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Data Analysis in Excel – Courses, Classes, Training, Tutorials

In this Specialization, you’ll learn to frame business challenges as data questions.

Mastering Data Analysis in Excel is course 2 of 5 in the Excel to MySQL: Analytic Techniques for Business Specialization.

Data Analysis in Excel – Courses, Classes, Training, Tutorials

You’ll use powerful tools and methods such as Excel, Tableau, and MySQL to analyze data, create forecasts and models, design visualizations, and communicate your insights. In the final Capstone Project, you’ll apply your skills to explore and justify improvements to a real-world business process.


CHANDIGARH INSTITUTE provides the best excel data analysis training in Chandigarh, Mohali and Panchkula.

  • In business, data and algorithms create economic value when they reduce uncertainty about financially important outcomes.
  • This course teaches the concepts and mathematical methods behind the most powerful.
  • Universal metrics used by Data Scientists to evaluate the uncertainty-reduction – or information gain – predictive models provide.
  • We focus on the two most common types of predictive model – binary classification and linear regression.
  • You will learn metrics to quantify for yourself the exact reduction in uncertainty each can offer.


  • The standard way of representing forecasts in data science.
  • You will learn proper methodology to avoid common data-analytic pitfalls when forecasting – such as being “fooled by randomness” and over-fitting “noise” as if it were “signal.”
  • Uniquely among data-analytic offerings, this course empowers you to understand very well.
  • You can apply quite advanced information theory methods – Bayesian Logical Data Analysis – in business practice.
  • Without needing any calculus or matrix algebra, or any knowledge of Matlab or software programming.

These metrics are applicable to any form of model that uses new information to improve predictions cast in the form of a known probability distribution

Benefits of This Course:

Be aware that this is not a broad general Excel skills course; it focuses on use of Excel to calculate information-related metrics, and to solve real business problems, such as:

  • Developing your own predictive analytic model for which credit card applicants a bank should accept and which reject as too risky.
  • Real problems are complicated! Personally I think learning to solve real problems is also a great way to learn Excel.
  • We use specific tools in the Excel toolbox to build something useful, and you can always go back and learn more tools in the toolbox.
  • More Excel functions – if and when you ever need them. This course requires some mathematical background
  • You should already know how to solve for an unknown using algebra.
  • Have a basic familiarity with sigma (summation) notation; the concept of logarithms and working with bases other than base 10 (including base 2, and the natural logarithm and base “e”).
  • Probability theory concepts such as calculating conditional, product, and joint probabilities.
  • These concepts are assumed in the course rather than taught.
  • All the “new” math taught in the course is summarized in a downloadable PDF document – “Mathematical Supplement”.
  • Refer to it to decide if the difficulty level of this course seems right for you
  • Moreover you will be able to answer all homework and quiz questions either by using basic algebra, or with the special custom Microsoft Excel Templates provided.
  • Nor is any prior experience with Excel required.
  • We will cover in detail at the beginning everything you need to know about using Excel to succeed in the course itself.


Week 1

About this Specialization

Excel Essentials for Beginners
  • Introduction to the Specialization
  • Functions on Arrays
  • Introduction to This Course
  • Important Course Materials
  • Basic Excel Syntax

Week 2

Binary Classification

  • Binary Classification and the Confusion Matrix

Week 3

Information Measures

  • Introduction to Measuring Uncertainty
  • New Data and Information Gain

Week 4

Linear Regression

  • Introduction to Parametric Models
  • Unpacking Linear Regression

Week 5

  • Samples and Random Variables
  • Samples and Random Variables

Week 6

Final Course Project

Case Study: Modeling Credit Card Default Risk and Customer Profitability

Quiz: Part 1: Building your Own Binary Classification Model
Part 2: Should the Bank Buy Third-Party Credit Information?
Quiz: Part 3: Comparing the Information Gain of Alternative Data and Models
Quiz: Part 4: Modeling Profitability Instead of Default
Assignment: Part 5: Modeling Credit Card Default Risk and Customer Profitability

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