Big Data & Artificial Intelligence 101

Mon, Nov 17 · 1:00 PM PST

•

Online

Data Science for Healthcare Professionals

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.
Data science and digital image processing are becoming an increasingly integral part of health care. This course exposes you to ways data science is used to extract innovative and actionable insights from healthcare-related datasets and medical imaging.
In this course, we will examine how predictive modeling is used to assess outcomes, needs, and potential interventions. We will also explore medical image analysis which has become an inherent part of medical technology.

Prerequisites:
Basic Python programming experience
Learning Objectives:
During this course, you will have the opportunity to:

• Install Anaconda on a personal computer.
• Prepare and explore healthcare-related datasets using the primary tools for data science in Python (e.g., NumPy, Pandas, Matplotlib, Scikit-learn).
• Examine many of the unique qualities and challenges of healthcare data.
• Understand how data science is impacting medical diagnosis, prognosis, and treatment.
• Use a data-science approach to evaluate and learn from healthcare data (e.g., behavioral, genomic, pharmacological).
• Use deep learning and TensorFlow to interpret and classify medical images.
• Perform feature extraction, segmentation, and quantitative measurements of medical images.
• Understand the increasing importance of data science and image processing in healthcare.

Topic Outline:

• Course Introduction
• Overview of Data Science in Healthcare
• Milestone 1: Install Anaconda/Work with Jupyter Notebooks
• The Data Science Process
• How Data Science is transforming the healthcare sector
• Essential Python Data Science Libraries

- NumPy
- Pandas
- Matplotlib
- Scikit-learn

• Data Visualization

- Line Chart
- Scatterplot
- Pairplot
- Histogram
- Density Plot
- Boxplot
- Customizing Charts

• Milestone 2: Perform Exploratory Data Analysis of Healthcare Datasets
• Milestone 3: Use Scikit-learn to Apply Machine Learning to Healthcare Questions
• Introduction to Deep Learning for Medical Image Analysis
• Digital Image Processing
• Contrast and Brightness Correction
• Edge Detection
• Image Convolution
• Milestone 4: Use TensorFlow to Interpret and Classify Medical Images
• Conclusion: Next Steps

Structured Activity/Exercises/Case Studies:

• Milestone 1: Install Anaconda/Work with Jupyter Notebooks
• Milestone 2: Perform Exploratory Data Analysis of Healthcare Datasets
• Milestone 3: Use Scikit-learn to Apply Machine Learning to Healthcare Questions
• Milestone 4: Use TensorFlow to Interpret and Classify Medical Images

Date & Time :
11/17/2025: 1-4 PM PST
12/1/2025: 1-4 PM PST
12/8/2025: 1-4 PM PST
12/15/2025: 1-4 PM PST

Tue, Nov 18 · 9:00 AM PST

•

Online

Machine Learning with Python and Libraries

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 class helps increase awareness about Machine Learning patterns and use cases in the real world, and will help you understand the different ML techniques. Learn about popular ML offerings, and utilize Jupyter Notebooks to perform hands-on labs.

Prerequisite: Basic Python Programming training, or equivalent experience

After this course, you will be able to:

• Describe the role of Machine Learning and where it fits into Information Technology strategies
• Explain the technical and business drivers that result from using Machine Learning
• Describe Supervised and Unsupervised learning techniques and usages
• 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.
• Install and Setup Anaconda.
• Perform hands-on activity using Jupyter Notebooks.

Topic Outline:
Course Introduction

• History and background of Machine Learning
• Compare Traditional Programming Vs Machine Leaning
• Supervised and Unsupervised Learning Overview
• Machine Learning patterns

- Classification
- Clustering
- Regression

• Gartner Hype Cycle for Emerging Technologies
• Machine Learning offerings in Industry
• Hands-on exercise 1: Install and Setup Anaconda.
• Python Libraries

- NumPy
- Pandas
- Scikit Learn

• Hands-on exercise 2: Data Analysis using Pandas
• Algorithms

- Linear Regression
- Decision Tree
- Random Forest
- K-Means Clustering

• Hands-on exercise 3: Perform Linear regression using Scikit-learn
• Hands-on exercise 4: Perform Decision tree on Titanic Data set using Scikit-learn
• References and Next steps

Tue, Nov 18 · 9:00 AM PST

•

Online

Data Analysis and Visualizations on Tableau Server

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.

