AI Workflow: Business Priorities and Data Ingestion

About this 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.

Created by: IBM


Related Online Courses

This specialization covers the fundamentals of surveys as used in market research, evaluation research, social science and political research, official government statistics, and many other topic... more
This specialization is designed to let you explore computational thinking and beginning C programming topics, applying those concepts to develop solutions to a variety of practical problems.The... more
In this meticulously designed course, you\'ll embark on a journey to understand the fundamental principles of DevOps and the crucial role Git plays in version control and continuous integration.... more
This Specialization aims to make branding concepts accessible to every learner and to teach them to analyze and apply all the relevant concepts, using the broad and diverse toolkit of branding. It... more
This specialization provides an introduction to corporate finance and accounting, emphasizing their application to a wide variety of real-world situations spanning personal finance, corporate... more

CONTINUE SEARCH

FOLLOW COLLEGE PARENT CENTRAL