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.

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 :
12/3/2025: 9-12 PM PST
12/5/2025: 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 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:
1.Overview of Data Analytics
2.Excel Review

• Foundations of Analytics in Excel
• Variables in Excel
• Exploratory Data Analysis in Excel
• Data Visualization in Excel

3. Introduction to the Python Programming Language

• Installing Anaconda

4. Milestone 1: How to use Jupyter Notebooks
Python Essentials

• Introduction to Pandas
• Using Pandas to access Excel files
• Data Analysis with Pandas

5. 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

6. 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

7. Milestone 4: Perform complete Python data analysis
8.Conclusion: Data Analytics in the real world, and next steps.

Date & Time :
12/3/2025: 1-4 pm pst
12/5/2025: 1-4 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:

This course is designed for non-technical professionals who want to gain a foundational understanding of Artificial Intelligence (AI) and Data Science. The course will cover key concepts and terminologies in AI and Data Science in a way that is easy to understand, without requiring any programming or technical expertise.
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, we will provide real-life examples of how AI and Data Science are used in different industries. We will also discuss the limitations and ethical considerations of using data and explore future trends in AI and Data Science and their potential impact.
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. This course is ideal 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.
Learning Objectives:

• Acquire a solid grasp of AI and Data Science, even without technical background, to make informed decisions in your field.
• Master key concepts and terms in AI and Data Science.
• Learn to navigate different types of data, their collection methods, .
• Understand and gain skills in data cleaning and preprocessing.
• Explore real-world examples showcasing how AI and Data Science are effectively applied across various industries.
• Understand the ethical considerations and limitations tied to utilizing data, ensuring responsible and informed application of AI and Data Science solutions.
• Understand informed application of AI and Data Science solutions.
• Understand Gain insights into emerging trends within AI and Data Science.

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.

Unlock data insights without coding! This hands-on course covers AI-powered no-code tools like ChatGPT to clean, analyze, and visualize data. Learn to automate workflows and make data-driven decisions using generative AI for business and analytics.

Learning Objectives:
Participants will have the opportunity to:
- Explore the role of AI in modern data analytics.
- Use generative AI for data interpretation, analysis, and insights generation.
- Leverage AI-powered automation tools for data cleaning and transformation.
- Identify trends and patterns in data without coding.
- Apply AI-powered data analytics in decision-making processes.

Topic Outline:
Introduction to No-Code AI for Data Analytics
- The evolution of data analytics and AI¿s role
- Understanding no-code vs. traditional analytics
- Overview of AI-powered no-code tools
Pre-requisite: Basic understanding of data concepts
Audience: Gen AI enthusiasts

Date & Time:
12/08/25 : 1 to 4 pm PST
12/15/25 :1 to 4 pm PST