Data Analytic Principles/Habits

Name: Data Analytic Principles/Habits
Rating: 4.5 (50 reviews)

A set of highly effective and easy to enforce habits that professionalize your analytic work

Created byEddie Jay

Last updated 7/2024

English

What you'll learn

Recognize the importance of a methodical approach to doing analytic work
Learn good habits for doing analytic work
Pick up universally applicable, practical, easy to enforce habits

Course content

6 sections • 27 lectures • 1h 12m total length

Course promotion video2:14
Introduction to the Habits for Excellence in Analysis course.
Course introduction1:50
Introduction to the course chapters and format

Filing System2:34
Principles of a good filing system, including folders, file layout and text/colour formats
Filing System example4:34
This example video illustrates how the principles in the previous video is applied in the Chocolate Factory analysis. Note the supplementary material.
Input Items2:57
Principles on working with input items
What affects quality of input items?
Input Items checking example2:44
Be careful when using input items such as data and assumptions. Make sure they are relevant and correct.
Model structure2:36
Principles on designing the model structure
What personality attribute makes a great data analyst?
Model Structure example2:06
Illustrates the application of model structure principles in our Chocolate Factory example.

Building principle2:09
Principles for building the formulas and performing the calculations

Attached are two PDF print outs containing the

Most Useful Keyboard shortcuts

Most Useful Formulas in Excel
Building "Dos"2:43
Recommended steps for building your analysis
Building "Don'ts"3:14
Testing and Checking3:33
Principles on doing testing and checking of your analysis
What can be said about testing and checking?
Testing and Checking example3:25
Example illustrating testing and checking through our Chocolate factor example
Documentation1:39
Detailed documentation is critical to your work
Documentation example2:43
Illustration of principles of documenting your work
Why is documentation important?

Results1:57
Principles on generating great and useful results
Results example2:59
Illustrates how principles relating to results were applied for the Chocolate Factory example
Presentation1:21
Principles on making great presentations of the results
Presentation example3:20
Illustration of how the results could be presented from our Chocolate factory example
Refinements0:37
No one gets it right the first time! Iterative refinements are required to identify issues and improve your analysis over time.

5 key elements of health insurance products2:58
US Health System series - Health Insurance Products
The 5 key elements of health plan design, that help you see and understand the whys behind each different design
View the complete course + others here ==> https://www.udemy.com/user/yidingjiang/
Intro to the Payors lines of business (health insurers)3:03
US Health System series - Payor lines of business
View the complete course + others here ==> https://www.udemy.com/user/yidingjiang/
Machine Learning No Coding Required! - Decision Trees3:20
Learn what are decision trees in ML
View the complete course + others here ==> https://www.udemy.com/user/yidingjiang/
Health data 101 - Insurance Claims2:09
Health insurance claims data are a major source of health care data. As long as the the medical services are paid by health insurers, you will see the claims. This is a relative well structured and complete source, although clinical detail can be lacking at times.
View the complete course + others here ==> https://www.udemy.com/user/yidingjiang/

Requirements

Some basic understanding of spreadsheet operations
A basic understanding of what doing analytic work entails
Open mind to change and practice your work habits

Description

Do you want to turn your analytic work from mediocre to great?

Do you want to shine at the workplace by demonstrating a superior, professional analytic ability?

Then this course is for you... Read on please.

Over 15 years, I identified a number of steps that elevate the quality of data analytic work. When done habitually, these steps will professionalize the analysis, making it more error resistant, flexible and effective in answering the “question”. This content is what I wish someone taught me when I first started working as a data analyst.

Course content:

Context
Preparations (filing system, inputs, model structure)
Building (calculations, testing/checking, documentation)
Output (results, presentation, iterations)

I have distilled the content into easy to remember sections to ensure that you gain a long lasting value from this course. Each section comes with supplemental material including poster for easy reminder and course reading that for reinforcements.

In Summary

Goal - Help you learn and adopt good habits when doing analytic work.

Format - 40 mins of videos that introduce the principles and illustrate the principles through an example. Poster and Reading Materials for each section serves as cheat sheets for future reference and enforcement.

Target Audience - Beginner level data analysts or Students who are about to enter the workforce (data, statistical, financial analyses)

This course is not a training program on how to use MS Excel. We use Excel to illustrate the principles; not hours on hours of lectures that are hard to remember. 40 minutes is all you need.

Who this course is for:

Beginner data/financial analysts
Anyone doing regular spreadsheet work
Students who are about to enter the workforce

Data Analytic Principles/Habits

What you'll learn

Explore related topics

Course content

Welcome and introduction2 lectures • 4min

Understanding the Context3 lectures • 10min

Preparations6 lectures • 18min

Building7 lectures • 19min

Output5 lectures • 10min

Bonus Sections4 lectures • 12min

Requirements

Description

Who this course is for: