Mastering Data Analysis in Excel
€0, aangeboden door Coursera
About this course: 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 and 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 - and you will learn metrics to quantify for yourself the exact reduction in uncertainty each can offer. 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 – the standard way of representing forecasts in data science. In addition, 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-analytics offerings, this course empowers you to understand and 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 R or software programming. 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. If you already know Excel, you can skip that part. 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 analytics 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; and 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 “eâ€); and 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" – please refer to it to decide if the difficulty level of this course seems right for you.
Created by:Â Â Â Duke University
Taught by:Â Â Â Â Jana Schaich Borg, Post-doctoral Fellow
Psychiatry and Behavioral Sciences
Taught by:Â Â Â Â Daniel Egger, Executive in Residence and Director, Center for Quantitative Modeling
Pratt School of Engineering, Duke University
Basic Info
Course 2 of 5 in the Excel to MySQL: Analytic Techniques for Business Specialization.
Commitment
6 weeks, 3 - 5 hours per week
Language
English
How To Pass
Pass all graded assignments to complete the course.
User Ratings
4.2 stars
Average User Rating 4.2See all 340 reviews
Course 2 of Specialization
Turn Data Into Value. Drive business process change by identifying & analyzing key metrics in 4 industry-relevant courses.
Excel to MySQL: Analytic Techniques for Business
Duke University
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Coursework
Each course is like an interactive textbook, featuring pre-recorded videos, quizzes and projects.
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About Duke University
Duke University has about 13,000 undergraduate and graduate students and a world-class faculty helping to expand the frontiers of knowledge. The university has a strong commitment to applying knowledge in service to society, both near its North Carolina campus and around the world.
Syllabus
WEEK 1
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 appli...Â
2 videos, 3 readings
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Excel Essentials for Beginners
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 E...Â
8 videos, 2 readings, 1 reading
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Graded: Excel Essentials
WEEK 2
Binary Classification
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 measu...Â
6 videos, 2 readings, 1 reading
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Graded: Binary Classification (graded)
WEEK 3
Information Measures
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 aggreg...Â
7 videos, 2 readings, 1 reading
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Graded: Information Measures (graded)
WEEK 4
Linear Regression
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 esti...Â
11 videos, 2 readings, 2 readings
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Graded: Parametric Models for Regression
WEEK 5
Additional Skills for Model Building
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 ...Â
4 videos, 2 readings
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Graded: Probability, AUC, and Excel Linest Function
WEEK 6
Final Course Project
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 po...Â
2 videos, 4 readings
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Graded: Part 1: Building your Own Binary Classification Model