Big Data & Artificial Intelligence 101

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

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 provides a fun and non-technical introduction to Artificial Intelligence and Machine Learning. It provides the vocabulary and basics for this exciting new world.
Prerequisite: Basic programming knowledge preferred
This Artificial Intelligence (AI) and Machine Learning (ML) class helps in awareness about AI and ML patterns and use cases in real world. You will get an understanding of ML concepts like Supervised and Unsupervised learning techniques and usages. We will discuss the difference between AI vs ML vs Deep Learning (DL) along with usage patterns. We will help you expand your vocabulary in AI to understand techniques like Classification, Clustering and Regression. Finally, we would do a ML demo to illustrate few tools and next steps.
In this course, you will have an opportunity to learn how to:

• Describe Supervised and Unsupervised learning techniques and usages
• Compare AI vs ML vs DL
• Understand techniques like Classification, Clustering and Regression
• Discuss how to identify which kinds of technique to be applied for specific use case
• Understand the popular Machine offerings like Amazon Machine Learning, TensorFlow, Azure Machine Learning, Spark mlib, Python and R etc.
• Understand the relation between Data Engineering and Data Science
• Understand the Data Science process
• Discuss Machine Learning use cases in different domains
• Identify when to use or not use Machine Learning
• Define how to form a ML team for success
• Understand usage of tools through a ML Demo and hands-on labs.

Topic Outline:

• Course Introduction
• History and background of AI and ML
• Compare AI vs ML vs DL
• Describe Supervised and Unsupervised learning techniques and usages
• Machine Learning patterns

- Classification
- Clustering
- Regression

• Gartner Hype Cycle for Emerging Technologies
• Machine Learning offerings in Industry
• Discuss Machine Learning use cases in different domains
• Understand the Data Science process to apply to ML use cases
• Understand the relation between Data Engineering and Data Science
• Identify the different roles needed for successful ML project
• Hands-on: Create account for Microsoft Azure Machine Learning Studio
• Demo: ML using Azure ML studio
• Demo: ML using Scikit-learn
• References and Next steps

Date & Time:
2/11/2026 :9-12 pm pst.
2/12/2026 :9-12 pm pst.

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 six-hour session will review the foundations of data analytics using Excel and then transfer and advance that knowledge to perform a complete data analysis using the Python programming language.
Prerequisite: Learners should have an understanding of Basic Programming and Excel.
You will have the opportunity to learn how to conduct exploratory data analysis, data visualization and hypothesis testing, and how to use Python to access and manipulate Excel files. At the end of the course, you will be able to perform a complete data analysis using Python.
Learning Objectives:
During this course, you will have the opportunity to learn how to:

• Understand the Foundations of Analytics in Excel
• Explore Variables in Excel
• Understand Exploratory Data Analysis
• Understand the Foundations of Inferential Statistics and Hypothesis Testing
• Use the Python Programming Language for Data Analysis
• Access Excel Files Using Python
• Perform Data Visualization and Exploration in Python
• Perform More Efficient and Deeper Data Analyses using Python
• Explore Correlation and Linear Regression in Excel and Python
• Use Python to Manipulate Excel Files and to perform Machine Learning

Topic Outline:
Overview of Data Analytics
Excel Review
Foundations of Analytics in Excel
Variables in Excel
Exploratory Data Analysis in Excel
Data Visualization in Excel
Introduction to the Python Programming Language
Installing Anaconda
Milestone 1: How to use Jupyter Notebooks
Python Essentials
Introduction to Pandas
Using Pandas to access Excel files
Data Analysis with Pandas
Milestone 2: Perform exploratory data analysis using Pandas
Using Python for data wrangling
Using Python to manipulate Excel files
Data Visualization in Python: Matplotlib, Pandas, Seaborn
Milestone 3: Perform data visualization using Python
Inferential Statistics and Hypothesis Testing in Python
Correlation and Linear Regression using Excel and Python
Using Python to perform machine learning
Milestone 4: Perform complete Python data analysis
Conclusion: Data Analytics in the real world, and next steps.

Date & Time:
2/19/2026 :1-4 pm pst
2/26/2026 :1-4 pm pst