Open Source Intelligence Summit 2026
Ryan Paul Lafler, Kirk Paul Lafler
Wednesday, March 18, 2026
10:00 am - 3:00 pm PT
Virtual 5-Hour Summit
$29.95
Workshop Tags
Open Source Analytics, Data Science, Python, R, Machine Learning, AI, Data Visualization, Analytics Workflows, Professional Development
Workshop Description
Open-Source Tools, Intelligent Analytics, Real-World Impact
Attend the Open Source Intelligence Summit 2026 on Wednesday, March 18, 2026, for a 5-hour virtual experience delivering practical, real-world open source analytics and artificial intelligence (AI) insights for data scientists, analytics professionals, analysts, programmers, and decision-makers.
The Open Source Intelligence Summit 2026 brings together industry experts to explore how Python and R are transforming data science, AI, and analytics workflows across industries. Sessions emphasize coding techniques, best practices, and real-world use cases, showing how open source software tools can be leveraged to build scalable, transparent, and impactful analytics solutions.
Attendees will gain a deeper understanding of modern AI-enabled analytics, open source ecosystems, and how to move confidently from raw data to actionable insights – whether working independently, in teams, or within enterprise environments.
Sessions and Schedule
The Open Source Analytics Ecosystem: Why Python and R Power Modern Data Science
Open source technologies have become the backbone of modern analytics and artificial intelligence. This keynote sets the stage for the summit by exploring how Python and R emerged as the dominant languages for data science, machine learning, and AI. Attendees will gain a high-level understanding of the open source ecosystem, including core libraries, community-driven innovation, and how open tools enable transparency, scalability, and reproducibility across industries. This session highlights how organizations successfully adopt open source analytics to accelerate insight and innovation.
Instructors: Kirk Paul Lafler and Ryan Paul Lafler
Intended Audience: Data Scientists, Statistical Programmers, Data Analysts, Statisticians, Data Engineers, and Others
Session Time: 10:00 – 10:25 AM
Length: 25 minutes
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From Raw Data to Ready Data: Practical Data Wrangling with Python and R
High-quality analytics begins with high-quality data. This session focuses on real-world data preparation techniques using Python and R, including cleaning, transforming, validating, and reshaping data for analysis. Through practical examples, attendees will learn how to handle missing values, outliers, inconsistent formats, and messy real-world datasets. Best practices for building efficient, reusable data preparation workflows are emphasized, helping participants spend less time fixing data and more time generating insights.
Instructor: Kirk Paul Lafler
Intended Audience: Data Scientists, Statistical Programmers, Data Analysts, Statisticians, Data Engineers, and Others
Session Time: 10:30 – 10:55 AM
Length: 25 minutes
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Exploratory Data Analysis That Drives Insight
Before modeling or AI can deliver value, analysts must understand their data. This session demonstrates how exploratory data analysis (EDA) using Python and R uncovers patterns, trends, and anomalies that guide analytical decisions. Attendees will learn how to use summary statistics, visual exploration, and domain-driven questions to form hypotheses and identify opportunities for deeper analysis. The session emphasizes EDA as a critical bridge between raw data and advanced analytics.
Instructor: Kirk Paul Lafler
Intended Audience: Data Scientists, Statistical Programmers, Data Analysts, Statisticians, Data Engineers, and Others
Session Time: 11:00 – 11:25 AM
Length: 25 minutes
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Applied Machine Learning with Open Source Tools
Machine learning is most powerful when applied thoughtfully to real-world problems. This session introduces practical machine learning workflows using open source libraries in Python and R. Topics include selecting appropriate algorithms, preparing data for modeling, training and evaluating models, and interpreting results. Attendees will leave with a clear understanding of how to move beyond theory and apply machine learning techniques responsibly and effectively within analytics projects.
Instructor: Ryan Paul Lafler
Intended Audience: Data Scientists, Statistical Programmers, Data Analysts, Statisticians, Data Engineers, and Others
Session Time: 11:30 – 11:55 AM
Length: 25 minutes
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AI-Enabled Analytics: Moving from Models to Decisions
Building models is only part of the analytics journey – driving decisions is the real goal. This session explores how AI and advanced analytics can be integrated into decision-making processes using open source tools. Attendees will learn strategies for deploying models, monitoring performance, communicating results, and ensuring ethical and transparent use of AI. Real-world examples demonstrate how organizations turn analytical outputs into measurable business and operational value.
Instructor: Ryan Paul Lafler
Intended Audience: Data Scientists, Statistical Programmers, Data Analysts, Statisticians, Data Engineers, and Others
Session Time: 12:00 – 12:25 PM
Length: 25 minutes
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Visualization and Storytelling with Data
Effective communication is essential for analytics success. This session focuses on data visualization and storytelling techniques using Python and R to transform complex analytical results into clear, compelling narratives. Attendees will learn best practices for choosing the right visualizations, avoiding common pitfalls, and tailoring messages for technical and non-technical audiences. This session emphasizes how strong storytelling bridges the gap between data science and informed decision-making.
