Core data science and Machine Learning skills with NumPy, SciPy, Pandas, Matplotlib, Random and Ufunc.
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Free
The curriculum includes instruction on several probability distributions, including Normal, Binomial, Poisson, Logistic, Chi Square, and Pareto.
Instruction covers essential array operations like indexing, slicing, and reshaping, alongside Pandas workflows for data cleaning and transformation.
Editorial course preview
These 3 complementary views highlight concrete, legible examples from the course presentation.
A code editor window displaying Python source code that imports the numpy library, defines a 2D array variable, and includes print statements.
A dark-themed code editor displays Python code importing numpy and creating an array, with an autocomplete dropdown listing various numpy functions.
A dark-themed code editor displays a Python script creating a NumPy array, with an autocomplete dropdown listing object attributes and data types.
The curriculum lacks explicit projects, coding exercises, or hands-on assignments to reinforce the technical concepts.
The curriculum aligns with the stated goals of learning NumPy and Pandas for data analysis.