Introduction to Data Analytics with Microsoft Excel

Introduction to Data Analytics with Microsoft Excel

Requirements

• Microsoft Office 365 or Excel 2010 - 2019

• Mac users Pivot Visuals may look slightly different to the examples shown

• Basic experience with Excel functionality is a bonus but not required
  

Description

Welcome 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 Scientist

Course Outline

The 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 Analytics

• Why Do We Need It in this new world

• Thinking about Data, how it works in the lad v how it works in the wild

• Qualitative v Quantitative data and their importance
  

Finding Your Data

• How to find Sources of Data and what they contain

• Reviewing the Dataset and getting hands on
  

Analysing Your Data

• Mean, Modes, Median and Range

• Normal and Non normal Data and its impacts to predictability

• What is an Outlier in our data and how do we remove

• Distribution and Histograms and why they are important

• Standard Deviation and Relative Standard Deviation, why variance is the enemy

• What are Run and Control charts and what do they tell us?

Working With Pivot Tables

• How the Pivot Builder Works

• Setting Our Headers

• Working with calculated fields

• Sorting and Filtering

• Transforming Data with Pivot Tables

Data Engineering

• How to create new, insightful datasets

• The importance of balanced data

• Looking at Quality, Cost and Delivery together

Start Telling Our Analytical Story

• What is your data telling?

• Ask Yourself Questions

• Transforming Data into Information

Visualizing Your Data

• Levels of Reporting

• What Chart to Use

• Does Color Matter

• Let's Visualize Some Data

Presenting Your Data

• Bringing The Story Together with a Narrative

Practical Activities

We will cover the following practical activities in detail through this course:

• Practical Example 1 - Mean, Mode, Median, Range & Normality

• Practical Example 2 - Distribution and Histograms

• Practical Example 3 - Standard Deviation and Relative Standard Deviation

• Practical Example 4 - A Little Data Engineering

• Practical Example 5 - Creating a Run Chart

• Practical Example 6 - Create a Control Chart

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

• Practical Example 8 - Transforming Data

• Practical Example 9 - Calculated Fields, Sorting and Filtering

• Practical Example 10 - Lets Engineer Some QCD Data

• Practical Example 11 - Lets Answer Our Analytical Questions with Pivots

• Practical Example 12 - Visualizing Our Data

• Practical Example 13 - Lets Pull our Strategic Level Analysis Together

• Practical Example 14 - Lets Pull our Tactical Level Analysis Together

• Practical Example 15 - Lets Pull our Operational Level Analysis Together

• Practical Example 16 - Lets Add Our Key Findings

• Practical Example 17 - Lets Add Our Recommendations
  
  

Who this course is for:

• Anyone who works with Excel on a regular basis and wants to supercharge their skills

• Excel users who have basic skills but would like to become more proficient in data exploration and analysis

• Students looking for a comprehensive, engaging, and highly interactive approach to training

• Anyone looking to pursue a career in data analysis or business intelligence

Master data analysis through Excel with advanced hands on practical training

Url: View Details

What you will 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

Rating: 5

Level: All Levels

Duration: 3.5 hours

Instructor: Wayne Bowden