Instructors: Kirk Paul Lafler and Ryan Paul Lafler
Intended Audience: Data Scientists, Statistical Programmers, Data Analysts, Statisticians, Data Engineers, and Others
Session Time: 12:30 – 12:55 PM
Length: 25 minutes
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Reproducible, Transparent, and Scalable Analytics with Open Source
As analytics projects grow in complexity, reproducibility and transparency become critical. This session highlights open source best practices for building analytics workflows that are reliable, maintainable, and scalable. Topics include version control, documentation, reproducible research concepts, and collaborative development in Python and R. Attendees will gain practical guidance on designing analytics solutions that stand up to review, reuse, and long-term value.
Instructors: Kirk Paul Lafler and Ryan Paul Lafler
Intended Audience: Data Scientists, Statistical Programmers, Data Analysts, Statisticians, Data Engineers, and Others
Session Time: 1:00 – 1:25 PM
Length: 25 minutes
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From Open Source Skills to Real-World Impact
The closing session brings together key themes from the summit and focuses on next steps for attendees. Participants will learn how to continue developing their data science and AI skills, apply open source tools within their organizations, and stay engaged with the evolving analytics ecosystem. The session reinforces how Python, R, and open source AI empower professionals to move from data to insight – and from insight to impact.
Instructors: Kirk Paul Lafler and Ryan Paul Lafler
Intended Audience: Data Scientists, Statistical Programmers, Data Analysts, Statisticians, Data Engineers, and Others
Session Time: 1:30 – 1:55 PM
Length: 25 minutes
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Questions / Open Discussion
Instructors: Kirk Paul Lafler and Ryan Paul Lafler
Intended Audience: Data Scientists, Statistical Programmers, Data Analysts, Statisticians, Data Engineers, and Others
Session Time: 2:00 – 3:00 PM
Length: 60 minutes
Workshop Summary
The Open Source Intelligence Summit 2026 brings together industry experts to explore how Python and R are transforming data science, AI, and analytics workflows across industries. Sessions emphasize coding techniques, best practices, and real-world use cases, showing how open source software tools can be leveraged to build scalable, transparent, and impactful analytics solutions.
Benefits of Enrolling in this Workshop
At the end of this summit, participants will be able to:
Understand the open source data science landscape, including where Python and R excel
Apply best practices for data cleaning, exploration, and preparation
Build and interpret machine learning models using open source tools
Incorporate AI techniques into analytics workflows responsibly and effectively
Create compelling data visualizationsthat support data-driven decisions
Design reproducible and transparent analytics solutions
Translate analytical results into business and organizational value
Identify next steps for advancing their data science and AI skill sets
Intended Audience
Beginner to Intermediate. No advanced AI or machine learning background required; sessions focus on practical skills, best practices, and applied use cases.
Prerequisites
Basic familiarity with data concepts (tables, variables, summaries)
Introductory exposure to Python or R is helpful but not required
General interest in analytics, data science, or AI-driven decision-making
No advanced programming, machine learning, or AI experience is required. Sessions are designed to be accessible while still providing meaningful value to intermediate practitioners.
About the Trainer(s)
Ryan Paul Lafler is the President, CEO, and Lead Consultant of Premier Analytics Consulting, LLC, a San Diego–based consulting firm specializing in applied AI and machine learning systems, big data engineering, statistical analysis, and developing custom full-stack analytics platforms. He partners with enterprise organizations, public-sector agencies, and research institutions to help clients process, analyze, and model complex data in support of informed decision-making.
Ryan brings experience as a consultant, programmer, full-stack developer, AI/ML engineer, and data scientist. His work focuses on building open-source analytics platforms, scalable big data engineering solutions, rigorous statistical analysis, and applied AI workflows, with a strong emphasis on reliability, validation, and production readiness. He has expertise in cross-industry programming for analytics, AI, and data science using Python, R, SQL/NoSQL, SAS®, and modern JavaScript frameworks, and regularly implements quality controls and validation practices for AI-generated code and automated analytics workflows.
Ryan is also an Adjunct Faculty member in the Big Data Analytics Graduate Program, the Department of Mathematics and Statistics, and the Global Campus at San Diego State University.
He earned a Master of Science in Big Data Analytics (2023), following the successful defense and publication of his thesis, and a Bachelor of Science in Statistics with a Minor in Quantitative Economics, with Distinction (2020), both from San Diego State University.
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Kirk Paul Lafler is a globally recognized data scientist, consultant, educator, programmer, author, and speaker with more than 40 years of experience delivering practical, real-world analytics training. His expertise spans SAS, SQL, Python, databases, big data, AI, machine learning, and cloud technologies. Known for transforming complexity into clarity, Kirk helps learners build confidence and proficiency through hands-on, applied instruction.
As the creator, developer, and educator of numerous courses, workshops, and publications, Kirk empowers professionals, students, and organizations to think critically, solve complex problems, and make data-informed decisions. Whether delivering keynote presentations, training corporate teams, or authoring technical content, he connects people with knowledge that drives meaningful impact. Kirk also serves as Adjunct Faculty for the Department of Mathematics and Statistics at San Diego State University (SDSU) and is the co-developer and educator of the SAS Programming certificate at the University of California San Diego Extension.
Kirk is the author of several books, including PROC SQL: Beyond the Basics Using SAS, Third Edition (SAS Press, 2019), and has written hundreds of papers and articles. He is a frequent invited speaker, educator, and keynote presenter at conferences, symposiums, and professional meetings worldwide, and the recipient of 29 “Best” contributed paper, hands-on workshop (HOW), and poster awards.