Data Analysis and Machine Learning with 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.

Retail Price: $2,195.00

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Course Days: 3


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WHAT YOU'LL LEARN

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

WHO SHOULD ATTEND?

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.

PREREQUISITES

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

COURSE OUTLINE

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


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