Presented By O’Reilly and Cloudera
Make Data Work
21–22 May 2018: Training
22–24 May 2018: Tutorials & Conference
London, UK
Jared Lander

Jared Lander
Chief Data Scientist, Lander Analytics

Website | @jaredlander

Jared P. Lander is chief data scientist of Lander Analytics, where he oversees the long-term direction of the company and researches the best strategy, models, and algorithms for modern data needs. He specializes in data management, multilevel models, machine learning, generalized linear models, data management, visualization, and statistical computing. In addition to his client-facing consulting and training, Jared is an adjunct professor of statistics at Columbia University and the organizer of the New York Open Statistical Programming Meetup and the New York R Conference. He is the author of R for Everyone, a book about R programming geared toward data scientists and nonstatisticians alike. Very active in the data community, Jared is a frequent speaker at conferences, universities, and meetups around the world and was a member of the 2014 Strata New York selection committee. His writings on statistics can be found at He was recently featured in the Wall Street Journal for his work with the Minnesota Vikings during the 2015 NFL Draft. Jared holds a master’s degree in statistics from Columbia University and a bachelor’s degree in mathematics from Muhlenberg College.


11:1511:55 Thursday, 24 May 2018
Data science and machine learning, Expo Hall
Location: Expo Hall Level: Beginner
Secondary topics:  Time Series and Graphs
Jared Lander (Lander Analytics)
Average rating: ****.
(4.00, 2 ratings)
Temporal data is being produced in ever-greater quantity, but fortunately our time series capabilities are keeping pace. Jared Lander explores techniques for modeling time series, from traditional methods such as ARMA to more modern tools such as Prophet and machine learning models like XGBoost and neural nets. Along the way, Jared shares theory and code for training these models. Read more.