• Develop a hands-on, practical overview of data analysis and connected topics;
• Integrate statistical concepts and analysis tools that are widely used in corporate analytics environments;
• Analyze examples of practical applications for statistical methods used in solving real-life business issues;
• Acquire mastery of basic MS Excel and statistical techniques though practical examples;
• Solve complex problems, using intermediate and advanced Excel techniques

Day 1 – Understanding data analysis

Course context

• Introduction of the participants;
• Expectations setting;
• Learning objectives formulation;
• Course agenda presentation.

Data Analysis – The Basics

• Definitions and utility of data analysis;
• Data analysis process;
• Realignment based on analysis;
• Governance of data analysis.

Data quality

• Data accuracy;
• Logical inconsistencies;
• Data sampling errors;
• Data comparability;
• Data completeness;
• Economic/business interpretation of qualitative data;

Organizing, synthesizing and aggregating data

• Data structure;
• Challenges in aggregating data;
• Data preparation;
• Expert judgement;
• Meta-analysis and evaluation synthesis;
• Normalization of data;

Day 2 – Data analysis

Statistical analysis tools

• Statistical tools: mean, median and mode;
• Trend analysis: variance and standard deviation;
• Hypothesis testing;
• Statistical process control;

Data visualization and pattern detection

• Single, two and multi-dimensional data visualization;
• Level, trend, seasonality and noise in time series data;
• Autocorrelation;

Data comparison

• Analysis using histograms and Pareto Charts;
• Cumulative percentage analysis;
• Rules for interpreting data and formulating conclusions;

Univariate and multivariate analysis

• Differences and complementarities in single and multivariate analyses;
• Techniques used in analyzing single variables;
• Techniques for analyzing relationships between variables (correlation analysis);
• Parametric vs. non-parametric techniques used for analysis;

Regression analysis

• Linear and logistic regression;
• Assumptions and basic models;
• Diagnostic measures and uses;
• Nonlinear models using categorical data and other topics of interest

Day 3 – Advanced data analysis  

Probability and confidence

• Expected values and hypothesis testing;
• Contingency tables – ANOVA;

From exploratory to predictive modelling

• Expected values;
• Confidence limits;
• Risk and uncertainty;
• Type 1 and type 2 errors;
• Tentative sensitivity analysis;

Data dimensionality

• Compensation for small sample sizes;
• Big Data;

Professionals interested in analyzing data

Professionals from different fields, interested in the subject of data analysis, data collection and the data reporting processes will improve their knowledge and competencies in these areas.

Top/middle/lower management professionals

Individuals, such as executives or operational managers, regardless of their field of expertise, will gain the ability and knowledge to better analyze and understand performance measurement data and will be able to maximize the meaning of data provided by KPIs and metrics.

Performance Management experts

For professionals like data analysts, strategy managers, performance management officers, project managers, it is important to develop competencies in analyzing data related to KPIs or metrics. Usually, this particular audience already has a performance measurement system set in place and the Certified Data Analysis Professional training course offers them the opportunity to better organize, analyze, report and understand the meaning of the data provided through specific metrics or KPIs.