Conferences and Training
Discover more about the Conferences we're giving training at including the presentations, publications, papers, panels, and talks we've been invited to deliver.
Select from any of the Conferences listed below to learn more about the publications, presentations, panel discussions, hands-on-workshops, and training seminars we're giving at each one.
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Upcoming Conferences and Training
More information about the Conferences, including the Papers, Presentations, Hands-on-Workshops, and Training Seminars offered by the Premier Analytics Team will be provided as each Conference nears.
We hope to meet you at these fantastic Data Science, Machine Learning, and Programming Conferences!
Southeast SAS User Group (SESUG) 2024
Sep. 22 to Sep. 24, 2024
Bethesda, Maryland
Midwest SAS Users Group (MWSUG) 2024
Nov. 16 to Nov. 19, 2024
Waukesha, Wisconsin
Western Users of SAS Software (WUSS) 2024
Sep. 4 to Sep. 6, 2024
Sacramento, California
Machine Learning Conference (ML Conference) 2024
Oct. 7 to Oct. 10, 2024
New York City, New York & Online
105th Annual American Meteorological Society (AMS) 2025
Jan. 12 to Jan. 16, 2025
New Orleans, Lousiana
Conference Proceedings
Select from any of the Conferences listed below to view the Publications, Presentations, Panel Discussions, Hands-on-Workshops, and Training Seminars given by the Premier Analytics Team.
Publications & Papers
Charting Your Organization's Machine Learning Roadmap
Ryan Paul Lafler
2024
Machine Learning is experiencing a golden age of investment, democratization, and accessibility across all sectors and industries encompassing the life sciences, healthcare, financial technology (fintech), consumer marketing, e-commerce, manufacturing, and more. But what exactly is “Machine Learning”? How is it connected to Artificial Intelligence (AI)? And most importantly, how can data scientists, programmers, software engineers, and/or researchers start their endeavors into Machine Learning? This Paper answers these questions, and more, by giving attendees a roadmap to help them navigate the complexities of Machine Learning in an application-oriented guide. This Paper covers the main areas of Machine Learning including supervised, unsupervised, deep learning, and reinforcement learning approaches. Attendees are given a roadmap that starts with linear regression and progressively builds towards more complex and flexible algorithms depending how the data fits each model. In doing so, attendees will learn about Python libraries for Machine Learning; real-world applications of both labeled and unlabeled data; overfitting and underfitting; cross-validation; and the importance of hyperparameter tuning to better fit algorithms to their data.
Harnessing the Power of Python & Google Earth Engine for Extracting, Analyzing, and Visualizing, High Resolution Spatiotemporal Data
Ryan Paul Lafler; Anna Wade
2023
Google Earth Engine is a powerful cloud-based storage platform for accessing publicly available geospatial data from third party sources, including satellite imagery, geophysical, socioeconomic, climatological, census, and meteorological data measured over time for academic-use, personal-use, research, and business applications. Through a combination of beautiful visualizations and easy-to-implement Python code, users will be given the tools to conduct their own analysis with Google Earth Engine. Using the intuitive Python API, along with a suite of visualization packages and map-making libraries available for Python, this paper showcases methods for accessing, querying, extracting, and visualizing Earth Engine's spatiotemporal data to develop interactive maps. Optimized techniques permitting intensive spatiotemporal analysis on large, complex datasets are introduced through server-side operations in Google Earth Engine. By the end of this Paper, users will feel comfortable setting-up, configuring, and linking Earth Engine to Python, become acquainted with commonly-used formats for storing various types of spatial data, understand methods for querying, selecting, uploading, and exporting datasets from Earth Engine, effectively visualize high resolution spatiotemporal data using Python's Geemap package, and be able to conduct analysis using server-side operations to efficiently complete resource-intensive tasks.
