March 30th – April 1st, 2021

R for Data Science

Learn the latest models, packages, and tools from the top practitioners and R Data Scientists
Register now

R, long one of the most popular languages for statistical modeling, has evolved into a data science powerhouse platform with dozens of packages and interfaces for machine learning, deep learning, data visualization, and various other data science methods.

This advanced language is used for performing complex statistical modeling and provides support for operations on arrays, matrices, and vectors. Its many graphical libraries allow users to delineate aesthetic graphs and also develop web-applications using R Shiny, which is used for embedding visualizations in web-pages. R also provides  several options for advanced data analytics like prediction modeling and advanced machine learning algorithms. With interfaces to popular frameworks like TensorFlow and Keras, R users can easily build and deploy the latest deep learning models. 

Some of Our Current R Presenters

Chief Data Scientist, Author of R for Everyone, ProfessorLander Analytics, Columbia Business School

Jared Lander

Jared Lander is the Chief Data Scientist of Lander Analytics a data science consultancy based in New York City, the Organizer of the New York Open Statistical Programming Meetup and the New York R Conference and an Adjunct Professor of Statistics at Columbia University. With a masters from Columbia University in statistics and bachelors from Muhlenberg College in mathematics, he has experience in both academic research and industry. His work for both large and small organizations ranges from music and fundraising to finance and humanitarian relief efforts. He specializes in data management, multilevel models, machine learning, generalized linear models, data management and statistical computing. He is the author of R for Everyone: Advanced Analytics and Graphics, a book about R Programming geared toward Data Scientists and Non-Statisticians alike and is creating a course on glmnet with DataCamp.

Machine Learning in R Part I & II(Training)

Advanced ResearcherInria

Julie Josse, PhD

Julie Josse is a senior researcher in statistics and machine learning applied to health at Inria, a French research institute in digital sciences, and Professor at Ecole Polytechnique (Paris). She is an expert in the treatment of missing values (inference, multiple imputation, matrix completion, MNAR, supervised learning with missing values) and has created a website on the topic (https://rmisstastic.netlify.app/) for users. Her research also focuses on causal inference techniques (causal inference with missing values, combining RCT and observational data) for personalized medicine. Julie Josse is dedicated to reproducible research with R statistical software: she has developed packages including FactoMineR and missMDA to transfer her work.

Overview of Methods to Handle Missing Values(Tutorial)

Principal AI ScientistZS Associates

Srinivas Chilukuri

Srinivas leads ZS AI Research Lab with a focus on frontier innovation and development of cutting edge algorithms. Srinivas’s core expertise areas include automated machine learning, natural language processing, and marketing AI across industries. He has authored several thought leadership articles and presented at conferences. Prior to joining ZS, Srinivas spent time as a solution architect building expert systems to automate product design and manufacturing across multiple industries viz., automobile, power systems, medical devices and retail

Improving Structured Data Ml Processes with Generative Adversarial Networks(Business Talk)

Director of Data Science EducationFlatiron School

Lore Dirick, PhD

Lore Dirick is Director of Data Science Education at Flatiron School (https://flatironschool.com). She joined Flatiron School in May 2018, where she built Flatiron School’s 15-week Data Science Program, that by now has graduated hundreds of students globally, from the ground up. Before joining Flatiron School, Lore worked as a Curriculum Lead for online data science school DataCamp, where she built out the R and Python curriculum and taught online data science courses to over 70,000 students. She earned a PhD in Business Economics at KULeuven (Belgium), where she was a member of the statistics department and performed research on advanced credit risk modeling techniques.

Using Survival Analysis to Model Credit Risk: What, Why and How?(Business Talk)


Some of Our Previous R Presenters

FounderOnPoint Insights

Nirav Shah

Nirav Shah is the Founder of OnPoint Insights, data analytics, software services, and staff augmentation consultancy based in Boston. He has 15 years of industry experience – mainly in consulting on data analytics, big data modeling, process analytics, and real-time data solutions, and training customers in data analytics, dashboards, and data visualization.

He consults and teaches in applying data analytics for manufacturing, operations, supply chain, process control strategies with clients to improve the manufacturing process and operational efficiency. He has implemented real-time process monitoring data analytics and fault detection systems for leading biopharma customers and clients from other industries such as chemical, pulp, and paper, food and beverages. He helps customers in providing better process insights using data-driven solutions.

He is also an Adjunct Professor at the University of Massachusetts in Boston where he teaches Engineering Process Analytics, a graduate-level class in the Engineering Department,  teaches Business Analytics and Dashboard Visualization at a technical college and conducts BootCamps and Workshops at General Assembly Boston.  He has taught courses and conducted workshops to industry clients on Multivariate Data Analysis for ten years. He has spoken at various conferences ( ODSC East Boston, ODSC India, Global AI).

