Published 2/2023MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHzLanguage: English | Size: 221.42 MB | Duration: 0h 47m

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

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

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 CreationArange, Zeros, Ones, Eye, Linspace, Diag, Full, Intersect1d, TriArray ManipulationReshape, Expand_dims, Broadcast, Ravel, Copy_to, Shape, Flatten, Transpose, Concatenate, Split, Delete, Append, Resize, Unique, Isin, Trim_zeros, Squeeze, Asarray, Split, Column_stackLogic FunctionsAll, Any, Isnan, EqualRandom SamplingRandom.rand, Random.cover, Random.shuffle, Random.exponential, Random.triangularInput and OutputLoad, Loadtxt, Save, Array_strSort, Searching and CountingSorting, Argsort, Partition, Argmax, Argmin, Argwhere, Nonzero, Where, Extract, Count_nonzeroMathematicalMod, 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, RootLinear AlgebraLinalg.norm, Dot, Linalg.det, Linalg.invString OperationChar.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 bners who are new to data science as well as experienced practitioners looking to deepen their understanding of the NumPy library.

Overview

Section 1: Introduction

Lecture 1 Introduction

Lecture 2 Welcome to Numpy for Data Science

Lecture 3 Numpy

Section 2: Array Routine Creation

Lecture 4 Solution 1

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Section 3: Array Manipulation

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Section 4: Logic Functions

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Section 5: Random Sampling

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Section 6: Input and Output

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Section 7: Sorting, Searching & Counting

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Section 8: Linear Algebra

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Lecture 96 Exercise 11

Section 9: Mathematical

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Section 10: String Operations

Lecture 129 Solution 1

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Section 11: Installation & Configuration

Lecture 145 Intro

Lecture 146 What is Google Colaboratory?

Lecture 147 Google Colab and Github

Lecture 148 What is Anaconda Python?

Lecture 149 How to install Anaconda Python on Mac?

Lecture 150 How to install Anaconda Python on Windows?

Lecture 151 How to use Anaconda Navigator

Lecture 152 Jupyter and Spyder

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, eeers, 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 bners 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.

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