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AI Workflow: Business Priorities and Data Ingestion
  • Level Professional
  • Duration 8 hours
  • Course by IBM
  • Offered by

About

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.

Modules

Everything you need to know before starting this course

    • 2 Videos
    • 6 Readings
Show info about module content

2 Videos

  • Course Introduction
  • IBM Watson Studio - Create a project

6 Readings

  • About this Course
  • Target Audience
  • Required skills
  • An introduction to IBM Watson Studio and IBM Design Thinking
  • Overview of IBM Watson Studio
  • Am I Ready?

Aren’t certain if you are ready?

    • 2 Readings
    • 1 Assignment
Show info about module content

1 Assignment

  • Readiness Quiz

2 Readings

  • Am I ready to take this Specialization?
  • Readiness Quiz Review

Workflow Overview: Data Science Process Models & Design Thinking

    • 1 Videos
    • 4 Readings
    • 1 Assignment
Show info about module content

1 Assignment

  • Process Models & Design Thinking: Check for Understanding

1 Videos

  • Workflow Overview

4 Readings

  • Advantages and Disadvantages of Process Models
  • Data Science Process Models
  • The Design Thinking Process
  • Data Science Workflow Combined with Design Thinking

End of module review & evaluation

    • 1 Readings
    • 1 Assignment
Show info about module content

1 Assignment

  • Process Models, Design Thinking, and Introduction: End of Module Quiz

1 Readings

  • Process Models, Design Thinking, and Introduction: Summary/Review

Getting started with data collection

    • 1 Videos
    • 1 Readings
Show info about module content

1 Videos

  • Data Collection Overview

1 Readings

  • Data Collection Objectives

Business Opportunities

    • 1 Videos
    • 1 Readings
    • 1 Assignment
Show info about module content

1 Assignment

  • Business Opportunities: Check for Understanding

1 Videos

  • Introduction to Business Opportunities

1 Readings

  • Identifying the Business Opportunity: Through the Eyes of our Working Example

Scientific Thinking for Business

    • 1 Videos
    • 1 Readings
    • 1 Assignment
Show info about module content

1 Assignment

  • Scientific Thinking for Business: Check for Understanding

1 Videos

  • Introduction to Scientific Thinking for Business

1 Readings

  • Scientific Thinking for Business

Gathering Data

    • 2 Videos
    • 1 Readings
    • 1 Assignment
Show info about module content

1 Assignment

  • Gathering Data: Check for Understanding

2 Videos

  • Introduction to Gathering Data
  • AI Workflow: Gathering data

1 Readings

  • Gathering Data

End of module review & evaluation

    • 1 Readings
    • 1 Assignment
Show info about module content

1 Assignment

  • Data Collection: End of Module Quiz

1 Readings

  • Data Collection: Summary/Review

Ingesting Data

    • 3 Videos
    • 7 Readings
    • 1 Assignment
Show info about module content

1 Assignment

  • Ingesting Data: Check for Understanding

3 Videos

  • Introduction to Data Ingestion
  • AI Workflow: Data ingestion
  • AI Workflow: Sparse Matrices for Data Pipeline Development

7 Readings

  • Data Engineering
  • Limitations of Extract, Transform, Load (ETL)
  • Data Ingestion in the Modern Enterprise
  • Enterprise Data Stores for Data Ingestion
  • Why We Need a Data Ingestion Process
  • Data Ingestion and Automation
  • Sparse Matrices are Used Early in Data Ingestion Development

Case Study - Data Ingestion

    • 2 Videos
    • 7 Readings
    • 1 Lab
Show info about module content

1 Labs

  • Case Study Answer Key Notebook

2 Videos

  • Using Watson Studio to Complete the Case Study
  • Case Study

7 Readings

  • Getting started Watson Studio
  • Case Study Introduction
  • Getting Started
  • Data Sources
  • PART 1: Gathering the data
  • PART 2: Checks for quality assurance (Includes Assessment)
  • PART 3: Automating the process (Includes Assessment)

End of module review & evaluation

    • 1 Readings
    • 1 Assignment
Show info about module content

1 Assignment

  • Data Ingestion: End of Module Quiz

1 Readings

  • Data Ingestion: Summary/Review

Auto Summary

This professional course, "AI Workflow: Business Priorities and Data Ingestion," is the first in a six-part IBM AI Enterprise Workflow Certification specialization on Coursera. Designed for experienced data science practitioners, it emphasizes structured processes, design thinking, and data ingestion pipelines using Python and Jupyter notebooks. The course spans 480 hours and offers Starter and Professional subscription options. Ideal for those with expertise in statistics, linear algebra, and Python, it aims to deepen skills in deploying AI within large enterprises.

Mark J Grover
Instructors

Mark J Grover

Ray Lopez, Ph.D.
Instructors

Ray Lopez, Ph.D.