Data Science Essentials: Python, Numpy, Pandas, and Scikit-learn

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Explores data manipulation, exploration, and visualization in data science projects using Python.

Covers the basics of object-oriented programming in Python, including objects, classes, inheritance, and input/output handling.

Covers Python basics such as syntax, variables, and functions, introducing the Renku platform for collaborative data science.

Explores data wrangling, numerical data handling, and scientific visualization using Python for engineers.

Explores the ROC curve, True Positive Rate, False Positive Rate, and prediction probabilities in classification models.

Introduces Renku, a platform for collaborative data science, emphasizing reproducibility, shareability, reusability, and security.

Covers the fundamentals of big data ecosystems, focusing on technologies, challenges, and practical exercises with Hadoop's HDFS.

Focuses on file management and exception handling in Python programming.

Introduces functions in Python, covering predefined and user-defined functions, formal and effective parameters, and the importance of docstrings.