Go end-to-end from data sourcing to modeling and interpretation, with hands-on practice and project work.
17 lessons
Stephanie — Lead Data Scientist @JULO
Ex. GOJEK, UNILEVER.
Work through each lesson in order or jump to a topic you need.
Foundations & Python stack
60 min
Build a solid foundation of the data science workflow, from problem framing to model delivery.
Strengthen SQL skills for analytics, joins, aggregations, and warehouse-scale querying.
Set up Python fundamentals for data work, including syntax, variables, and basic control flow.
Understand lists, dictionaries, tuples, and sets to structure data effectively.
Process tabular datasets with pandas using filtering, transformation, and joining workflows.
Data understanding & exploration
Cover key statistical concepts for analysis, inference, and model interpretation.
Clean missing, duplicate, and inconsistent data to prepare reliable model-ready inputs.
Visualize trends, distributions, and comparisons to communicate insights clearly.
Perform exploratory data analysis to discover patterns, anomalies, and hypotheses.
Machine learning
Learn core machine learning concepts, training flow, and evaluation fundamentals.
Build and evaluate regression models to predict continuous outcomes.
Train classification models and assess performance with suitable metrics.
Apply clustering and dimensionality reduction for unlabeled data exploration.
Understand neural network basics and when deep learning is the right approach.
Business impact & capstone
Translate model outputs into business actions, KPIs, and stakeholder decisions.
Review the full lifecycle from problem framing through deployment-oriented thinking.
Deliver an end-to-end data science project that demonstrates technical and business impact.