NumPy for Data Science: 140+ Practical Exercises in Python

This course will provide a comprehensive introduction to the NumPy library and its capabilities. The course is designed to be hands-on and will include over 140+ practical exercises to help learners gain a solid understanding of how to use NumPy to manipulate and analyze data. The course will cover key concepts such as:Array Routine CreationArange, Zeros, Ones, Eye, Linspace, Diag, Full, Intersect1d, TriArray ManipulationReshape, Expand_dims, Broadcast, Ravel, Copy_to, Shape, Flatten, Transpose, Concatenate, Split, Delete, Append, Resize, Unique, Isin, Trim_zeros, Squeeze, Asarray, Split, Column_stackLogic FunctionsAll, Any, Isnan, EqualRandom SamplingRandom.

rand, Random. cover, Random. shuffle, Random.

exponential, Random. triangularInput and OutputLoad, Loadtxt, Save, Array_strSort, Searching and CountingSorting, Argsort, Partition, Argmax, Argmin, Argwhere, Nonzero, Where, Extract, Count_nonzeroMathematicalMod, Mean, Std, Median, Percentile, Average, Var, Corrcoef, Correlate, Histogram, Divide, Multiple, Sum, Subtract, Floor, Ceil, Turn, Prod, Nanprod, Ransom, Diff, Exp, Log, Reciprocal, Power, Maximum, Square, Round, RootLinear AlgebraLinalg. norm, Dot, Linalg.

det, Linalg. invString OperationChar. add, Char.