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Mastering Data Analysis in Excel

Approx. 21 hours to complete

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Course Summary

This course focuses on teaching students how to use Excel to analyze data and make data-driven decisions. Through hands-on exercises and examples, students will learn how to perform statistical analysis, create charts and graphs, and more.

Key Learning Points

Learn how to use Excel to analyze data
Perform statistical analysis and create charts and graphs
Gain skills in data-driven decision making

Job Positions & Salaries of people who have taken this course might have

Data Analyst
- USA: $62,453
- India: ₹451,668
- Spain: €27,133
Business Analyst
- USA: $70,009
- India: ₹500,000
- Spain: €30,000
Financial Analyst
- USA: $63,103
- India: ₹476,352
- Spain: €29,000

Learning Outcomes

Ability to use Excel for data analysis
Knowledge of statistical analysis techniques
Skills in data-driven decision making

Prerequisites or good to have knowledge before taking this course

Basic knowledge of Excel
Basic understanding of statistics

Course Difficulty Level

Beginner

Course Format

Online
Self-paced

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Description

Important: The focus of this course is on math - specifically, data-analysis concepts and methods - not on Excel for its own sake. We use Excel to do our calculations, and all math formulas are given as Excel Spreadsheets, but we do not attempt to cover Excel Macros, Visual Basic, Pivot Tables, or other intermediate-to-advanced Excel functionality.

This course will prepare you to design and implement realistic predictive models based on data. In the Final Project (module 6) you will assume the role of a business data analyst for a bank, and develop two different predictive models to determine which applicants for credit cards should be accepted and which rejected. Your first model will focus on minimizing default risk, and your second on maximizing bank profits. The two models should demonstrate to you in a practical, hands-on way the idea that your choice of business metric drives your choice of an optimal model. The second big idea this course seeks to demonstrate is that your data-analysis results cannot and should not aim to eliminate all uncertainty. Your role as a data-analyst is to reduce uncertainty for decision-makers by a financially valuable increment, while quantifying how much uncertainty remains. You will learn to calculate and apply to real-world examples the most important uncertainty measures used in business, including classification error rates, entropy of information, and confidence intervals for linear regression. All the data you need is provided within the course, all assignments are designed to be done in MS Excel, and you will learn enough Excel to complete all assignments. The course will give you enough practice with Excel to become fluent in its most commonly used business functions, and you’ll be ready to learn any other Excel functionality you might need in the future (module 1). The course does not cover Visual Basic or Pivot Tables and you will not need them to complete the assignments. All advanced concepts are demonstrated in individual Excel spreadsheet templates that you can use to answer relevant questions. You will emerge with substantial vocabulary and practical knowledge of how to apply business data analysis methods based on binary classification (module 2), information theory and entropy measures (module 3), and linear regression (module 4 and 5), all using no software tools more complex than Excel.

Outline

About This Course
About This Specialization
Introduction to Mastering Data Analysis in Excel
Specialization Overview
Course Overview

Excel Essentials for Beginners
Introduction to Using Excel in this Course
Basic Excel Vocabulary; Intro to Charting
Arithmetic in Excel
Functions on Individual Cells
Functions on a Set of Numbers
Functions on Ordered Pairs of Data
Sorting Data in Excel
Introduction to the Solver Plug-in
Tips for Success
Excel Essentials Practice
Excel Essentials

Binary Classification
Introduction to Binary Classification
Bombers and Seagulls: Confusion Matrix
Costs Determine Optimal Threshold
Calculating Positive and Negative Predictive Values
How to Calculate the Area Under the ROC Curve
Binary Classification with More than One Input Variable
Tips for Success
Binary Classification (practice)
Binary Classification (graded)

Information Measures
Quantifying the Informational Edge
Probability and Entropy
Entropy of a Guessing Game
Dependence and Mutual Information
The Monty Hall Problem
Learning from One Coin Toss, Part 1
Learning From One Coin Toss, Part 2
Tips for Success
Using the Information Gain Calculator Spreadsheet (practice)
Information Measures (graded)

Linear Regression
Introducing the Gaussian
Introduction to Standardization
Standard Normal Probability Distribution in Excel
Calculating Probabilities from Z-scores
Central Limit Theorem
Algebra with Gaussians
Markowitz Portfolio Optimization
Standardizing x and y Coordinates for Linear Regression
Standardization Simplifies Linear Regression
Modeling Error in Linear Regression
Information Gain from Linear Regression
Tips for Success
The Gaussian (practice)
Regression Models and PIG (practice)
Parametric Models for Regression (graded)

Additional Skills for Model Building
Describing Histograms and Probability Distributions Functions
Some Important and Frequently Encountered PDFs
Linear Regression with More than One Input Variable
Understanding Why Over-fitting Happens
AUC Calculator Explanation and Spreadsheet
Probability, AUC, and Excel Linest Function

Final Course Project
Final Project Information: Part 1
Final Project Information: Part 2
Final Project Information
Summary of Learning Points for Final Project: Quiz 1
Summary of Learning Points for Final Project: Quiz 2
Part 1: Building your Own Binary Classification Model
Part 2: Should the Bank Buy Third-Party Credit Information?
Part 3: Comparing the Information Gain of Alternative Data and Models
Part 4: Modeling Profitability Instead of Default

Summary of User Reviews

Discover the power of Analytics in Excel with this comprehensive course. Students have praised the course for its thoroughness and practical approach. One key aspect that many users appreciated was the instructor's ability to break down complex topics into easily understandable concepts.

Pros from User Reviews

Comprehensive and practical approach to learning analytics in Excel
Instructor breaks down complex topics into easily understandable concepts
Real-world examples and hands-on exercises enhance learning experience
Great for beginners and professionals alike
Flexible scheduling and self-paced learning

Cons from User Reviews

Some users found the course to be too basic
Limited interaction with instructor and peers
Not suitable for advanced Excel users looking for more complex analytics techniques
No certification or accreditation offered
Some technical issues reported with the online platform

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