[2023] Introduction To Data Analytics With Microsoft Excel

Master data analysis through Excel with advanced hands on practical training

What you'll learn

What is Data Analytics & Why is it so Important

Why Do We Need Analytics? What's Changed?

How to Find Appropriate Datasets to work with

How to Analyse you Data

The importance of Mean, Mode, Median and Range of Data

What is the difference between Normal and Non Normal Data

How to create a histogram

How to find and remove outliers

Understand what is a standard deviation and relative standard deviation

Understand the Difference Between a Run and a Control Chart?

The basics of working with pivot tables

Starting to Tell Our Analytical Story

How to Visualize our data

How to Present You data and bring the story together

Requirements

This course is designed for complete beginners, there is no requirements or prerequisites

Description

RequirementsMicrosoft Office 365 or Excel 2010 - 2019Mac users Pivot Visuals may look slightly different to the examples shownBasic experience with Excel functionality is a bonus but not requiredDescriptionWelcome to the world of Data Analytics, voted the sexiest job of the 21st Century.In this expertly crafted course, we will cover a complete introduction to data analytics using Microsoft Excel, you will cover the concepts, the value and practically apply core analytical skills to turn data into insight and present as a story.Look at this as the first step in becoming a fully-fledged Data ScientistCourse OutlineThe course covers each of the following topics in detail, with datasets, templates and 17 practical activities to walk through step by step:What is Data AnalyticsWhy Do We Need It in this new worldThinking about Data, how it works in the lad v how it works in the wildQualitative v Quantitative data and their importanceFinding Your DataHow to find Sources of Data and what they containReviewing the Dataset and getting hands onAnalysing Your DataMean, Modes, Median and RangeNormal and Non normal Data and its impacts to predictabilityWhat is an Outlier in our data and how do we removeDistribution and Histograms and why they are importantStandard Deviation and Relative Standard Deviation, why variance is the enemyWhat are Run and Control charts and what do they tell us?Working With Pivot TablesHow the Pivot Builder WorksSetting Our HeadersWorking with calculated fieldsSorting and FilteringTransforming Data with Pivot TablesData EngineeringHow to create new, insightful datasetsThe importance of balanced dataLooking at Quality, Cost and Delivery togetherStart Telling Our Analytical StoryWhat is your data telling?Ask Yourself QuestionsTransforming Data into InformationVisualizing Your DataLevels of ReportingWhat Chart to UseDoes Color MatterLet's Visualize Some DataPresenting Your DataBringing The Story Together with a NarrativePractical ActivitiesWe will cover the following practical activities in detail through this course:Practical Example 1 - Mean, Mode, Median, Range & NormalityPractical Example 2 - Distribution and HistogramsPractical Example 3 - Standard Deviation and Relative Standard DeviationPractical Example 4 - A Little Data EngineeringPractical Example 5 - Creating a Run ChartPractical Example 6 - Create a Control ChartPractical Example 7 - Create a Summary Pivot of Our Claims DataPractical Example 8 - Transforming DataPractical Example 9 - Calculated Fields, Sorting and FilteringPractical Example 10 - Lets Engineer Some QCD DataPractical Example 11 - Lets Answer Our Analytical Questions with PivotsPractical Example 12 - Visualizing Our DataPractical Example 13 - Lets Pull our Strategic Level Analysis TogetherPractical Example 14 - Lets Pull our Tactical Level Analysis TogetherPractical Example 15 - Lets Pull our Operational Level Analysis TogetherPractical Example 16 - Lets Add Our Key FindingsPractical Example 17 - Lets Add Our RecommendationsWho this course is for:Anyone who works with Excel on a regular basis and wants to supercharge their skillsExcel users who have basic skills but would like to become more proficient in data exploration and analysisStudents looking for a comprehensive, engaging, and highly interactive approach to trainingAnyone looking to pursue a career in data analysis or business intelligence

Overview

Section 1: Course Overview

Lecture 1 What Will This Course Cover?

