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AI Workflow: Business Priorities and Data Ingestion

Approx. 8 hours to complete

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Course Summary

Learn how to prioritize business goals and effectively manage data ingestion in the IBM AI Workflow: Business Priorities and Data Ingestion course.

Key Learning Points

Understand the importance of prioritizing business goals in AI projects
Learn how to effectively manage data ingestion to ensure accurate and reliable data
Discover how to use IBM Watson Studio and IBM Cloud to streamline data ingestion and management

Job Positions & Salaries of people who have taken this course might have

- USA: $60,000 - $120,000
- India: ₹400,000 - ₹1,200,000
- Spain: €25,000 - €60,000
- USA: $60,000 - $120,000
- India: ₹400,000 - ₹1,200,000
- Spain: €25,000 - €60,000
- USA: $80,000 - $150,000
- India: ₹600,000 - ₹2,000,000
- Spain: €35,000 - €80,000
- USA: $60,000 - $120,000
- India: ₹400,000 - ₹1,200,000
- Spain: €25,000 - €60,000
- USA: $80,000 - $150,000
- India: ₹600,000 - ₹2,000,000
- Spain: €35,000 - €80,000
- USA: $100,000 - $200,000
- India: ₹800,000 - ₹3,000,000
- Spain: €45,000 - €100,000

Learning Outcomes

Prioritize business goals in AI projects
Effectively manage data ingestion for accurate and reliable data
Use IBM Watson Studio and IBM Cloud to streamline data ingestion and management

Prerequisites or good to have knowledge before taking this course

Basic understanding of AI and machine learning concepts
Familiarity with IBM Watson Studio and IBM Cloud

Course Difficulty Level

Intermediate

Course Format

Online self-paced course
Video lectures
Assignments and quizzes

Similar Courses

IBM AI Enterprise Workflow Certification
IBM AI Engineering Certification

Related Education Paths

Notable People in This Field

Principal Data Scientist at Booz Allen Hamilton
Chief Decision Scientist at Google Cloud

Related Books

Description

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.

Outline

IBM AI Enterprise Workflow Introduction
Course Introduction
IBM Watson Studio - Create a project
Workflow Overview
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?
Am I ready to take this Specialization?
Readiness Quiz Review
Advantages and Disadvantages of Process Models
Data Science Process Models
The Design Thinking Process
Data Science Workflow Combined with Design Thinking
Process Models, Design Thinking, and Introduction: Summary/Review
Readiness Quiz
Process Models & Design Thinking: Check for Understanding
Process Models, Design Thinking, and Introduction: End of Module Quiz

Data Collection
Data Collection Overview
Introduction to Business Opportunities
Introduction to Scientific Thinking for Business
Introduction to Gathering Data
AI Workflow: Gathering data
Data Collection Objectives
Identifying the Business Opportunity: Through the Eyes of our Working Example
Scientific Thinking for Business
Gathering Data
Data Collection: Summary/Review
Business Opportunities: Check for Understanding
Scientific Thinking for Business: Check for Understanding
Gathering Data: Check for Understanding
Data Collection: End of Module Quiz

Data Ingestion
Introduction to Data Ingestion
AI Workflow: Data ingestion
AI Workflow: Sparse Matrices for Data Pipeline Development
Using Watson Studio to Complete the Case Study
Case Study
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
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)
Data Ingestion: Summary/Review
Ingesting Data: Check for Understanding

Summary of User Reviews

Discover the IBM AI Workflow: Business Priorities and Data Ingestion course on Coursera. This highly-rated course offers valuable insights on AI workflow and teaches you how to prioritize your business needs while ingesting data. Many users praise the course for its comprehensive coverage and hands-on approach to learning.

Key Aspect Users Liked About This Course

The hands-on approach to learning is highly appreciated by many users.

Pros from User Reviews

Comprehensive coverage of AI workflow and business priorities.
Hands-on approach to learning with practical exercises and real-world examples.
Great instructor who is knowledgeable and engaging.
Course content is well-structured and easy to follow.
Excellent preparation for AI-related certifications and job roles.

Cons from User Reviews

Some users found the course content to be too basic or introductory.
The course may require prior knowledge of coding or programming.
Some users found the pace of the course to be too slow or too fast.
Limited interaction with other students or the instructor.
The course may not be suitable for experienced AI professionals.

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