SupStat offers high-quality classroom and online training in statistical software use, applied statistical methods, data mining, and data visualization. Our goal is to empower students to utilize modern analytic tools in the management of data in a wide range of industry applications and research fields.
We offer training for individuals and corporate clients in New York, Beijing, and Shanghai. The following is a selection of our core data science courses being offered at the NYC Data Science Academy, the training brand of SupStat in the New York City area. Please visit www.nycdatascience.com for our full course listing and corporate training opportunities.
Intro to Data Science with R(workday public workshop)
Data Science with Python: Machine Learning
Data Science with R: Data Analysis
Data Science with Python: Data Analysis
Big Data with Hadoop: 5 Real World Applications
Big Data with Hadoop: Data Engineer Professionals
Training Course Modules
We have more than 10 years experience in developing data analytic solutions with R. We offer the best learning experience and introduce more people into the R world. We can provide beginner, intermediate and advanced level customized content based on participants’ feedback and domain focus. The following are some of our courses:
Module
Description
Intro to Data Analysis
Basic programming, data handling, plotting and analytics
Visualization with R
Basic visualization packages, such as lattice and ggplot2, as well as
advanced packages such as iPlots, googleVis and RGGobi, and rChart
R Software and Applied Statistics
Introduction to the R language, programming basics, plotting, basic statistics,
regression, classification, association rules, logistic regression, generalized
linear models, multi-layer models, PLS and SEM path models, time series,
and state space models.
Data Mining
Focusing on real applications of R in the Data Mining field, we teach
the caret package and how to write classification, regression, and clustering
algorithms.
Advanced Programing
The course emphasizes good practices in object-oriented programming in
R, including designing and developing clear, concise and efficient code,
understanding S3 and S4 objects, functional programming, as well as making
R packages.
Automated Reporting
We teach how to use the Knitr package to create reproducible and automated
reports.
R and Optimization
We provide an Introduction to mathematical programming and optimization
through several classical theories and models. Specific packages introduced
include lpSolve, TSP, Rsolnp, and Rglpk, all of which can be used to help
companies better optimize management.
R and Web Services
We cover deployment of R applications on the server via Rserver and OpenCPU,
allowing client connections through a web service’s API.
Quantitative Finance
We introduce quantitative investment theory and modeling through theuse
of high-efficiency R programming.
Big Data with R and RHadoop
Configuration-related knowledge of RHadoop under Ubuntu with the completion
of the data analysis process using RHadoop.
Text Mining with R
Describes the principles of HMMs and their application through the use
of Rwordseg, tm, and rmmseg4j packages.
R and Database Management
Use R inside MySQL to connect the R language with its data mining and
analytics capabilities to your database.
R and High Performance Computing
Improve computational efficiency in R by utilizing parallel computing
and compilation procedures
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Our Customers
The following firms have improved the expertise of their teams through training with SupStat:
