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Trainer Name

Mark Hartford

Skill Area

Financial Services and Financial Technologies

Reviews

4.3 (11 Rating)

Course Requirements

No requirement

Course Description

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 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.

Course Outcomes

This course will give you enough practice with Excel to become fluent in its most commonly used business functions, and will be ready to learn any other Excel functionality in the future

Course Curriculum

1 Introduction to mastering data analysis in excel


2 About this specialisation


1 Introduction to using excel in this course


2 Basic excel vocabulary intro to charting


3 Arithmetic in excel


4 Functions on individual cells


5 Functions on a set of numbers


6 Functions on ordered pairs of data


7 Sorting data in excel


8 Introduction to the solver plug in


1 Introduction to binary classification


2 Bombers and seagulls confusion matrix


3 Costs determine optimal threshold


4 Calculating positive and negative predictive values


5 How to calculate the area under the roc curve


6 Binary classification with more than one imput variable


1 Learning from one coin toss part 1


2 Learning from one coin toss part 2


3 The Monty Hall Problem


4 QUantifying the informational edge


5 Probability and Entropy


6 Entropy of a Guessing Game


7 Dependence and mutual information


1 Standardizing X and Y coordinates for linear regression


2 Introducing the Gaussian


3 Introduction to standardization


4 Standard normal probability distribution in excel


5 Calculating Probabilites from Z scores


6 Central limit theorem


7 Algebra with gaussians


8 Markowitz Portfolio Optimization


9 Standardization Simplifies Linear Regression


10 Modeling Error In Linear Regression


11 Information Gain from Linear Regression


1 Describing histograms and probability distributions functions


2 Some important and frequently encountered pdfs


3 Linear regression with more than one input variable


4 Understanding why over fitting happens


1 Final project information part 1


2 Final project information part 2


Learner Feedback

Analytics Excel

4

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