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NumPy for Data Science: 140+ Practical Exercises in Python

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NumPy for Data Science: 140+ Practical Exercises in Python, available at $44.99, has an average rating of 5, with 152 lectures, 141 quizzes, based on 5 reviews, and has 262 subscribers.

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You will learn about 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 This course is ideal for individuals who are 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. or 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. or 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. It is particularly useful 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. or 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. or 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.

Enroll now: NumPy for Data Science: 140+ Practical Exercises in Python

Summary

Title: NumPy for Data Science: 140+ Practical Exercises in Python

Price: $44.99

Average Rating: 5

Number of Lectures: 152

Number of Quizzes: 141

Number of Published Lectures: 152

Number of Published Quizzes: 141

Number of Curriculum Items: 293

Number of Published Curriculum Objects: 293

Original Price: £19.99

Quality Status: approved

Status: Live

What You Will 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

Who Should Attend

  • 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.

Target Audiences

  • 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.

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.

Course Curriculum

Chapter 1: Introduction

Lecture 1: Introduction

Lecture 2: Welcome to Numpy for Data Science

Lecture 3: Numpy

Chapter 2: Array Routine Creation

Lecture 1: Solution 1

Lecture 2: Solution 2

Lecture 3: Solution 3

Lecture 4: Solution 4

Lecture 5: Solution 5

Lecture 6: Solution 6

Lecture 7: Solution 7

Lecture 8: Solution 8

Lecture 9: Solution 9

Lecture 10: Solution 10

Lecture 11: Solution 11

Lecture 12: Solution 12

Chapter 3: Array Manipulation

Lecture 1: Solution 1

Lecture 2: Solution 2

Lecture 3: Solution 3

Lecture 4: Solution 4

Lecture 5: Solution 5

Lecture 6: Solution 6

Lecture 7: Solution 7

Lecture 8: Solution 8

Lecture 9: Solution 9

Lecture 10: Solution 10

Lecture 11: Solution 11

Lecture 12: Solution 12

Lecture 13: Solution 13

Lecture 14: Solution 14

Lecture 15: Solution 15

Lecture 16: Solution 16

Lecture 17: Solution 17

Lecture 18: Solution 18

Lecture 19: Solution 19

Lecture 20: Solution 20

Lecture 21: Solution 21

Lecture 22: Solution 22

Lecture 23: Solution 23

Chapter 4: Logic Functions

Lecture 1: Solution 1

Lecture 2: Solution 2

Lecture 3: Solution 3

Lecture 4: Solution 4

Lecture 5: Solution 5

Lecture 6: Solution 6

Chapter 5: Random Sampling

Lecture 1: Solution 1

Lecture 2: Solution 2

Lecture 3: Solution 3

Lecture 4: Solution 4

Lecture 5: Solution 5

Instructors

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Frequently Asked Questions


How long do I have access to the course materials?


You can view and review the lecture materials indefinitely, like an on-demand channel.


Can I take my courses with me wherever I go?


Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don’t have an internet connection, some instructors also let their students download course lectures. That’s up to the instructor though, so make sure you get on their good side!

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