Lecture 2 How to Get an Office 365 Trial for Free

Lecture 3 Additional Resources

Section 2: Introduction

Lecture 4 Get to Know a little about me and my experience

Section 3: What is Data Analytics?

Lecture 5 Is Analytics Right for Me?

Lecture 6 What is Data Analytics

Lecture 7 Think About Data

Lecture 8 What is Qualitative v Quantitative Data?

Section 4: Finding Your Data

Lecture 9 Finding Your Data

Section 5: Analysing Your Data Part 1

Lecture 10 Mean, Mode, Median & Range of Data

Lecture 11 Normal v Non-Normal Data

Lecture 12 What is an Outlier?

Lecture 13 Data We Will Be Using as Part of This Course

Lecture 14 Data Intimacy

Lecture 15 Practical Example 1 - Mean, Mode, Median, Range & Normality

Lecture 16 Practical Activity

Section 6: Analysing Your Data Part 2

Lecture 17 What will this section cover?

Lecture 18 Distribution and Histograms

Lecture 19 Standard Deviation & RSD

Lecture 20 What is a Run Chart? & Data Engineering

Lecture 21 Practical Example 5 - Creating a Run Chart

Lecture 22 Practical Example 6 - Create a Control Chart

Section 7: Working with Pivot Tables

Lecture 23 What will this section cover?

Lecture 24 Why do we use Pivot tables?

Lecture 25 Practical Example 7 - Create a Summary Pivot of Our Claims Data

Lecture 26 Transforming Data with the Pivot Builder

Lecture 27 Practical Example 8 - Transforming Data

Lecture 28 Using Calculated Fields, Sorting and Filtering

Lecture 29 Practical Example 9 - Calculated Fields, Sorting and Filtering

Lecture 30 Data Engineering 2.0

Lecture 31 Practical Example 10 - Lets Engineer Some QCD Data

Section 8: Start Telling Our Analytical Story

Lecture 32 The Start of Our Story

Lecture 33 Practical Exercise 11 - Part 1 - Quality Performance

Lecture 34 Practical Exercise 11 - Part 2 - Cost Performance

Lecture 35 Practical Exercise 11 - Part 3 - Performance By Day

Lecture 36 Practical Exercise 11 - Part 4 - Paid/Denial Rate Over The Month

Lecture 37 Practical Exercise 11 - Part 5 - Paid/Denial Rate By Day of Week

Lecture 38 Practical Exercise 11 - Part 6 - Paid/Denial Outcome Impact on Quality

Lecture 39 Practical Exercise 11 - Part 7 - Paid/Denial Outcome Impact on Costs

Lecture 40 Practical Exercise 11 - Part 8 - Volume of Claims by Claim Type

Lecture 41 Practical Exercise 11 - Part 9 - Claim Type Impact on Quality

Lecture 42 Practical Exercise 11 - Part 10 - Claim Type Impact on Paid/Denial Rate

Lecture 43 Practical Exercise 11 - Part 11 - Claim Type Impact on Costs

Lecture 44 Practical Exercise 11 - Part 12 - Paid/Denial Rates per Individual

Lecture 45 Practical Exercise 11 - Part 13 - Quality Performance per Individual

Lecture 46 Practical Exercise 11 - Part 14 - Cost Performance per Individual

Lecture 47 Practical Exercise 11 - Part 15 - Individual Delivery Performance By Day of the

Section 9: Data Visualization

Lecture 48 Data Visualization

Lecture 49 Practical Example 12 - Visualizing Our Data

Section 10: Presenting Your Data

Lecture 50 Bringing the Story Together

Lecture 51 Practical Example 13 - Lets Pull our Strategic Level Analysis Together

Lecture 52 Practical Example 14 - Lets Pull our Tactical Level Analysis Together

Lecture 53 Practical Example 15 - Lets Pull our Operational Level Analysis Together

Lecture 54 Practical Example 16 - Lets Add Our Key Findings

Lecture 55 Practical Example 17 - Lets Add Our Recommendations

Section 11: And That's a Wrap

Lecture 56 Course Wrap Up

Complete Beginners