Rutgers Classifieds>Rutgers Online Courses>AI Workflow: Business Priorities and Data Ingestion

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

At the end of this project, you will be able to use the different features in Microsoft Word to create a lesson plan. You will be able to create a table with content. Additionally, you will be able... more
In this hands-on project, we will build and train an XG-Boost classifier to predict whether a person has a risk of having cervical cancer. Cervical cancer kills about 4,000 women in the U.S. and... more
AWS: Network Security, Compliance and Governance is the third course of Exam Prep ANS-C01: AWS Certified Advanced Networking Specialty specialization. This course will help learners designing and... more
By the end of the specialization, you will be able to: Assess conflict types and identify the needs and responses of all conflicting parties. Identify when conflicts arise and manage them in... more
Learn fundamental concepts in data analysis and statistical inference, focusing on one and two independent samples.Created by: Johns Hopkins University more

CONTINUE SEARCH

FOLLOW COLLEGE PARENT CENTRAL