Big Data & Artificial Intelligence 101

Python for Data Science

Wed, Feb 18 · 9:00 AM PST

·

Online

Online

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

Advancing into Data Analytics from Excel to Python

Thu, Feb 19 · 1:00 PM PST

·

Online

Online

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

ChatGPT for Data Scientists: Advanced Prompt Techniques

Tue, Feb 24 · 9:00 AM PST

·

Online

Online

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.

Description:
Participants will learn to design efficient prompts in this ChatGPT training to achieve desired results with fewer steps in their data science projects. They will develop the ability to comprehend and explain the basic principles of prompt engineering and its relevance in the field of data science.
Duration 1 Day
Course Code: BDT309
Learning Objectives:

• With this course, you will have the opportunity to
• Gain skills to employ ChatGPT and other GenAI tools to initiate data science projects
• Transform categorical data into numerical form using ChatGPT effectively.
• Learn to leverage Google Colab for efficient Python programming and data analysis.
• Develop skills in using ChatGPT to prepare and clean data for analysis, simplifying the preprocessing stage.
• Gain proficiency in analysing qualitative data with ChatGPT, extracting valuable interpretations from non-numerical data.

Course Outline:
Session 1

• Deriving insights from data
• Working with Google Colab
• Chat GPT Prompt Engineering
• Data Analysis with ChatGPT

Session 2

• Using ChatGPT to prepare data
• Hands-On Data Processing: Applying ChatGPT for Efficient Business Data Prep
• Creating Data Analysis Reports and Communicating with Stakeholder with ChatGPT

Training Material Provided: Yes (Digital Format)
Date & Time:
2/24/2026 :9-12 pm pst.
3/3/2026 :9-12 pm pst.

Data Storytelling With Intuitive Visualization

Tue, Feb 24 · 9:00 AM PST

·

Online

Online

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 is designed to help you understand best practices for data visualization and storytelling, even without prior experience. Learn tips to engage your audience to help them remember the key points of your data.
Audience:

• Professionals who have an audience who would benefit from insightful data communication
• Professionals who want to. create impactful storytelling with data visualization

Human brains are wired to remember stories. But real decision-making is spurred by emotion of story combined with the logic of data, influenced by visuals. This highly-interactive course will show you how to turn insights, data and recommendations into an audience-centric visual narrative.
You will use Microsoft Excel to create effective visualization of common data analysis. This course culminates in a project in which you will use sample data to create visualizations and storyboards to prepare a presentation to a fictional audience.
Learning Objectives
After this course, you will have the opportunity to develop these skills:

• Elevate the meaning behind your data
• Transform your messages into impactful data stories that resonate with your audience
• Describe data visualization best practices
• Understand an easy-to-apply framework for building audience excitement about your data

Topic Outline
Crafting the Story

• Understanding your data
• Understanding your audience
• Exploratory vs. explanatory analysis
• Sketching your ideas and stories
• Questions to ask -- What makes a good data visualization?

Information Hierarchy

• Focusing on an audience on what's most important and only revealing details as needed
• Explore - Visual exploration
• Explore - Indexes and ratios
• Convert - Grouping
• Convert - Aggregating
• Convert - Data formats

Visual Display

• Position, size, color, contrast and shape
• Typography and iconography
• Basic charts and alternative charts
• Hierarchical data
• Legends and sources
• Watch out - Cognitive overload (Eliminating distractions)
• Watch out - Signaling where to look
• Watch out - Lack of visual order

Interactivity

• Why interactive experience? (vs. static experience)
• When and where to go interactive
• Think interactively to provide productive experiences
• The right technology for your needs

Storytelling

• Defining your narrative
• Keeping audience's attention, and making everything relatable

Date &Time:
2/24/2026:9-12 PM PST
2/26/2026:9-12 PM PST