Real-time, authoritative Stata training modules with live instructor interaction, providing a dynamic and engaging learning experience that is structured around an academic learning style with assessments and ongoing feedback. Get in-depth understanding of Stata, its capabilities and methods, while learning a variety of data analysis and statistical approaches that you can use in your business, your research and your studies.
Classes start shortly. Register for all five Core modules or choose from the five core modules to suite your learning needs and timetable. Special discounts apply for the first 15 registrations. Click the links below to learn more and buy your seat today.
| Certification module | Current schedule | Next schedule |
|---|---|---|
| Core 1 - Working Efficiently in Stata | 10 February 2025 | August 2025 |
| Core 2 - Data Fundamentals | 17 March 2025 | September 2025 |
| Core 3 - Programming Foundations | 21 April 2025 | October 2026 |
| Core 4 - Data Structures | 26 May 2025 | November 2026 |
| Core 5 - Graphics | 30 June 2025 | January 2026 |
Learn, Apply, Assess and get certified
The SDAS Certification program is developed with academic, business, government and professional data analysts and researchers in mind. This comprehensive Stata certification is the first of its type worldwide, offering stackable credentials that demonstrate commitment to ongoing skill-building and professional development with quality-assured learning. The certificate program offers a flexible paced mode of delivery with assessable outcomes and face-to-face feedback, while providing you with valuable outcomes that improve your resumes and allows employers to select Stata users that are best for their business needs.
Who should consider obtaining certification
Data is everywhere and Stata is there to help you achieve more with your data. Anyone seeking to demonstrate their expertise or develop the skills in order to prove their data analytics and data research abilities should consider taking this certification program. Consider the program if you are:
The core modules
The modular approach to this certificate program allows users to build proficiency and demonstrate mastery in Stata. The core modules develop overarching software skills that are specific to Stata and are independent of research methodology. These skills can then be applied to any project using any type of analysis or method packaged within Stata. The core modules begin with "Working efficiently in Stata" and then allows users to expand their Stata skills:
The aim this is to develop fundamental skills for boosting productivity when working with Stata by producing research that is accurate, explicit, tractable, well documented and exactly reproducible.
The aim of Core Module 2 - Data Fundamentals is to develop core proficiencies for constructing comprehensive workflow approaches to data management, from the point of data acquisition to data validation, cleaning, documentation and archiving.
The aim of Core Module 3 - Programming Foundations is to develop skills for conducting highly efficient and exactly reproducible data analysis. The tools and approaches discussed in this module target the development of explicit, versatile and parsimonious routines for executing error-free automated operations that minimise discretionary human input and allow for scalability and replicability in larger projects.
The aim of Core Module 4 - Data Structures is to develop deep understanding of how to manage and analyse different sources of variation and data structures.
The aim of Core Module 5 - Graphics is to develop core skills for conducting graphical analysis using Stata. The module begins with an exposition on Stata’s graphics environment, idiosyncrasies and graph syntax, and presents strategies for producing readable, extensible and well-structured graph routines.
By completing assessments at the end of each module, and achieving passing grades, registrants will receive a certificate of proficiency in that module. Successful completion of all five core modules will result in the designation of Certified Stata Analyst (CSA: An SDAS Program).
The methods modules
Methods Modules provide self-contained training for specific classes of research methods packaged within Stata. They provide training on background theory, motivation, suitability to research questions, implementation using Stata, and discussion of method-specific requirements including data structures, settings and underlying assumptions.
All Methods Modules assume prior knowledge of "at a minimum" Core Module 1. Depending on the type of Methods Module, prior knowledge of Core Modules 2, 3 or 4 may also be required. Prior knowledge of Core Module 5 is not required.
A fundamental step in the analysis of all data is the transformation of variation into a more manageable and interpretative form. The aim of this module is to develop approaches for reducing skewness, containing explosive variation, and transforming multiplicative effects into additive effects by approximating normality and linearity, including the analysis of distributional form.
Extreme values are anomalous data points, representing extremely large or extremely small values that exert a disproportional influence over the analysis of other data points in the sample, and their inclusion or exclusion from the sample would bear a disproportional effect on inference. This module presents methods for identifying, classifying, analysing and treating extreme values.
Smoothing involves the collection of non-parametric and semi-parametric approaches that are designed to help uncover trends, tendencies and relationships from data with unknown functional form. Smoothing relies on visual analysis and the tuning of hyper-parameters to extract signals from noisy data where patterns are not readily discernible.
Time-series filtering is relevant for time-series data and refers to the extraction of systematic signals from a noisy time series. Filtering involves methods for the decomposition of a time series into periodic or aperiodic low frequency, mid frequency and high frequency signals.
Data visualisation is the graphical representation of information extracted from data. This module presents data visualisation as an information system using graphics theory for constructing complex multidimensional graphs that can encode many signals. The module presents advanced forms of visualisation and aims to develop skills for creating bespoke data visualisation solutions.
Causal analysis refers to the identification and effect measurement of a cause-and-effect relationship, particularly how a treatment or an intervention may cause a measurable impact on an outcome variable. This class of methods is relevant for observational data and aims to produce inferences that could be considered to be ‘as-good’ as those produced by a randomised control treatment experiment.
Panel data refers to the systematically repeated observations of several individual units over time. Observations could also be repeated both over time and across clusters giving rising to hierarchical crossed panel data. This module presents methods for estimating models relying on panel data.
Monte Carlo simulation describes the process of repeated random sampling for imitating a real situation under controlled probabilistic conditions. Monte Carlo simulation can be used to evaluate complex formulations that are characterised by significant uncertainty, such as modelling extreme risk and understanding the performance of different statistical estimators.
Survival analysis is used to analyse time-to-event data, where the focus is on estimating the time until an event of interest, such as death, failure, or relapse, occurs. It can also account for censored data, and can estimate the proportion of a population that survives past a certain time and of those that survive at what rate will they fail. Depending on the application, survival analysis has also been known as reliability analysis, event history analysis or duration analysis.
Meta-analysis combines results from multiple studies to provide a more precise estimates of an overall effect size or relationship of interest. It systematically synthesises data from multiple sources and exploits variability between individual studies. By aggregating findings, meta-analysis enhances the power and reliability of conclusions, offering a comprehensive understanding of a research question across different contexts and samples.
Randomised controlled trials (RCT) assess the causal effect of an intervention by randomly assigning participants into treatment and control groups. Randomization reduces selection bias and ensures that any differences in outcomes between the groups can be attributed to the intervention. RCTs require a priori power sample analysis to determine the minimum sample size required to detect a statistically significant effect of the intervention.
Instructor
SDAS has worked closely with leading Stata expert Demetris Christodoulou to develop the SDAS certification program. The program brings together Demetris’ two-decades of experience training (more than a thousand research professionals and executives in Stata) and his practical experience using Stata for published research. Demetris has been consulted by various industry and government agencies to up-skill their research potential in the use of Stata. He has published research articles and Stata Tips in the Stata Journal, wrote a chapter for "Thirty Years with Stata: A Retrospective", and has a forthcoming Stata Press textbook on Visual Data Analytics. Demetris maintains www.graphworkflow.com that demonstrates his structural approach for crafting truthful data visualisation solutions, and has previous published an online textbook on Stata and the Management of Financial Data that was used by thousands of subscribed researchers from 75 countries.
Register today to learn more
If you are ready to take your Stata experience to the next level then register today to show your clients, your superiors and your data that you are in control of your data in Stata.
Call 02 6247 0177 (AU) or 09 889 2231 (NZ) to register your interest.
For terms and conditions of the SDAS Certified Stata Analyst program please see the terms and conditions page.
