Numpy is a widely used Python library that is essential for scientific computing and data analysis. It provides efficient numerical operations and multi-dimensional array manipulation capabilities. For individuals seeking to develop their skills in this area, there are numerous online courses available. These courses offer comprehensive guidance in using Numpy to perform various tasks, such as data analysis, visualization, and machine learning. In this article, we will explore some of the best Numpy courses available online, highlighting their features and benefits.
Here’s a look at the Best Numpy Courses and Certifications Online and what they have to offer for you!
10 Best Numpy Courses and Certifications Online
- 10 Best Numpy Courses and Certifications Online
- 1. Deep Learning Prerequisites: The Numpy Stack in Python (V2+) by Lazy Programmer Inc. (Udemy) (Our Best Pick)
- 2. 100+ Exercises – Python – Data Science – NumPy – 2022 by Paweł Krakowiak, takeITeasy Academy (Udemy)
- 3. Learn Basic Data science and Python Libraries by Akbar Khan (Udemy)
- 4. Data Visualization with Numpy and Pandas by Exam Turf (Udemy)
- 5. NumPy for Data Scientist by Sumit Khandelwal (Udemy)
- 6. 130+ Exercises – Python – Data Science – Pandas – 2022 by Paweł Krakowiak (Udemy)
- 7. Python NumPy For Absolute Beginners by Eftekher Husain (Udemy)
- 8. Python NumPy Library for Data Science by Sai Acuity Institute of Learning Pvt Ltd Enabling Learning Through Insight! (Udemy)
- 9. Python Numpy: Machine Learning & Data Science Course by Oak Academy (Udemy)
- 10. Python with NumPy For Absolute Beginners by Surendra Varma (Udemy)
1. Deep Learning Prerequisites: The Numpy Stack in Python (V2+) by Lazy Programmer Inc. (Udemy) (Our Best Pick)
The Deep Learning Prerequisites: The Numpy Stack in Python (V2+) course by Lazy Programmer Inc. is designed to help individuals learn the necessary skills to implement deep learning and data science algorithms using the Numpy stack in Python. The course aims to remove the obstacle of not knowing enough about the Numpy stack, which often leaves individuals behind when learning these subjects. The Numpy stack includes Numpy, Scipy, Pandas, and Matplotlib, which are frequently needed in deep learning and data science.
The course starts by introducing Numpy, which forms the basis for everything else in the Numpy stack. The central object in Numpy is the Numpy array, which is not like a regular array in other programming languages but is a mathematical object like a vector or a matrix. The course covers various Numpy array operations like addition, subtraction, and multiplication, and how Numpy arrays are optimized for speed. The course also compares the speed of Numpy vectorized operation with Python lists and covers more complicated matrix operations.
Next, the course introduces Pandas, which makes working with datasets more accessible and similar to R programming. Pandas’ central object is the DataFrame, which is similar to SQL tables. The course covers essential dataframe operations like filtering by column, filtering by row, and the apply function.
The course then focuses on Matplotlib, which helps visualize data. Matplotlib can create common plots like line charts, scatter plots, and histograms. It also shows how to display images using Matplotlib.
Finally, the course introduces Scipy, which is an add-on library to Numpy. Scipy uses Numpy’s general building blocks to do specific things like signal processing and common statistics calculations. It includes PDF value, the CDF value, sampling from a distribution, and statistical testing.
The course is suitable for individuals who understand the theory of deep learning and machine learning and can see the code but cannot connect how to turn those algorithms into actual running code.
2. 100+ Exercises – Python – Data Science – NumPy – 2022 by Paweł Krakowiak, takeITeasy Academy (Udemy)
The 100+ Exercises – Python Programming – Data Science – NumPy course, taught by Paweł Krakowiak from takeITeasy Academy, offers participants the opportunity to improve their Python programming and data science skills by solving over 100 exercises in NumPy. The course is designed for individuals with basic knowledge of Python and the NumPy package. The course consists of 100 exercises with solutions that cover popular topics in data science, such as working with numpy arrays, generating numpy arrays, and reshaping arrays. Additionally, participants will learn how to deal with missing values, work with matrices, and compute basic array statistics.
The course is divided into several sections, including Configuration (optional), Tips, Starter, and Exercises 1-100+, as well as a Bonus section. The exercises cover a range of topics from working with dates and strings in arrays to solving systems of equations and linear algebra. Completing the exercises is a great way for individuals to test their knowledge of Python and data science, as well as prepare for interviews.
According to the Stack Overflow Developer Survey 2021, Python is the most wanted programming language, and NumPy is the second most used tool in the “Other Frameworks and Libraries” category. Python has surpassed SQL to become the third most popular technology. The course offers participants the chance to advance their Python skills and prepare for a highly in-demand field.
Overall, the 100+ Exercises – Python Programming – Data Science – NumPy course is a great opportunity for individuals to challenge themselves and improve their skills in Python programming and data science. The course covers a wide range of topics and is suitable for individuals looking to enhance their knowledge in the field.
