Statistics and Data Science for Real-World Research
This package is designed for students, researchers, analysts, and professionals who want to use statistics and data science to analyze real-world data with confidence. We will work together to understand the statistical ideas that matter most in practice, including distributions, uncertainty, hypothesis testing, regression models, model assumptions, interpretation, visualization, and communicating results clearly. Depending on your goals, we can cover linear regression, logistic regression, generalized linear models, mixed-effects models, classification, model comparison, feature selection, cross-validation, and practical data analysis workflows in Python. The focus is not only on running statistical models, but on understanding what the model is doing, when it is appropriate, how to interpret the results, and how to avoid common mistakes in real research or applied data science projects.
What's included
This package can be delivered through live 1:1 coaching sessions or asynchronous support, depending on your preferences, schedule, and learning style. Support typically includes conceptual explanations, practical examples, analysis planning, code walkthroughs, model interpretation, project feedback, and clear next steps. I can provide simple examples and exercises to build your understanding step by step. Before we begin, you can share your background, goals, and any relevant materials. Whether through live sessions or asynchronous feedback, you will receive personalized guidance, practical direction, and recommended resources or exercises when helpful.
- A clear understanding of key statistical concepts such as distributions, uncertainty, p-values, confidence intervals, and effect sizes Practical guidance on choosing the right regression model for your data Support with linear regression, logistic regression, generalized linear models, and mixed-effects models Feedback on your own dataset, research question, analysis plan, or statistical model Help interpreting statistical results clearly and avoiding common mistakes A structured analysis plan with clear next steps for your project, coursework, or research
Meet your expert
Jamal E.
AI Researcher at Cambridge | ML & Neuroimaging Expert
I am a computational neuroscientist and AI researcher with experience in machine learning, data science, and AI. Over the years, I have worked across academia and applied research, building quantitative models for complex real-world problems. I now help people navigate careers in AI, research, and data science, especially those trying to move between academia and industry. My approach is practical, honest, and tailored to each person’s strengths and goals
