NumPy for Data Science: 140+ Practical Exercises in Python

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Price: 19.99$

This course will provide a comprehensive introduction to the Num Py 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 Num Py to manipulate and analyze data. The course will cover key concepts such as: Array Routine Creation Arange, Zeros, Ones, Eye, Linspace, Diag, Full, Intersect1d, Tri Array Manipulation Reshape, Expand dims, Broadcast, Ravel, Copy to, Shape, Flatten, Transpose, Concatenate, Split, Delete, Append, Resize, Unique, Isin, Trim zeros, Squeeze, Asarray, Split, Column stack Logic Functions All, Any, Isnan, Equal Random Sampling Random. rand, Random. cover, Random. shuffle, Random. exponential, Random. triangular Input and Output Load, Loadtxt, Save, Array str Sort, Searching and Counting Sorting, Argsort, Partition, Argmax, Argmin, Argwhere, Nonzero, Where, Extract, Count nonzero Mathematical Mod, 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, Root Linear Algebra Linalg. norm, Dot, Linalg. det, Linalg. inv String Operation Char. add, Char. split. Char. multiply, Char. capitalize, Char. lower, Char. swapcase, Char. upper, Char. find, Char. join, Char. replace, Char. isnumeric, Char. count. This course is designed for data scientists, data analysts, and developers who want to learn how to use Num Py to manipulate and analyze data in Python. It is suitable for both beginners who are new to data science as well as experienced practitioners looking to deepen their understanding of the Num Py library.