EncartaLabs

Data Analysis and Machine Learning with Excel

( Duration: 3 Days )

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 with Excel training course 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.

By attending Data Analysis and Machine Learning with Excel workshop, delegates 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
  • Basic concepts and calculations to understand how artificial neural networks work
  • Connect Excel to the Microsoft Azure cloud
  • Get beyond proof of concepts and build fully functional data analysis flows

  • 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.

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

Encarta Labs Advantage

  • One Stop Corporate Training Solution Providers for over 4,000 Modules on a variety of subjects
  • All courses are delivered by Industry Veterans
  • Get jumpstarted from newbie to production ready in a matter of few days
  • Trained more than 50,000 Corporate executives across the Globe
  • All our trainings are conducted in workshop mode with more focus on hands-on sessions

View our other course offerings by visiting http://encartalabs.com/course-catalogue-all.php

Contact us for delivering this course as a public/open-house workshop/online training for a group of 10+ candidates.

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