Lander Analytics is a New York–based data science firm. We specialize in providing statistical consulting and training services, serving firms both large and small in a diverse set of industries, ranging from financial services and government agencies to consumer goods companies and professional sports organization.

• Our Chief Data Scientist, Jared Lander, is a Columbia University professor and host of the world's largest R meetup. (He also literally wrote the book on R.)

• Our team includes academics, industry practioners and management consultants with skills in analyzing and visualizing data–not to mention developing models.

We're expert at providing data-driven, client-specific solutions for complex, challenging problems, helping our clients navigate Big Data to unlock its full potential.

The team of Data Scientists at Lander Analytics has diverse academic backgrounds, professional experience, and areas of expertise. We specialize in Data Science and its applications, with a focus on projects of all sorts. Types of projects include the use of advanced statistical techniques and data visualization (such as developing Shiny Apps), designed to help forecast, analyze, and predict outcomes for our clients.

• Dr. Andrew Gelman is a professor of Statistics and Political Science and the director of the Applied Statistics Center at Columbia University. He holds a bachelors degree from MIT and both masters and Ph.D. degrees from Harvard University. He works on a wide range of topics.

• Daniel Chen specializes in research design, analysis. He teaches scientific computing with an emphasis on R, Git, Python and Linux. He has a background in Epidemiology, and utilizes data and statistical models to study the spread of disease and the efficacy of medicine and treatments.

• Vivian Peng is a visual artist with experience in infomatics and sociomedical sciences to display statistical data in a compelling fashion. She holds a masters in public health from Columbia University and a bachelors in Molecular Cell Biology from University of California, Berkeley.

• Jeff Horner is a software engineer who has been participating and contributing to the R community for a number of years.Jeffrey received his bachelors of Computer Science degree from the University of Tennessee.

• Michael Piccirilli is a data scientist at the Stanford Graduate School of Business, using algorithms from econometrics, statistics and machine learning to derive insights. He holds a masters in statistics from Columbia University and a bachelors in finance from Pace University.

• Bob Carpenter is a research scientist in computational statistics (Columbia University). He designed the Stan probabilistic programming language and is one of the Stan core developers.

We offer a variety of trainings, both onsite and off, in statistical programmming and modeling techniques, among which are including the following:

• Introduction to R
• Introduction to Python
• Introduction to Stan
• Introduction to Julia

We also offer more advanced courses that can be tailored to the individual needs of an organization, examples of which including the following:

• Modeling and Analytics in R
• Data Visualization and Presentation
• Machine Learning Techniques
• Bayesian Inference and Imputation
• Developing Shiny apps

Beyond statistics, We provide general training courses in programming data, from C++ and RCPP to SQL and Qlikview.

We provide consulting solutions to address our clients most pressing business needs and challenging business problems. Our clients are firms both large and small, operating across a wide array of industries, such as the following:

• Finance
• Government Agencies
• Consumer Package
• Corporate Real Estate
• Professional Sports Organizations

Some of the techniques we employ in the work we do include the following:

• Time Series Analysis
• Survey Analysis
• Machine Learning
• Regularization and Shrinkage
• Marketing Analytics (including targeting)
• Spatial Analysis and Mapping