Non-techie Starter Series - AI & Data Science

Enroll in this training and receive a one-month complimentary e-learning subscription with access to 40+ courses.

This course is provided by Big Data Trunk for Stanford Technology Training Program but a limited few seats available to the public.

Students of this class may have opportunity to be considered for Internship with Big Data Trunk.

This course for non-tech professionals seeking a foundational understanding of AI and Data Science. It covers key concepts and terms in an easily comprehensible manner. No programming or tech expertise needed.

The course will begin with an introduction to AI and Data Science, including an overview of the field and its potential applications. We will explore different types of data and how they are collected, as well as the importance of data quality and how to clean and preprocess data.

Throughout the course, you will be provided with real-life examples of how AI and Data Science are used in different industries. There will be a discussion on the limitations and ethical considerations of using data and explore future trends in AI and Data Science and their potential impact.

Audience

This course is designed for professionals in HR, finance, marketing, operations, or any other non-technical field who want to stay up to date with the latest developments in AI and Data Science and leverage them to drive business value.

Prerequisites/Professional Experience

Understanding of how computers work.

Learning Outcomes

By the end of this course, participants will have gained a foundational understanding of AI and Data Science and will be able to apply this knowledge in their own work and decision-making.

Course Topics

Introduction to AI and Data Science

• What is Data Science and why is it important?
• An overview of the Data Science process and its components
• Common terminologies in AI and Data Science and what they mean

Applications & Trends of AI and Data Science

• Real-life examples of how Data Science is used in different industries
• Understanding the limitations and ethical considerations of using data
• Future trends in Data Science and its potential impact