Certified Data Analysis Professional

Introduction 

The Certified Data Analysis Professional is a first level, hands-on training course aimed at equipping you with the necessary concepts and tools needed to perform basic statistical and analytics reporting activities, in order to generate value out of the existing data.

The course will provide you with the knowledge required for understanding distinct methods used in the interpretation of statistical data. Also, by attending this certification program, you will be able to understand the basic methodology used in statistical interpretation of quantitative data and become proficient in using key Microsoft Excel features, histograms and Pareto Charts.

The course presents an overview of the basic data analysis concepts, such as data accuracy, preparation and comparability. Furthermore, the course provides both a general understanding upon basic analysis tools, such as mean, median and mode, as well as advanced operations, such as hypothesis testing or tentative sensitivity analysis.

5 Benefits 

• Improve the organization’s decision making process by gaining knowledge on data analysis and interpretation;

• Obtain the most relevant data you need by setting up a customized data analysis process;

• Achieve the management’s buy-in, by understanding the utility of implementing customized data analysis methodology in daily business activities;

• Provide a logical framework for understanding data analysis instruments;

• Obtain premium recognition as a Certified Data Analysis Professional, by completing a unique learning program.

Learning objectives 

• 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.

Evaluation

• Certificate of Completion (soft copy): after completing pre-course activities and passing the Certification Exam;
• Certificate of Attendance (hard copy): after participating at the 3 days of on-site training course;
• Certified Data Analysis Professional diploma (hard copy): after successfully completing all of the 3 stages of the learning experience.

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;

Review and Certification Exam

• Course review;
• Certification exam.