Data Analysis Skills

Introduction

Do you work in a role that involves working with business data, interpreting or producing reports and recommendations for colleagues, managers and executives?

If so, this seminar offers a great opportunity to develop your ability to perform operational analysis, choose the most appropriate approach to identify improvement opportunities in your organisation and apply statistical methods when analysing the business.

Pre-Requisites

There are no pre-requisites.

Who should attend?

This workshop is designed for those who work in a data analyst role or operations.

What will you learn?

• Use data driven analysis to identify performance opportunities
• Become comfortable using basic operational formulas
• Prepare methods of analysis for performance
• Apply statistical methods when analysing business opportunities or challenges
• Select and apply formulas for operational analysis
• Produce executive summaries that support good decision making

Course Outline

Introductions & Agenda

• Health and safety (evacuation procedure)
• Facilitator introduction
• Participant introductions
• Seminar agenda

Data Analysis & Synthesis

• An overview of statistics applied in organizations
• Defining the problem or opportunity
• Evidence/fact driven analysis
• Chunking the data (working at the right level of detail)
• Cognitive bias, how it skews data analysis and produces bad outcomes
• Using inductive and deductive approaches to validate data
• Building valid hypothetical models , based on sound analysis

Developing and Testing Hypotheses

• Defining tests for your hypotheses
• Using control groups as a baseline
• Group selection, the risk of a skewed sample
• Selecting a reasonable sample from the data population
• Validating survey data

Working with Decision Variables and Optimising Solutions

• Understanding your constraints
• Identifying variables that you can control
• Developing realistic assumptions
• Optimising your worksheet
• Managing negatively linked variables

Statistical and Other Tools

• The probability based approach
• Common traps in using probability
• Defining the ‘middle’ of the data, mean, mode, media
• Calculating confidence (standard deviation, the three sigma rule)
• Different types of probability curves
• Sensitivity analysis
• Correlation
• Cause and effect analysis
• Regression analysis (simple, multi-variate, step by step and hierarchical)
• Decision trees
• Expected monetary value calculations
• Monte Carlo analysis
• Heuristics

Using Graphics to Present Data

• When to use charts to enhance comprehension
• The basics of visual literacy, bringing the data to life
• The psychology of charts
• The potential traps of using charts
• Different types of charts, for different data sets, bar charts, histograms, histographs, tornado diagrams, radar diagrams, area diagrams, grouped columns, stacked columns, pie charts
• The crucial important of aligning chart and title
• Using non-numeric charts to present data
• Recap of course content
• Questions and answers
• Ebbinghaus (memory reinforcing) final exercise