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.

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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:
8/7/2025,9-12 pm pst.
8/8/2025,9-12 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 course exposes you to real-world applications of data science and why it's become an integral part of business and academia. We will discuss the data science process and the tools used to perform data exploration, analysis, and modeling.
Prerequisite: Basic Python Programming training, or equivalent experience
Learning Objectives
In this class, you will have the opportunity to:

• Install Anaconda on a personal computer
• Understand the Data Science Field
• Become familiar with Descriptive and Inferential Statistics and statistical analysis
• Learn primary tools used for data science in Python including Pandas and Scikit-learn
• Learn how to perform exploratory data analysis
• Learn the importance of data cleaning
• Utilize common Machine Learning algorithms such as Linear and Logistic Regression
• Solidify understanding by completing hands-on exercises and milestones
• Walkthrough two data science projects

Course Outline

• Course Introduction
• Install Anaconda
• Review the Essentials of Python
• Overview of Data Science
• The Difference Between Business Analytics (BI), Data Analytics and Data Science
• Descriptive Statistics Fundamentals
• Central Tendency

- Mean

• Median
• Mode

• Spread of the Data

- Variance

• Standard Deviation
• Range

• Relative Standing

- Percentile

• Quartile
• Inter-quartile Range

• Inferential Statistics Fundamentals
• Data Distributions

- Normal Distribution

• Uniform Distribution

• The Data Science Process

- Define the Problem

• Get the Data
• Explore the Data
• Clean the Data
• Model the Data
• Communicate the Findings

• Feature Selection
• Data Cleaning

- Dropping Rows
- Imputing Missing Values

• Data Transformation

- Binary Encoding
- One-Hot Encoding
- Standardization
- Normalization

• Machine Learning Overview
• Introduction to Pandas
• Milestone 1: Use Pandas to perform data analysis on a real-world dataset.
• Data Exploration

- Describe
- Merge
- Group
- Feature Evaluation

• Feature Engineering
• Milestone 2: Perform exploratory data analysis and feature engineering
• Test/Train Split
• Model Training
• Basic Machine Learning Implementation

- Linear Regression

• Logistic Regression
• Support Vector Machine
• Decision TreeBasic Machine Learning Implementation

• Milestone 3: Perform an end-to-end project of the data science process.
• Structured Activity/Exercises/Case Studies
• Milestone Project 1: Use Pandas to perform data analysis on a real-world dataset.
• Milestone Project 2: Perform exploratory data analysis and feature engineering.
• Milestone Project 3: Perform an end-to-end project of the data science process.

Date & Time
8/7/2025,9-12 pm pst.
8/8/2025,9-12 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:
8/14/2025, 9-12 pm pst
8/15/2025, 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.
Description:
Whether you’re collecting customer feedback, performing employee evaluations, or planning an event, the first step toward creating an effective survey is to brush up on the basics of survey science. Check out our resources for online survey tips and best practices to make sure your next survey is a success!
Learning Objectives:

• Why survey?
• How to strategically plan your survey
• Understand the best practices for survey design
• Review collection techniques to get the best response rate
• How to analyze, present and share the findings
• Determine which online tool is best for your survey

Topic Outline:
Planning

• Formulating the problem or question to be answered
• Determining the research approach
• Setting objectives for information collection

Design

• Choosing a collection method (Qualtrics, Google Forms, or SurveyMonkey)
• Tips for good visual design
• Types of questions/response alternatives
• Pretesting and pilot testing
• Pitfalls to avoid
• Good question test - is it clear, easy answerable, unbiased?

Collection

• Collecting responses
• Statistical significance

Qualitative vs. Quantifiable

• Improving response rates and retention

Analysis & Reporting

• Filtering and customizing results
• Sharing results

Date & Time:

• Tue Aug 19, 9:00 am to 12:00 pm
• Wed Aug 20, 9:00 am to 12:00 pm