Mastering Data Analysis in Excel

Go to Course: https://www.coursera.org/learn/analytics-excel

Introduction

## Course Review: Mastering Data Analysis in Excel ### Overview "Mastering Data Analysis in Excel" on Coursera is an engaging course designed for those interested in diving deep into the world of data analysis without getting lost in the complexities of Excel's advanced features. This course primarily focuses on statistical concepts and methodologies while utilizing Excel as the tool for execution. As a participant, you’ll walk away with invaluable skills tailored to designing and implementing predictive models relevant to business decision-making. ### Course Structure The course is organized into six modules that progressively build your understanding of essential data analysis techniques, with a practical application at its core. 1. **Excel Essentials for Beginners**: This introductory module covers the fundamental functions necessary to navigate Excel effectively. It lays the groundwork for understanding the subsequent detailed analyses you will undertake, ensuring that you feel comfortable with the software while focusing on the mathematical concepts. 2. **Binary Classification**: Here, you will learn how to categorize data into binary outcomes (e.g., accept/reject) using various metrics. Mastering binary classification methods, including calculating the Area Under the Curve (AUC), is crucial for any data analyst focused on optimizing decision-making processes. 3. **Information Measures**: This module introduces you to the concept of entropy as a measure of uncertainty, empowering you to quantify uncertainty associated with predictive modeling. You'll learn how to calculate information gain, which reflects the improvement that a model brings in reducing uncertainty. 4. **Linear Regression**: A focus on linear regression prepares you to understand relationships in your data. You will utilize the Central Limit Theorem (CLT) and learn to derive point estimates and confidence intervals, solidifying your grasp of how linear relationships can help inform business decisions. 5. **Additional Skills for Model Building**: This module wraps practical skills around model creation and evaluation, warning against common pitfalls like overfitting. You'll learn to create optimized models and apply them to real-world datasets, enhancing your problem-solving abilities. 6. **Final Course Project**: The culmination of your learning, the final project requires you to apply everything you've learned throughout the course. You'll take on the role of a business data analyst, developing two distinct predictive models for a bank's credit card applicants. This project comprises quizzes and peer assessments to reinforce your skills and enable collaboration with fellow learners. ### Learning and Skill Development What stands out in this course is its commitment to teaching meaningful data analysis techniques using Excel without getting bogged down in complicated software tools. The course ensures that participants become fluent in core Excel functions while simultaneously grasping crucial analysis concepts, such as uncertainty measures and predictive modeling. While advanced Excel functions like macros and Pivot Tables are excluded, this approach is deliberate, allowing students to focus on foundational skills and methodologies that are applicable across various data analysis environments. ### Ideal Candidates This course is best tailored for beginners to intermediate learners who are eager to gain hands-on experience in data analysis but seek to focus on the mathematical side rather than mastering the intricacies of Excel’s advanced functionalities. Ideal candidates include: - Business professionals aiming to integrate data-driven decision-making into their roles. - Students of economics or business analytics seeking to strengthen their analytical skills. - Individuals preparing for a career in data analysis who wish to build a robust foundation in essential concepts. ### Conclusion and Recommendation I highly recommend "Mastering Data Analysis in Excel" for anyone looking to enhance their data analysis skills critically while becoming adept at using Excel in a business context. The focus on practical applications ensures that by the end of the course, you'll not only understand theoretical aspects but also confidently apply them in real-world scenarios. The final project provides a fantastic opportunity to cement your learning while simulating an authentic analytical environment. Take the step towards mastering data analysis—enroll in "Mastering Data Analysis in Excel" today on Coursera!

Syllabus

About This Course

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, and all assignments are designed to be done in MS Excel. 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 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.

In this module, will explore the essential Excel skills to address typical business situations you may encounter in the future. The Excel vocabulary and functions taught throughout this module make it possible for you to understand the additional explanatory Excel spreadsheets that accompany later videos in this course.

Separating collections into two categories, such as “buy this stock, don’t but that stock” or “target this customer with a special offer, but not that one” is the ultimate goal of most business data-analysis projects. There is a specialized vocabulary of measures for comparing and optimizing the performance of the algorithms used to classify collections into two groups. You will learn how and why to apply these different metrics, including how to calculate the all-important AUC: the area under the Receiver Operating Characteristic (ROC) Curve.

In this module, you will learn how to calculate and apply the vitally useful uncertainty metric known as “entropy.” In contrast to the more familiar “probability” that represents the uncertainty that a single outcome will occur, “entropy” quantifies the aggregate uncertainty of all possible outcomes. The entropy measure provides the framework for accountability in data-analytic work. Entropy gives you the power to quantify the uncertainty of future outcomes relevant to your business twice: using the best-available estimates before you begin a project, and then again after you have built a predictive model. The difference between the two measures is the Information Gain contributed by your work.

The Linear Correlation measure is a much richer metric for evaluating associations than is commonly realized. You can use it to quantify how much a linear model reduces uncertainty. When used to forecast future outcomes, it can be converted into a “point estimate” plus a “confidence interval,” or converted into an information gain measure. You will develop a fluent knowledge of these concepts and the many valuable uses to which linear regression is put in business data analysis. This module also teaches how to use the Central Limit Theorem (CLT) to solve practical problems. The two topics are closely related because regression and the CLT both make use of a special family of probability distributions called “Gaussians.” You will learn everything you need to know to work with Gaussians in these and other contexts.

This module gives you additional valuable concepts and skills related to building high-quality models. As you know, a “model” is a description of a process applied to available data (inputs) that produces an estimate of a future and as yet unknown outcome as output. Very often, models for outputs take the form of a probability distribution. This module covers how to estimate probability distributions from data (a “probability histogram”), and how to describe and generate the most useful probability distributions used by data scientists. It also covers in detail how to develop a binary classification model with parameters optimized to maximize the AUC, and how to apply linear regression models when your input consists of multiple types of data for each event. The module concludes with an explanation of “over-fitting” which is the main reason that apparently good predictive models often fail in real life business settings. We conclude with some tips for how you can avoid over-fitting in you own predictive model for the final project – and in real life.

The final course project is a comprehensive assessment covering all of the course material, and consists of four quizzes and a peer review assignment. For quiz one and quiz two, there are learning points that explain components of the quiz. These learning points will unlock only after you complete the quiz with a passing grade. Before you start, please read through the final project instructions. From past student experience, the final project which includes all the quizzes and peer assessment, takes anywhere from 10-12 hours.