NumPy for Data Science: 140+ Practical Exercises in Python

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

Description

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.

Who this course is for:

• A hands-on 140+ exercise course on numpy is suitable for anyone interested in learning or improving their skills in data analysis, scientific computing, or machine learning using numpy. This course would be especially useful for data scientists, engineers, researchers, or analysts who want to learn how to use numpy to manipulate, analyze, and visualize data efficiently.
• This course would be a good fit for beginners who want to learn the basics of numpy as well as advanced users who want to deepen their understanding of numpy and learn more advanced techniques. However, some basic knowledge of programming and Python is typically required to get the most out of a numpy course.
• If you have a specific application or project in mind that requires the use of numpy, a 140+ exercise course on numpy can help you acquire the skills and knowledge you need to complete that project effectively. It can also be a good way to prepare for more advanced courses or certifications in data science or machine learning, as numpy is a fundamental library used in many data analysis and machine learning tasks.