The course titled “Learn Basic Data Science and Python Libraries” is instructed by Akbar Khan and is designed to cover essential Python topics and libraries for beginners in Data Science or Machine Learning, such as NumPy, Pandas, etc. The course short description indicates that it will cover all the essential Python topics and libraries required for Data Science or Machine Learning beginners.
The long description of the course provides a more detailed overview of the course content. It is designed to teach the basics of Python Data Structures and important Data Science libraries such as NumPy and Pandas with step-by-step examples. The course will start with a basic understanding of the Jupyter notebook and how to write code in it. Then students will learn about basic Python data types such as strings, numbers, and their operations.
The course will also cover different ways to assign and access strings, string slicing, replacement, concatenation, formatting, and strings. Students will learn about basic operations and advanced ones like exponents, as well as order of operations, increments, and decrements, rounding values, and typecasting.
Next, the course will proceed to basic data structures in Python like Lists, Tuples, and Sets. Students will learn about different assignments, access, and slicing options for lists, as well as popular list methods. The course will also cover list extension, removal, reversing, sorting, min and max, existence check, list looping, slicing, and inter-conversion of lists and strings.
The course content is divided into three main sections: Introduction, Numpy and Pandas operations, and Plotting Libraries such as Matplotlib, Seaborn, Cufflinks, and Plotly.
The course titled “Data Visualization with Numpy and Pandas” is taught by Exam Turf instructors, and aims to provide participants with knowledge and skills in data analysis and visualization using the NumPy and Pandas Python libraries. The course is designed to help learners become experts in working with these libraries and gain proficiency in data science concepts. The focus of the training is on Pandas and NumPy, with detailed explanations of all concepts related to these libraries.
NumPy is an open-source structure for mathematical needs and is used for numerical and computation workloads. It is associated with machine learning tools such as Scikit-learn, Pandas, Matplotlib, and TensorFlow. Panda, on the other hand, is preferred for data wrangling and manipulation. Both libraries are essential for Python as a scientific language and can handle matrix and vector manipulations.
The course on Panda and NumPy is a worthwhile investment for anyone looking to enhance their career, whether they are a newcomer to Python or an experienced professional. The skill set is extensive, and the course offers detailed discussions and exposure to advanced toolkits like Python, Azure, and machine learning and data analysis techniques. The course content covers Introduction, Numpy, Pandas, Dataframe Manipulation, and Missing Values, among other topics.
In summary, the course on Data Visualization with Numpy and Pandas is taught by Exam Turf instructors and focuses on providing participants with in-depth knowledge and skills in working with the Pandas and NumPy Python libraries. The course is designed for learners of all levels and covers essential topics such as matrix and vector manipulation and data wrangling. Participants can expect to gain proficiency in data science concepts and access detailed discussions on advanced toolkits and techniques such as Python, Azure, machine learning, and data analysis.
The NumPy for Data Scientist course is designed to teach numeric python for everyone. NumPy is a python package that allows for the computation and processing of multidimensional and single dimensional array elements. As data science continues to grow, data analysis libraries like NumPy, SciPy, and Pandas have become increasingly popular. Python’s easy syntax makes it a top choice for data scientists. NumPy provides an efficient way to handle large amounts of data, particularly with matrix multiplication and data reshaping. It is also fast, making it ideal for working with large datasets.
NumPy offers several advantages for data analysis, such as array-oriented computing, efficient implementation of multidimensional arrays, scientific computations, and the ability to perform Fourier Transform and reshape data stored in multidimensional arrays. Additionally, it has built-in functions for linear algebra and random number generation. NumPy is often used in combination with SciPy and Matplotlib as a replacement for MATLAB due to Python’s completeness and ease of use.
The course is divided into several sections, including an introduction to NumPy, array creation and processing, NumPy datatypes and array creations, bitwise operators and string functions in NumPy, mathematical and statistical functions, NumPy sorting, searching, copies, and views, NumPy matrix library and linear algebra, and the save function.
The 130+ Exercises – Python Programming – Data Science – Pandas course offers a way to test Python programming skills in data science, specifically in Pandas. The course includes 130 exercises with solutions, covering topics such as working with Series, DataFrames, and indexes, filtering and sorting data, mapping columns, and preparing data for machine learning models. The course is designed for individuals with basic knowledge in Python, NumPy, and Pandas, and is a useful tool for those seeking new challenges or preparing for interviews. The course is divided into sections of 10 exercises each, and includes a bonus section.
Python is the most wanted programming language according to the Stack Overflow Developer Survey 2021, with NumPy being the second most used tool in the “Other Frameworks and Libraries” category. Python has surpassed SQL to become the third most popular technology.
The course includes a Configuration section (optional), Tips, and Starter exercises, as well as sections for Exercises 1-130 and a Bonus section.
The Python NumPy for Absolute Beginners course is designed for individuals who wish to learn the fundamentals of Python NumPy for Data Science. The course is taught by Eftekher Husain. It features a collection of videos that provide in-depth yet simplified explanations of Python Numpy’s fundamental concepts. The videos include not only tutorials but also example walkthroughs that can help learners solve real-life situations and challenges in Data Science.
The boot camp covers several topics that are essential for understanding Python Numpy, such as quick tips, environment setup, Python Numpy basics, and Numpy array indexing. The course is suitable for absolute beginners and offers a seamless way to learn Python Numpy.
Overall, the Python NumPy for Absolute Beginners course comes highly recommended for individuals who want to learn how to use Python Numpy to create awesome programs that can help solve real-world situations. Anyone interested in learning about Python Numpy should consider enrolling in this course.
8. Python NumPy Library for Data Science by Sai Acuity Institute of Learning Pvt Ltd Enabling Learning Through Insight! (Udemy)
This course, titled “Python NumPy Library for Data Science,” is being offered by Sai Acuity Institute of Learning Pvt Ltd. The course aims to provide a comprehensive tutorial on NumPy for data science beginners. NumPy is a scientific library that supports large multidimensional array objects and various tools to work with them. It is a fundamental library in Python and is used by various other libraries such as Pandas, Matplotlib, and Scikit-learn.
Python lists and NumPy arrays differ in the way objects are stored in memory. Python objects are pointers to a memory location that stores details about the object, such as bytes and value. Python lists are essentially an array of pointers, which adds overhead in terms of memory and computation. In contrast, NumPy arrays contain only homogeneous elements, making them more efficient at storing and manipulating the array. NumPy arrays are preferred over Python lists when performing mathematical operations on a large amount of data.
The course content and sections include an introduction to NumPy, fundamentals, creating 1-dimensional and 2-dimensional arrays, comparison of NumPy with standard Python, indexing, subsetting, slicing, and iterating through arrays, execution speed in NumPy and standard Python lists, and file handling.
In summary, this course provides an ultimate tutorial on NumPy for data science beginners. It covers the basics of NumPy and highlights the differences between Python lists and NumPy arrays. It also includes essential NumPy operations and compares NumPy with standard Python. The course aims to equip data scientists and aspiring data science professionals with a solid grasp of NumPy and its use in Python.
This course, titled “Python Numpy: Machine Learning & Data Science Course,” is offered by Oak Academy and focuses on teaching Numpy Python. The course is designed to provide students with the necessary skills to start a career in data science and machine learning. Python is an essential skill for professionals in various industries, and this course offers hands-on examples and exercises to reinforce the concepts taught. The course covers topics such as datatypes, loops, conditional statements, functions, modules, and data science concepts. Additionally, the course delves into Numpy for data manipulation, linear algebra, and using Numpy in neural networks.
Data science is a growing field that offers numerous opportunities for professionals to analyze data and discover hidden patterns. Python is the most popular programming language for data science, and this course teaches students how to use Numpy, which is a library for Python that adds support for large, multi-dimensional arrays and matrices. In addition to providing a collection of high-level mathematical functions to operate on these arrays, Numpy forms the foundation of the machine learning stack. The course aims to teach students how to use Python in linear algebra, neural network concepts, and powerful machine learning algorithms.
The course covers a broad range of topics, including how to use Anaconda and Jupyter notebook, fundamentals of Python, datatypes, operators, methods, conditional statements, loops and control statements, functions, modules, data science, and data literacy concepts. Additionally, the course covers the fundamentals of Numpy, including Numpy arrays and their features, Numpy functions, Numexpr module, indexing and slicing on arrays, linear algebra, and using Numpy in neural networks. The course also includes exercises and a final project on neural networks using Numpy.
To become a data scientist, individuals need to have a strong understanding of statistical analysis and mathematics, knowledge of machine learning, familiarity with databases, and programming skills. Python is the dominant programming language in data science, and familiarity with Python and SQL is essential.
The course entitled “Python with NumPy for Absolute Beginners” is instructed by Surendra Varma. The course aims to teach the basics of NumPy, an external library in Python used for complex mathematical operations. It uses multi-dimensional array objects to overcome slower executions and has built-in functions for manipulating arrays. NumPy has a wide range of applications in different sectors, such as Data Science, Data Analysis, and Machine Learning. It is also a base for other Python libraries that use the functionalities in NumPy to enhance their capabilities.
The course covers various topics, including creating, accessing, and slicing arrays, iterating through arrays, joining and splitting arrays, sorting, searching, and filtering arrays, and generating random arrays using NumPy in Python. The course is suitable for absolute beginners who want to learn the majority of concepts of the numerical Python library.
Arrays in NumPy are homogenous sets of elements that are equivalent to lists in Python. The homogeneity of NumPy arrays differentiates them from Python arrays, making them more suitable for mathematical operations. The library provides a large number of functions applicable to these arrays, which could not be performed when applied to Python arrays due to their heterogeneous nature.
The course content includes sections on understanding the differences between lists and NumPy arrays, functions in Python, using the NumPy module in Python, and quizzes. The course aims to provide a comprehensive understanding of NumPy through simple videos.