He completed his dual Masters in Chemical and Computer Engineering from the University of Massachusetts and an MBA in Entrepreneurship from Babson College.

Data Analysis, Dashboards and Visualization – How to Create Powerful Visualizations Like a Zen Master(Training)

Associate Professor of AnalyticsInstitute for Advanced Analytics at NC State University

Aric LaBarr, PhD

A Teaching Associate Professor in the Institute for Advanced Analytics, Dr. Aric LaBarr is passionate about helping people solve challenges using their data. There he helps design the innovative program to prepare a modern workforce to wisely communicate and handle a data-driven future at the nation’s first Master of Science in Analytics degree program. He teaches courses in predictive modeling, forecasting, simulation, financial analytics, and risk management. Previously, he was Director and Senior Scientist at Elder Research, where he mentored and led a team of data scientists and software engineers. As director of the Raleigh, NC office he worked closely with clients and partners to solve problems in the fields of banking, consumer product goods, healthcare, and government. Dr. LaBarr holds a B.S. in economics, as well as a B.S., M.S., and Ph.D. in statistics — all from NC State University.

Advanced Fraud Modeling & Anomaly Detection with Python & R part 1(Training)

Advanced Fraud Modeling & Anomaly Detection with Python & R part 2(Training)

Supervisor, Data EducationChildren's Hospital of Philadelphia

Joy Payton

Joy Payton is a cloud engineer, data scientist, and adjunct professor who specializes in helping biomedical professionals conduct reproducible computational research. In addition to moving medicine forward through principles of open science and reproducibility, Joy also enjoys teaching citizen scientists how to use public data repositories to understand their own communities better and advocate for change from a data-centric perspective. Her various roles allow Joy to lead efforts to teach people how to write their first line of code and help anyone who’s interested climb the data science learning curve. Currently employed by the Children’s Hospital of Philadelphia and Yeshiva University, Joy is always open to hearing about open-source, data-centric volunteer opportunities for herself and her students.

Using Google BigQuery Public Data With Colab Notebooks: From Insight to Knowledge Using Cloud Computing, SQL, and Python to Map NYC Fall Tree Color!(Workshop)

Computational BiologistDana Farber Cancer Institute

Aedin Culhane, PhD

Experienced computational biologist, R/Bioconductor developer, whose research seeks to uncover the molecular changes which give rise or promote cancer development. Aedin’s team curates GeneSigDB, a database of over 3,500 gene signatures (or genesets) and they develop gene set-based approaches for large scale integrated data analysis.

Specialties: bioconductor, R, bioinformatics, genomics, multivariate analysis, biostatistican, microarray, gene expression, computational biology, breast cancer, ovarian cancer.

Finding Correlated Trends across multiple Data Sets using Matrix Factorization(Talk)


More talks, hands-on workshop and training sessions
See all sessions

You Will Meet

  • Some of the world’s best data science speakers

  • The brains and authors behind today’s most popular open data science tools, topics, and languages

  • Hundreds of attendees focused on data science

  • Chief Data Scientists

  • Thought leaders working in data science

  • Data Scientists and Analysts

  • Software Developers

  • CEOs, CTOs, CIOs

  • Data Visualization professionals

  • Venture Capitalists and Investors

  • Startup Founders and Executives

  • Attendees from Healthcare, Finance, Education, Business, Intelligence, and other industries

  • Big data and data science innovators

 

What You’ll Learn

Talks, Tutorials, and hands-on workshop and training sessions for this focus area include:

Topics

  • Machine Learning with R

  • Deep Learning with R

  • Statistical Modeling

  • Predictive Analytics

  • Data Analysis

  • Data Visualization

  • Text Analytics

  • Natural Language Parsing (NLP)

Models

  • Regression

  • Classification

  • Bayesian Models

  • Machine Learning Models

  • Deep Learning Models

  • Gradient Boosting 

  • Time Series Models 

  • Random Forest  

  • KNN, SVM, & LDA 

Tools 

  • MLR

  • R Shiny & tidyr

  • ggplot2

  • Caret 

  • TensorFlow and Keras Interfaces

  • R Markdown

  • XGBoost

  • tm & OpenNLP 

  • e1071 & dplyr

Why Attend?

Several of the best minds and biggest names in data science will be presenting

Network with attendees from leading data science companies to learn how others are tackling similar problems

Gain quality training in the hottest data science topics, tools, and languages

Learn the latest in data science from industry leaders without having to make room in the budget — tickets are surprisingly affordable

ODSC EAST 2021 | March 30th – April 1st

Register Now
Open Data Science

 

 

 

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Cambridge, MA 02142
info@odsc.com

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