Data Analysis and Machine Learning with Excel

Course Objectives

This “skills-centric” course is about 50% hands-on lab and 50% lecture, with extensive practical exercises designed to reinforce fundamental skills, concepts and best practices taught throughout the course.  Students will be led through a series of progressively advanced topics, where each topic consists of lecture, group discussion, comprehensive hands-on lab exercises, and lab review.

Working in a hands-on learning environment led by our expert practitioner, attendees will learn to:

• Use Excel to preview and cleanse datasets
• Understand correlations between variables and optimize the input to machine learning models
• Use and evaluate different machine learning models from Excel
• Understand the use of different visualizations
• Learn the basic concepts and calculations to understand how artificial neural networks work
• Learn how to connect Excel to the Microsoft Azure cloud
• Get beyond proof of concepts and build fully functional data analysis flows

Course Prerequisites

This course is geared for attendees seeking next-level use cases using Automated Machine Learning, and artificial neural network, which simplifies the analysis task and represents the future of machine learning.

Attending students should have the following incoming skills

• Basic to Intermediate IT Skills and Machine Learning with Microsoft Excel 2019 knowledge
• Good foundational mathematics or logic skills
• Basic Linux skills, including familiarity with command-line options such as ls, cd, cp, and su

Course Agenda

1. Implementing Machine Learning Algorithms

• Technical requirements
• Understanding learning and models
• Focusing on model features
• Studying machine learning models in practice
• Comparing underfitting and overfitting
• Evaluating models

2. Hands-On Examples of Machine Learning Models

• Technical requirements
• Understanding supervised learning with multiple linear regression
• Understanding supervised learning with decision trees
• Understanding unsupervised learning with clustering

3. Importing Data into Excel from Different Data Sources

• Technical requirements
• Importing data from a text file
• Importing data from another Excel workbook
• Importing data from a web page
• Importing data from Facebook
• Importing data from a JSON file
• Importing data from a database

4. Data Cleansing and Preliminary Data Analysis

• Technical requirements
• Cleansing data
• Visualizing data for preliminary analysis
• Understanding unbalanced datasets

5. Correlations and the Importance of Variables

• Technical requirements
• Building a scatter diagram
• Calculating the covariance
• Calculating the Pearson's coefficient of correlation
• Studying the Spearman's correlation
• Understanding least squares
• Focusing on feature selection

6. Data Mining Models in Excel Hands-On Examples

• Technical requirements
• Learning by example – Market Basket Analysis
• Learning by example – Customer Cohort Analysis

7. Implementing Time Series

• Technical requirements
• Modeling and visualizing time series
• Forecasting time series automatically in Excel
• Studying the stationarity of a time series

8. Visualizing Data in Diagrams, Histograms, and Maps

• Technical requirements
• Showing basic comparisons and relationships between variables
• Building data distributions using histograms
• Representing geographical distribution of data in maps
• Showing data that changes over time

9. Artificial Neural Networks

• Technical requirements
• Introducing the perceptron – the simplest type of neural network
• Building a deep network
• Understanding the backpropagation algorithm

10. Azure and Excel - Machine Learning in the Cloud

• Technical requirements
• Introducing the Azure Cloud
• Using AMLS for free – a step-by-step guide
• Loading your data into AMLS
• Creating and running an experiment in AMLS

11. The Future of Machine Learning

• Automatic data analysis flows
• Automated machine learning

Course Materials

Each student will receive a Student Guide with course notes, code samples, software tutorials, diagrams and related reference materials and links (as applicable). Our courses also include step by step hands-on lab instructions and and solutions, clearly illustrated for users to complete hands-on work in class, and to revisit to review or refresh skills at any time. Students will also receive the project files (or code, if applicable) and solutions required for the hands-on work.