The R for Beginners training program is designed for individuals who are new to programming or data analysis and want to build a strong foundation in R, one of the most widely used languages in data science, statistics, and research. This hands-on course introduces participants to the basics of R programming, including data structures, functions, and essential packages. Through practical exercises and real-world examples, learners will gain the skills to import, clean, visualize, and analyze data confidently. Whether you’re a student, researcher, analyst, or working professional, this course equips you with the tools needed to start your journey in data analysis using R. By the end of the training, participants will be able to perform basic data manipulation, generate insightful plots, and write reproducible code for statistical analysis and reporting.
The Advanced Time Series Analysis with R training program is designed for researchers, data scientists, and finance professionals who aim to explore cutting-edge techniques for modeling complex time-dependent data. This course goes beyond traditional forecasting methods and introduces participants to advanced analytical tools capable of capturing nonlinearities, structural breaks, regime changes, and interconnected dynamics across time, frequency, and quantile domains. Using R and its rich ecosystem of specialized packages, participants will gain hands-on experience in analyzing financial markets, macroeconomic indicators, and high-frequency data. The training focuses on both methodological understanding and practical implementation, making it ideal for applied research and advanced empirical work in economics, finance, and related disciplines.
The Panel Data Econometrics with Stata training program is tailored for researchers, analysts, and graduate students who want to master the tools and techniques used in analyzing panel (longitudinal) data. This course offers a comprehensive, hands-on introduction to key econometric models and methods, using Stata as the primary software. Participants will learn how to handle unbalanced and balanced panel datasets, apply fixed effects (FE) and random effects (RE) models, perform robust causal inference, and deal with endogeneity using advanced techniques like GMM and 2SLS. The course also includes modern applications such as Difference-in-Differences (DiD), Oster’s delta for robustness checks, Impact Threshold of a Confounding Variable (ITCV), Propensity Score Matching (PSM), and Entropy Balancing. By the end of the training, learners will be equipped to conduct high-quality empirical research using panel data methods confidently and accurately.