>\n

AI Workflow: Business Priorities and Data Ingestion – IBM<\/strong><\/a><\/p>\n","protected":false},"excerpt":{"rendered":"

Description This is the first course of a six part specialization.\u00a0 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 […]<\/p>\n","protected":false},"author":1,"featured_media":19489,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"om_disable_all_campaigns":false,"_uag_custom_page_level_css":"","_monsterinsights_skip_tracking":false,"_monsterinsights_sitenote_active":false,"_monsterinsights_sitenote_note":"","_monsterinsights_sitenote_category":0,"_uf_show_specific_survey":0,"_uf_disable_surveys":false,"footnotes":""},"categories":[41],"tags":[],"class_list":["post-6841","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-ibm"],"aioseo_notices":[],"uagb_featured_image_src":{"full":["https:\/\/www.tun.com\/courses\/wp-content\/uploads\/2019\/12\/IBMonline-education.png",375,225,false],"thumbnail":["https:\/\/www.tun.com\/courses\/wp-content\/uploads\/2019\/12\/IBMonline-education-150x150.png",150,150,true],"medium":["https:\/\/www.tun.com\/courses\/wp-content\/uploads\/2019\/12\/IBMonline-education-300x180.png",300,180,true],"medium_large":["https:\/\/www.tun.com\/courses\/wp-content\/uploads\/2019\/12\/IBMonline-education.png",375,225,false],"large":["https:\/\/www.tun.com\/courses\/wp-content\/uploads\/2019\/12\/IBMonline-education.png",375,225,false],"1536x1536":["https:\/\/www.tun.com\/courses\/wp-content\/uploads\/2019\/12\/IBMonline-education.png",375,225,false],"2048x2048":["https:\/\/www.tun.com\/courses\/wp-content\/uploads\/2019\/12\/IBMonline-education.png",375,225,false]},"uagb_author_info":{"display_name":"Axiom Pegasus","author_link":"https:\/\/www.tun.com\/courses\/author\/magic\/"},"uagb_comment_info":0,"uagb_excerpt":"Description This is the first course of a six part specialization.\u00a0 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…","featured_media_src_url":"https:\/\/www.tun.com\/courses\/wp-content\/uploads\/2019\/12\/IBMonline-education.png","_links":{"self":[{"href":"https:\/\/www.tun.com\/courses\/wp-json\/wp\/v2\/posts\/6841","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.tun.com\/courses\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.tun.com\/courses\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.tun.com\/courses\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/www.tun.com\/courses\/wp-json\/wp\/v2\/comments?post=6841"}],"version-history":[{"count":0,"href":"https:\/\/www.tun.com\/courses\/wp-json\/wp\/v2\/posts\/6841\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.tun.com\/courses\/wp-json\/wp\/v2\/media\/19489"}],"wp:attachment":[{"href":"https:\/\/www.tun.com\/courses\/wp-json\/wp\/v2\/media?parent=6841"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.tun.com\/courses\/wp-json\/wp\/v2\/categories?post=6841"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.tun.com\/courses\/wp-json\/wp\/v2\/tags?post=6841"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}