Designing Artificial & Convolutional Neural Networks for Classification & Regression Tasks using TensorFlow in Python
Ryan Paul Lafler; Anna Wade
2023
Capable of accepting and mapping complex relationships hidden within structured and unstructured data, Neural Networks are composed from layers of neurons with functions that interact, preserve, and exchange information between each other to develop highly flexible and robust predictive models. Neural Networks are versatile in their applications to real-world problems; capable of regression, classification, and generating entirely new data from existing data sources, Neural Networks are accelerating the breakthroughs in deep learning methodologies. Given the recent advancements in graphical processing unit (GPU) cards, cloud computing, and the availability of interpretable APIs like the Keras interface for TensorFlow, Neural Networks are quickly moving from development to deployment in industries ranging from finance, healthcare, climatology, movies, video streaming, business analytics, and marketing given their versatility in modeling complex problems using structured, semi-structured, and unstructured data. This Paper offers users an intuitive, example-oriented guide to designing basic Artificial Neural Network and Convolutional Neural Network architectures in Python for non-parametric regression and image classification tasks.
Benefits, Challenges, & Opportunities with Open-Source Software (OSS) Integration
Kirk Paul Lafler; Ryan Paul Lafler
2023
The open-source world is alive, well and growing in popularity. This paper highlights the many benefits found with open source software (OSS) including its flexibility, agility, talent attraction, and the collaborative power of community; the trends show that open-source is ubiquitous penetrating many critical technologies we depend on, where more technology companies recognize the importance of the open-source community leading to more initiatives and sponsorships that support open-source creators; the challenges of open source including compatibility vulnerability issues, security limitations, intellectual property issues, warranty issues, and inconsistent developer practices; and the opportunities coming out of the open source community including cloud architecture, open standards, and the collaborative nature of community.
Training Seminars and Workshops
Introduction to Deep Learning: Building Neural Networks with Keras and TensorFlow
Ryan Paul Lafler
2024
Capable of accepting and mapping complex relationships hidden within structured and unstructured data, Neural Networks are a subset of Deep Learning that involves layers of neurons interacting with, transforming, and passing data through successive layers to develop highly flexible and robust predictive models. Whether they’re used for regression, classification, feature representation, forecasting, and/or data generation, Neural Networks are adept at untangling complex, real-world problems. Useful for modeling both structured and unstructured data types, Neural Networks have contributed to recent breakthroughs in industries spanning across finance, healthcare, the life sciences, climatology, video remastering, natural language processing, and business analytics for decision-making, modeling, and generative purposes. Focusing on the Keras API with TensorFlow, this hands-on workshop equips attendees with the skills and knowledge necessary to understand Deep Learning fundamentals in Python. By developing, training, and evaluating their own Artificial and Convolutional Neural Networks, attendees will better understand the potential applications, limitations, and implications of adopting Deep Learning methodologies into their organizations' existing (or new) AI workflows.
Applying Machine Learning Algorithms to Real-World Data with Python: Programming by Example
Ryan Paul Lafler
2024
This virtual half-day course is open to all data scientists, statistical programmers, software engineers, researchers, project managers, and Machine Learning enthusiasts searching for an example-oriented training seminar incorporating supervised and unsupervised Machine Learning algorithms to: ~ Confidently work with both labeled (tagged) and unlabeled (raw) data, ~ Automate classification and regression tasks for Artificial Intelligence workflows, ~ Mine real-world data to uncover relationships between features, ~ Perform clustering and dimensionality reduction on unlabeled data, ~ Optimize, evaluate, and measure the performance of Machine Learning algorithms using Python’s Scikit-Learn library. Several supervised and unsupervised Machine Learning algorithms will be thoroughly discussed, programmed, and fine-tuned using Python, including: ~ Decision Trees for Multi-Class Classification and Non-Linear Regression, ~ Random Forest and Gradient-Boosting Ensemble Methods, ~ Clustering strategies for Observation Segmentation and Anomaly Detection, ~ Dimensionality Reduction (and Manifold Learning) techniques to reduce the complexity of Big Data. By enrolling in this course, attendees receive the documented Python code, their personal copies of the PDF version of the slides, and the confidence to implement supervised and unsupervised Machine Learning algorithms in their organizations.
