Data Analysis and Machine Learning with Excel

Huge progress has been made in teaching computers to perform difficult tasks, especially those that are repetitive and time-consuming for humans. Much of this data analysis and machine learning work is completed leveraging modern scripting and programming skills, such as R or Python programming, for example. It’s easy for Excel users not fluent in these skills or languages to feel sidelined from this innovation wave. However, that isn’t the reality. The truth is that a large amount of the work needed to develop and use a machine learning model can be done in Excel.

Data Analysis and Machine Learning in Excel is a three-day, foundation-level, hands-on course that explores the fast-changing field and how experienced Excel users can leverage their skills to contribute. The course starts by providing an introduction to machine learning, making every concept clear and understandable. Then, it shows every step of a machine learning project, from data collection, reading from different data sources, developing models, and visualizing the results using Excel features and offerings. In every lesson, there are several examples and hands-on exercises that will show the reader how to combine Excel functions, add-ins, and connections to databases and to cloud services to reach the desired goal: building a full data analysis flow. Different machine learning models are shown, tailored to the type of data to be analyzed.

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Join an engaging hands-on learning environment, where you’ll 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

This course has a 50% hands-on labs to 50% lecture ratio with engaging instruction, demos, group discussions, labs, and project work. This is not a basic class.

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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

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

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

Data Cleansing and Preliminary Data Analysis

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

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

Data Mining Models in Excel Hands-On Examples

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

Implementing Time Series

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

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

Artificial Neural Networks

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

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

The Future of Machine Learning

Automatic data analysis flows
Automated machine learning

Before attending this course, you should have:

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

Analyst, Data Scientist, and other professionals who want a practical guide to extract the most out of Excel for data preparation, applying machine learning models, and understanding the outcome of your data analysis.

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Data Analysis and Machine Learning with Excel

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