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.
Python is the language of data science, and this class will expose you to the most important libraries (i.e., NumPy, Pandas, Matplotlib, and Scikit-learn) that will enable you to effectively do data science using Python.
Prerequisite: Basic Python Programming
In this course, you will have an opportunity to:

• Install Anaconda on a personal computer
• Understand the various options for performing data science
• Understand the reasons for Python's popularity in data science
• Learn the primary libraries for data science in Python including NumPy, Pandas, Matplotlib and Scikit-learn
• Perform exploratory data analysis using Pandas
• Use Matplotlib and Seaborn to perform data visualization
• Prepare data for machine learning
• Apply machine learning on a variety of datasets
• Understand the data science process
• Understand the big picture and the importance of data science in business, industry, and technology

We will begin by installing Anaconda, which provides the libraries required for most data problems. We will discuss the focus and strengths of the most important libraries and how they enable data analysis and the application of machine learning to defined data problems. We will then use these libraries to perform data exploration, visualization, analysis and modeling on a variety of datasets as we work through the data science process.
Topics covered in this class include:

• Course Introduction
• Overview of data science
• Understand the reasons for Python's popularity in data science
• Installing Anaconda
• Milestone 1: Learn how to use Jupyter Notebooks
• The data science process
• Essential Python data science libraries

- NumPy
- Pandas
- Matplotlib
- Scikit-learn

• Data Visualization

- Line Chart
- Scatterplot
- Pairplot
- Histogram
- Density Plot
- Bar Chart
- Boxplot

• Customizing Charts

- Prepare data for machine learning
- Milestone 2: Perform exploratory data analysis using Pandas
- Milestone 3: Apply machine learning algorithms using Scikit-learn
- Conclusion: Data Science in the real world, next steps

Date & Time:
2/18/2026:9-12 pm pst
2/20/2026:9-12 pm pst