Course Outline

Module 1: Logical and Reliable Data Analysis, Descriptive Statistics, and Pivot Tables

• Importing data into Excel
• Best practice when analyzing data
• Analyzing and representing coded data
• Descriptive statistics and their real meanings
• Performing a frequency analysis
• The use of pivot tables and pivot charts
• Noisy and incomplete data, statistical significance, and dealing with outliers
• Case Study: Analyzing customer satisfaction survey data to identify key areas for improvement using descriptive statistics and pivot tables.

Module 2: Data Mode Shape Analysis

• Plotting data against time
• Generating data mode shapes
• Fitting curves to data
• Correlating mode shape to time-based events
• Interpreting time series analyses
• Moving average calculations
• Case Study: Analyzing production data to detect seasonal patterns and anomalies in a manufacturing process.

Module 3: Scenario Analysis and Interactive Spreadsheets

• Representing analytical problems as multi-input, single-output (MISO) systems
• Deterministic systems analysis
• What if and visual scenario analysis
• Dynamic / interactive spreadsheets and the use of forms control
• Moving window, conditional and adaptive calculations
• Measuring the sensitivity of calculated variables
• Practical Component: Developing an interactive financial model to evaluate the impact of different market conditions on business profitability.

Module 4: Regression Analysis and Correlation

• Equations of curves
• The prediction of future behavior using data shape – regression analysis
• Linear, polynomial, exponential and power curve fits
• The dangers of over-fitting
• Data end effects
• Goodness of fit (sum of square error – SSE) and R2
• Evaluating equations, solving equations, and using Solver
• Correlation and causality
• Case Study: Conducting a regression analysis to predict sales based on advertising spend and market trends, including identifying potential overfitting issues.

Module 5: Data Driven Methods and Analysis of Variance

• Non-deterministic system
• Data driven methods
• One step ahead future prediction using data science (multivariate correlation)
• Single factor analysis of variance (ANOVA)
• Two factor analysis of variance
• A demonstration of artificial intelligence – the travelling salesman problem
• Practical Component: Applying ANOVA to evaluate the effectiveness of different marketing strategies on customer acquisition rates.