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Fundamentals, Python overview and Data Science lifecycle.
Pandas, NumPy and data wrangling techniques.
EDA, Matplotlib and Seaborn.
Regression, classification and clustering.
Feature engineering and hyperparameter tuning.
TensorFlow and Keras introduction.
Hadoop, Spark and PySpark.
ETL and data pipelines.
Flask/Django deployment.
Git and version control.
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