NumPy for Data Science: 140+ Practical Exercises in Python

Name: NumPy for Data Science: 140+ Practical Exercises in Python
Rating: 4.4 (8 reviews)

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

Created byRahul Lamba

Last updated 2/2023

English

What you'll learn

Develop a strong understanding of the fundamental concepts and capabilities of numpy , including array creation, indexing, slicing, reshaping etc
Become proficient in using various numpy functions and methods to manipulate and analyze data stored in arrays, such as aggregating, sorting or filtering.
Learn how to use numpy to perform advanced numerical computations, such as linear algebra
Gain practical experience applying numpy in real-world data analysis and scientific computing scenarios

Coding Exercises

This course includes our updated coding exercises so you can practice your skills as you learn.

Course content

11 sections • 152 lectures • 45m total length

Introduction1:09
Welcome to Numpy for Data Science0:29
Numpy0:42

Exercise 1
Solution 10:02
Exercise 2
Solution 20:03
Exercise 3
Solution 30:03
Exercise 4
Solution 40:04
Exercise 5
Solution 50:05
Exercise 6
Solution 60:06
Exercise 7
Solution 70:03
Exercise 8
Solution 80:03
Exercise 9
Solution 90:03
Exercise 10
Solution 100:04
Exercise 11
Solution 110:05
Exercise 12
Solution 120:06
Exercise 13
Solution 130:03
Exercise 14
Solution 140:06
Exercise 15
Solution 150:03
Exercise 16
Solution 160:07
Exercise 17
Solution 170:07
Exercise 18
Solution 180:03
Exercise 19
Solution 190:03
Exercise 20
Solution 200:05
Exercise 21
Solution 210:03
Exercise 22
Solution 220:04
Exercise 23
Solution 230:04

Exercise 1
Solution 10:05
Exercise 2
Solution 20:04
Exercise 3
Solution 30:12
Exercise 4
Solution 40:07
Exercise 5
Solution 50:06
Exercise 6
Solution 60:04
Exercise 7
Solution 70:05
Exercise 8
Solution 80:06
Exercise 9
Solution 90:07
Exercise 10
Solution 100:06
Exercise 11
Solution 110:05
Exercise 12
Solution 120:06
Exercise 13
Solution 130:06
Exercise 14
Solution 140:04
Exercise 15
Solution 150:06
Exercise 16
Solution 160:05
Exercise 17
Solution 170:16
Exercise 18
Solution 180:05
Exercise 19
Solution 190:08
Exercise 20
Solution 200:07
Exercise 21
Solution 210:06
Exercise 22
Solution 220:04
Exercise 23
Solution 230:06
Exercise 24
Solution 240:09
Exercise 25
Solution 250:04
Exercise 26
Solution 260:06
Exercise 27
Solution 270:07

Exercise 1
Solution 10:02
Exercise 2
Solution 20:10
Exercise 3
Solution 30:02
Exercise 4
Solution 40:03
Exercise 5
Solution 50:03
Exercise 6
Solution 60:18
Exercise 7
Solution 70:05
Exercise 8
Solution 80:09
Exercise 9
Solution 90:05
Exercise 10
Solution 100:06
Exercise 11
Solution 110:05
Exercise 12
Solution 120:05
Exercise 13
Solution 130:08
Exercise 14
Solution 140:07
Exercise 15
Solution 150:16
Exercise 16
Solution 160:07
Exercise 17
Solution 170:11
Exercise 18
Solution 180:06
Exercise 19
Solution 190:06
Exercise 20
Solution 200:21
Exercise 21
Solution 210:12
Exercise 22
Solution 220:05
Exercise 23
Solution 230:02
Exercise 24
Solution 240:07
Exercise 25
Solution 250:10
Exercise 26
Solution 260:04
Exercise 27
Solution 270:03
Exercise 28
Solution 280:05
Exercise 29
Solution 290:06
Exercise 30
Solution 300:02
Exercise 31
Solution 310:04
Exercise 32
Solution 320:05

Requirements

Basic knowledge of python and numpy

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 :

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 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.

NumPy for Data Science: 140+ Practical Exercises in Python

What you'll learn

Explore related topics

Coding Exercises

Course content

Introduction3 lectures • 2min

Array Routine Creation12 lectures • 1min

Array Manipulation23 lectures • 2min

Logic Functions6 lectures • 1min

Random Sampling8 lectures • 1min

Input and Output6 lectures • 1min

Sorting, Searching & Counting27 lectures • 3min

Linear Algebra11 lectures • 1min

Mathematical32 lectures • 4min

String Operations16 lectures • 1min

Requirements

Description

Who this course is for: