NumPy for Data Science: 140+ Practical Exercises in Python

NumPy for Data Science: 140+ Practical Exercises in Python free download

Enhance your Python programming and data science abilities by completing more than 140+ NumPy exercises.

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 :

1. Array Routine Creation

   Arange, Zeros, Ones, Eye, Linspace, Diag, Full, Intersect1d, Tri

2. 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

3. Logic Functions

   All, Any, Isnan, Equal

4. Random Sampling

   Random.rand, Random.cover, Random.shuffle, Random.exponential, Random.triangular

5. Input and Output

   Load, Loadtxt, Save, Array_str

6. Sort, Searching and Counting

   Sorting, Argsort, Partition, Argmax, Argmin, Argwhere, Nonzero, Where, Extract, Count_nonzero

7. 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

8. Linear Algebra

   Linalg.norm, Dot, Linalg.det, Linalg.inv

9. 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 NumPy 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 NumPy library.