Learn the capabilities of Tableau report manipulation and visualization. This class is intended for end users, not developers.
After reserving your spot, you will receive instructions on how to join the live online class via email prior to the day of the class.
Important Note:
This training will be conducted through Tableau Server within a browser, not Tableau Desktop. We will be using Tableau Online, as it has similar functionality and Tableau Server does not have a free trial. This class is intended for end-users, not developers.

For Developers interested in using Tableau Desktop, we recommend exploring the other class options, including "Introduction to Tableau Desktop Basics" or "Tableau Desktop Intermediate".
Tableau is a business intelligence tool that allows anyone to easily connect to data, then visualize and create interactive, sharable dashboards. These dashboards are a collection of various views of the data, such as charts, graphs, or summary pivots.
In this class, learn how Tableau Server is used to share and interact with visualizations securely across an organization. This training focuses on interacting with and editing workbooks that have been published to Tableau Server, and is intended for end users, not developers.
By the end of this session participants should feel comfortable working with filters, tabs, sharing, downloading raw data, setting-up favorites, using pause, working with edit mode, and much more.
After a brief once-over of the interface, students will create and manipulate numerous visualizations of the sample data. This session will be a mix of walking through examples with the class and a series of exercises for the student to dig through the data and create a visualization that meets their needs.
Activities and exercises will include but not be limited to:
- Working with filters across tabs
- Setting up subscriptions
- Sharing and downloading visualizations and raw data
- Working with the "pause" button
- Creating visualizations in edit mode
- Navigating between sites, projects, worksheets and dashboards
- Work with charts, graphs, maps and pivots

Tue, Nov 18 · 1:00 PM PST

•

Online

Scraping and Sourcing Data with Python

Online

## Details

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.

In this class, we will:

• Explore many sources and repositories for valuable data acquisition such as open government and university datasets
• Explore popular social APIs (e.g., Facebook, Spotify, Twitter) and domain-specific APIs (e.g., healthcare, news, science and math) that store a wealth of data
• Discuss methods to query web servers, and request and parse data to extract the information you need
• Explore scraping various types of data from websites and how to read and extract text from documents (e.g., PDF, Word) along with methods to clean and store sourced and scraped data.

Learning Objectives
During this course, you will have the opportunity to:

• Explore a Variety of Public Data Repositories
• Understand Effective Means to Search for Valuable Data
• Use the Python Programming Language to Source and Scrape Data
• Use Popular Social and Domain-specific APIs to Access Data (e.g., Slack)
• Extract Text from Documents (e.g., data in PDFs, Word) and access PDF Tables
• Scrape Data from Web Pages
• Clean Scraped Data and store Sourced and Scraped Data

Topic Outline
Overview of Data Sourcing

1. Public Open Dataset
2. Government Data
3. University Data
4. Milestone 1 Learning Exercise: Explore public data repositories

Introduction to the Python Programming Language

1. Installing Anaconda
2. Milestone 2 Learning Exercise: Learn how to use Jupyter Notebooks

Using Public APIs (Application Programming Interfaces)

1. Explore Popular and Domain-specific APIs
2. Common Conventions
3. Parsing JSON
4. Milestone 3 Learning Exercise: Access a public API (e.g., Facebook, Twitter, Google)

Extracting Text from Documents

1. Milestone 4 Learning Exercise: Extract data from PDFs

Overview of Data Scraping

1. Introduction to BeautifulSoup
2. Parsing HTML and Javascript
3. Milestone 5 Learning Exercise: Scrape data from a website

Cleaning Scraped Data

1. Storing Sourced and Scraped Data

Conclusion: Next steps
Prerequisites
Learners should have an understanding of Basic Python Programming.