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.
Description:
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

Date:

• Thu Jul 17, 1:00 pm to 4:00 pm
• Tue Jul 22, 1:00 pm to 4:00 pm

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 session will provide a non-intimidating introduction to Artificial Intelligence. We’ll discuss the differences between AI and Machine Learning, and we’ll delve into the technology behind AI, including neural networks and Deep Learning.
The lecture will discuss how machines learn and why data is so important. We'll talk about building AI projects and walk through a brief demonstration. We’ll also look at some of the amazing things that AI can do, and we’ll discuss how to spot opportunities for AI in your organization.
Structured Activity/Case Studies:
Demonstration: AI using Google Cloud Platform
Learning Objectives

• Understand the differences among AI, Machine Learning, and Deep Learning
• Understand how machines learn
• Understand the importance of data and how to acquire appropriate data
• Understand AI strategy and building AI projects
• Recognizing opportunities for AI
• Understand some of the tools employed in executing AI

Topic Outline

• AI, Machine Learning and Deep Learning
• How machines learn and the importance of data
• Shallow vs. Deep Neural Networks
• Convolutional Neural Networks and use cases
• Recurrent Neural Networks and use cases
• Demonstration - AI using Google Cloud Platform
• Examples of AI in the real world
• Identifying opportunities for AI in your world
• Next steps

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 lecture will give a broad overview of Data Science. We will clarify the relationship between Data Science and Machine Learning and explore the Data Science process.
In this session, we will talk about identifying an effective data analysis question that is actionable, and how to get the right data to answer the question. We will discuss data exploration and the importance of clean data, complete data, and the quantity and variety of data. We will also cover how to effectively apply and evaluate Machine Learning models.
The lecture will briefly demonstrate how to work through a Data Science project using Pandas and Scikit-learn, highlighting the variety of choices that need to be made throughout the process that determines its success.
Structured Activity/Case Studies:
Demonstration -- the Data Science process using pandas and scikit-learn

Learning Objectives

• Understand the relationship between Data Science and Machine Learning
• Become familiar with the Data Science process
• Identify effective data analysis questions that are actionable
• Identify effective data sources
• Understand the importance of clean, complete, and quantity of data
• Understand how Machine Learning is applied and evaluated within the Data Science process
• Become familiar with some of the tools used throughout the process

Topic Outline

• Introduction to lecture
• Data Science vs. Machine Learning
• The Data Science process
• The importance of data
• Exploring and transforming data
• Creating and evaluating Machine Learning models
• Developing an effective Data Science strategy
• Demonstra

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

Prerequisites
Working knowledge of Microsoft Excel

Date & Time:

• Tue Aug 5, 9:00 am to 12:00 pm
• Wed Aug 6, 9:00 am to 12:00 pm