🚀 My Journey in Master of Statistics and Data Science: Unveiling The Learnings
- 2 minsEmbarking on my Masters in Statistics and Data Science was a fascinating journey, filled with the thrill of uncovering complex concepts, facing challenges head-on, and making meaningful connections between theory and practice. Here, I would like to give you an insight into some of the most influential courses I have taken and the key insights I have gained from them:
1. Statistical Modelling
This course provided a comprehensive overview of a wide range of regression models, including generalised linear and additive models, mixed models, and duration/survival models, among others. We delved deep into the area of latent variable models, measurement error, model selection strategies and beyond mean regression. A fascinating part of the course was the introduction to Directed Acyclic Graphs (DAGs) and causal inference.
Through this course I developed a deep understanding of model building, diagnostics and selection. I learned how to apply different types of statistical models in real-world scenarios, how to formalise research questions, and how to interpret statistical results. The lecture series also allowed me to gain insights into current research in statistical modelling.
2. Supervised Learning
The supervised learning course was a deep dive into the theoretical foundations of machine learning and its most prominent methods. The course covered the principles of risk minimisation, information theory, the curse of dimensionality, and regularisation methods. We also explored several prominent learning algorithms such as support vector machines, Gaussian processes, and boosting.
Through this course, I not only grasped the theoretical underpinnings of machine learning, but also acquired the practical skills to implement advanced ML techniques. I learned how to select appropriate modelling approaches in different scenarios, bridging the gap between theory and application.
3. Statistical Inference
The Statistical Inference course gave me an in-depth understanding of various estimation and inference techniques. We started with the classical theory of point estimation and tests, exploring concepts such as loss function, risk function and multiple testing procedures. The course then moved on to likelihood-based estimation of statistical models and Bayesian inference methods, including modern sampling approaches.
This course enabled me to gain a deep understanding of the fundamental concepts of statistical inference and reasoning. I learned how to use important tools for parameter estimation and for estimating the distributions of these estimates. I also understood the assumptions, methodological framework, strengths and weaknesses of each approach.
My journey in the Master of Statistics and Data Science has equipped me not only with academic knowledge, but also with a deeper understanding of real-world data dynamics. Reflecting on this journey, I am more excited than ever to use my learning and skills to make data-driven decisions and contribute to the ever-evolving field of data science.
Medina Bajramovic
Data Scientist and Biostatistician