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
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