Mastering Statistical Hypothesis Testing Using R with Comparisons to SAS
Ryan Paul Lafler
2024
This half-day course is open to all aspiring and experienced data scientists, statisticians, bioinformatics scientists, and clinical programmers interested in understanding, designing, and developing parametric and non-parametric statistical hypothesis tests for clinical experiments. This course leverages the R and SAS programming languages to conduct statistical hypothesis testing using real-world examples geared towards the pharmaceutical industry, clinical trials, and the biological and life sciences. Attendees are given a rigorous introduction to frequentist hypothesis testing including discussions about parametric statistical distributions, significance levels, error rates, effect sizes, statistical power, standard errors, confidence intervals, and p-values. Attendees also learn about strategies for successful experimental design, controlling for confounding and lurking covariates, handling missing values, and assessing causation against correlation. Several parametric hypothesis tests including t-tests, Chi-Squared tests, One-Way ANOVA (Analysis of Variance), Factorial ANOVA, and One-Way MANOVA (Multivariate Analysis of Variance) are covered in R with comparisons to SAS, including a thorough discussion of each test’s assumptions, use-cases, output, and limitations. Frequently used non-parametric equivalents including the Mann-Whitney U test, the Wilcoxon Signed-Rank test, and the Kruskal-Wallis test are similarly investigated and developed in R. By enrolling in this course, each attendee receives the documented R and SAS code files, their personal copy of the PDF version of the slides, and the confidence to successfully perform statistical hypothesis testing in their organization.
Mastering the Machine Learning Toolkit to Power your Regression & Classification Needs: A Hands-on-Workshop Leveraging Python's Open-Source Libraries for Training Supervised ML Algorithms
Ryan Paul Lafler
2023 - 2024
This course is open to all aspiring and experienced data scientists interested in developing and optimizing popular machine learning algorithms for regression and classification using Python. By the end of this course, attendees will be empowered to confidently train, fine-tune, evaluate, and deploy their own supervised machine learning algorithms tailored to their organization’s needs. Starting simple and then incrementally building towards more advanced algorithms capable of flexible learning, attendees are given numerous tips, tricks, and techniques for navigating the complexities of machine learning. Attendees are also provided a rigorous introduction to statistics and probability that enhances their understanding of popular machine learning models. Topics include minimizing the bias-variance tradeoff associated with model selection; understanding statistical inference vs. purely predictive models; generalizing models beyond their training dataset; balancing model complexity with interpretability; optimizing model hyperparameters; and programming supervised machine learning algorithms developed in Python with Scikit-Learn and various data science packages including Pandas, NumPy, and SciPy. Several statistical and machine learning algorithms for classification and regression are fully trained and optimized using StatsModels, Scikit-Learn, and TensorFlow, including, Ordinary Least Squares Regression; LASSO Regression; Decision Trees; Random Forest Ensembles; Gradient Boosted Ensembles; and Artificial Neural Networks.
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Constantly Improving by Actively Publishing, Researching, Learning, Networking, & Collaborating with Data Science Professionals
Staying current in the field of data science requires constant research and a curious, growth-oriented mindset.
Premier Analytics Consulting is dedicated to publishing and presenting our experiences and research at professional conferences so that the entire data science community can learn and grow from it.
Visiting Conferences allows us to network, collaborate, and learn from other curious-minded professionals too.
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Contributing Our Knowledge to the Fields of Data Science, Machine Learning, Statistical Programming, & Data Reporting
Presenting our publications at Conferences is a wonderful experience. Our Team at Premier Analytics Consulting takes great pride in delivering informative, insightful, and meaningful presentations that all attendees can learn something from.
Using programming languages including Python, R, JavaScript, SAS, and SQL, we're constantly publishing new research to share with colleagues and the entire data science community.
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Delivering On-Site, Professional Training Seminars & Hands-on-Workshops to Clients & Conference Attendees
Premier Analytics Consulting excels at providing training on Machine Learning, Deep Learning, Statistical Modeling, and Data Engineering & Visualization using Python, R, & SAS.
Conferences provide an affordable venue for our clients to take a selection of our course offerings. Our Hands-on-Workshops show example-oriented programming courses geared towards applied data science topics.