EDA & SPC
Learning Program

Exploratory Data Analysis & Statistical Process Control
This program consists of three courses structured along a logical learning program, designed to provide self-paced, flexible, and practical training in data analysis for engineers and technical professionals. 
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3 Courses

9 lessons

Intermediate Level

Recommended

180 days to complete

60 days per course

Flexible Schedule

Learn at your own pace
About
Courses
Enrollment
Certification
Meet the Trainer
Requirements
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COURSE 1

Data Analysis Foundations

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COURSE 2

Statistical Metrics & Normal Distribution

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COURSE 3

Statistical Process Control & Capability Analysis

  • Language: English
  • 9 lessons
  • Weekly live session of 1 hour with the trainer.
  • Case studies across multiple industrial sectors, including Automotive, Moulds & Plastics, Pharmaceutical, Food, Logistics, Chemical, and more.
  • Excel tools with examples of how to plot histogram, normality plot, scatterplot, box plot and others on excel, how to get the statistic metrics, how to implement SPC with the control charts and capability studies, among many others.  
  • Throughout the course, you can book a 1:1 session with the trainer at any time for an additional fee.

Get the full learning program and save 16%

COURSE DURATION

60 DAYS

PER COURSE
INDIVIDUAL COURSE PRICING
Data Analysis Foundations: Roadmap, Data Quality, Variable Types, and Charts | 49 EUR

Statistical Metrics & Normal Distribution | 89 EUR

Statistical Process Control and Capability Analysis | 159 EUR
PROGRAM DISCOUNT

16%

- 48 eur
Earn certificates for both the full learning program and each individual course by completing a final assessment. Certificates are issued once all questions are answered correctly. Along the way, each lesson includes a short knowledge check with unlimited attempts to help you practice what you’ve learned.
SIA certificates are proof of your expertise:
  • Demonstrates mastery of practical, real-world engineering skills
  • Help you stand out to employers, clients, and project teams
  • Can be shared on LinkedIn, included in your CV, or used for professional development credits
  • Validate both technical competence and continuous learning
Training accredited by DGERT.
Created by

Cristina Barros

Cristina Barros will be available to support you throughout the course, answering questions via the course chat and offering optional 1:1 sessions. She brings more than 26 years of experience in industrial optimization, quality engineering, and statistical analysis.

Hardware Requirements

  • PC, Mac, or mobile device
  • Audio speakers or headset
  • Reliable high-speed internet connection

Supported Browsers

SIA courses can be accessed on any device that supports the browsers listed below, including smartphones and tablets.
  • Windows: Edge, Google and Mozilla.
  • Mac: Safari, Google Chrome, Mozilla.
  • Mobile Devices: Safari, Google Chrome.

Requires Browser Settings

To ensure proper access to SIA e-learning content, please verify that:
  • JavaScript is enabled
  • Font downloads are enabled to display content correctly
  • Compatibility View is disabled when using Internet Explorer
  • Pop-up blockers, spam filters, and corporate firewalls are configured to allow access to the platform
  • Third-party browser toolbars (e.g., Bing, Yahoo, Google) are disabled, as they may block course content

About this learning program

You will begin by building a solid foundation in data analysis methodology, data quality, variable classification, and visualization. The program then advances to statistical metrics and distribution analysis, enabling you to interpret data behaviour and assess probability and performance. Finally, you will apply these concepts to Statistical Process Control (SPC) and Capability Analysis, supporting robust, data-driven process evaluation and improvement. 

Throughout the program, you will work with real-world datasets and hands-on exercises to ensure immediate relevance to daily engineering and analytical activities. Microsoft Excel is used as the primary tool for data organization, visualization, and statistical analysis, allowing you to directly apply the concepts in a widely used professional environment. 

Learning Objectives

  • Apply the data analysis roadmap effectively.
  • Identify the type of variable under study. 
  • Apply best practices in data cleaning, organization, and structuring for analysis. 
  • Perform exploratory data analysis (EDA) for different types of variables. 
  • Understand the normal distribution and its importance in data interpretation. 
  • Select and apply appropriate visualization and quality tools to support decision-making and process improvement. 
  • Use Excel to analyse the proposed case studies. 

Prefer to take individual courses?

You can also enroll in each course separately if you are interested in a specific topic or skill.
This option is recommended if you:
  • Already have experience in part of the subject
  • Only need a specific competency
  • Prefer shorter, standalone learning
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Data Analysis Foundations

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Statistical Metrics & Normal Distribution

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Statistical Process Control & Capability Analysis

Recommended Books & Standards

Introduction to Statistical Quality Control

Montgomery, D.C. (2020)

Statistical Process Control

Oakland, J. S. (2014)

Six Sigma Distributions Modeling

Sleeper, A. (2007)

Implementing Six Sigma: Smarter Solutions® Using Statistical Methods

Breyfogle III, F. W. (2003)

Applied Statistics for the Six Sigma Green Belt

Gupta, B. C. & Walker, H. F. (2005)

Statistical Methods for Six Sigma

Anand M. Joglekar (2003)

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