Data Science Bootcamp

Running machine learning algorithms is easier than ever. Determining whether the results are reliable is more challenging. A model can achieve a high evaluation score yet fail to address the business problem it was designed to solve due to data leakage in the validation split, a misinterpreted metric, or an unexamined feature. This course guides you from querying raw data to building, evaluating, and explaining models whose results you can justify. I developed this course based on my research in neural networks, adaptive AI, and intelligent tutoring systems. My research reinforced the importance of evaluating model outputs against reliable evidence. I've noticed a similar pattern among learners and researchers: people can train models but often overlook rigorous evaluation, clear interpretation of results, and whether the chosen technique is appropriate for the problem. As VP of Technical Content at Educative, I've seen how these gaps can limit the effectiveness of even experienced engineers. You'll start with the fundamentals: SQL for querying and analyzing data, NumPy and pandas for data manipulation, and statistical methods for drawing reliable conclusions. You'll then clean and explore datasets, engineer features, and visualize and present findings using Matplotlib, Seaborn, Plotly, and Streamlit. Next, you'll define machine learning problems, select appropriate modeling approaches, and build linear and logistic regression models, decision trees, random forests, and gradient boosting models. You'll also learn to validate model performance, tune hyperparameters, and interpret results. Finally, you'll explore clustering, time series forecasting, text analytics, A/B testing, and recommender systems. By the end, you'll be able to translate business questions into data science solutions with validated results, explain your approach, and justify your findings to stakeholders who use them to make decisions.