Numerical Computing in Python with NumPy

Uplatz offers this comprehensive course on Numerical Computing in Python with NumPy. It is a self-paced video course consisting of recorded lectures. You will be awarded Course Completion Certificate at the end of the course.

Python is a popular object-oriented programming language that is simple to learn and use. It can operate on a variety of operating systems, including Windows, Linux, and Mac, making it a popular choice in the Data Analytics field. You may work in the Data Science, Machine Learning, Building real-world applications with Python, Big Data Hadoop environment, AI applications, and analytics after completing this Advanced Python Programming course.

NumPy is a fundamental library for numerical computing in Python. It stands for "Numerical Python" and provides support for large, multi-dimensional arrays and matrices, along with a collection of mathematical functions to operate on these arrays. NumPy forms the foundation for many other Python data science libraries and is a core tool for scientific computing and data analysis.

Key features of NumPy:

1. N-dimensional Arrays: NumPy provides an array object called ndarray, which is a multi-dimensional container for homogenous data. These arrays can be 1-dimensional, 2-dimensional, or even more dimensions, and they allow efficient storage and manipulation of large datasets.

2. Array Operations: NumPy supports a wide range of element-wise array operations, making it easy to perform mathematical and logical operations on entire arrays without the need for explicit loops. This feature, known as vectorization, results in faster and more concise code.

3. Broadcasting: NumPy allows broadcasting, which is a mechanism to perform operations on arrays of different shapes. It automatically extends the smaller array to match the shape of the larger array, making it possible to perform element-wise operations on arrays of different dimensions.

4. Mathematical Functions: NumPy provides a vast collection of mathematical functions for array computations, including basic arithmetic, trigonometric, logarithmic, statistical, and linear algebra functions.

5. Integration with Other Libraries: NumPy integrates seamlessly with other data science and numerical computing libraries in Python, such as SciPy, Matplotlib, and pandas, creating a powerful ecosystem for data analysis, visualization, and scientific computing.

• Numerical Computing in Python with NumPy - all lectures 24:30:17

  • 1: 1.1 INTRODUCTION-TO-NUMPY 22:20
  • 2: 1.2 NUMPY TUTORIAL BASICS 17:06
  • 3: 1.3 NUMPY ATTRIBUTES AND FUNCTIONS 24:43
  • 4: 1.4 CREATING ARRAYS FROM EXISTING DATA 24:52
  • 5: 1.5 CREATING ARRAY FROM RANGES 28:44
  • 6: 1.6 INDEXING AND SLICING IN NUMPY 15:39
  • 7: 1.7 ADVANCED SLICING IN NUMPY 29:55
  • 8: 1.8 APPEND AND RESIZE FUNCTIONS 25:20
  • 9: 1.9.1 NDITER FUNCTION AND BROADCASTING 24:23
  • 10: 1.9.2 NUMPY BROADCASTING - part 1 10:42
  • 11: 1.9.3 NUMPY BROADCASTING - part 2 07:41
  • 12: 1.9.4 NUMPY BROADCASTING - part 3 07:12
  • 13: 1.10 NDITER FUNCTION 26:17
  • 14: 2.1 ARRAY MANIPULATION FUNCTIONS 29:19
  • 15: 2.2 NUMPY UNIQUE() 16:53
  • 16: 2.3.1 NUMPY DELETE() - part 1 10:24
  • 17: 2.3.2 NUMPY DELETE() - part 2 05:45
  • 18: 2.4 NUMPY INSERT FUNCTION 10:23
  • 19: 2.5 NUMPY RAVEL() SWAPAXES() 14:44
  • 20: 2.6.1 SPLIT FUNCTION 11:54
  • 21: 2.6.2 HSPLIT() 12:18
  • 22: 2.6.3 VSPLIT() 07:10
  • 23: 2.7 LEFT SHIFT AND RIGHT SHIFT FUNCTIONS 11:44
  • 24: 2.8 NUMPY TRIGONOMETRIC FUNCTIONS 14:40
  • 25: 2.9 NUMPY ROUND FUNCTIONS 14:16
  • 26: 2.10 NUMPY ARITHMETIC FUNCTIONS 07:43
  • 27: 2.11 NUMPY POWER AND RECIPROCAL FUNCTIONS 07:51
  • 28: 2.12 NUMPY POWER AND MOD FUNCTIONS 06:36
  • 29: 2.13 NUMPY IMAG() REAL() 08:07
  • 30: 3. NUMPY CONCATENATE() 07:51
  • 31: 4.1 NUMPY STATISTICAL FUNCTIONS - AMIN AND AMAX 06:22
  • 32: 4.2 NUMPY STATISTICAL FUNCTIONS - MEAN MEDIAN PTP() 22:43
  • 33: 4.3 NUMPY AVERAGE FUNCTION 21:25
  • 34: 5.0 NUMPY SORT SEARCH COUNTING FUNCTIONS 20:10
  • 35: 5.1 NUMPY SEARCH SORT ALGORITHMS 06:55
  • 36: 5.2 SORT() 06:11
  • 37: 5.3 NUMPY SORT FUNCTION 16:40
  • 38: 5.4 NUMPY ARGSORT() 07:12
  • 39: 5.5 NONZERO WHERE 14:24
  • 40: 5.6 EXTRACT 06:32
  • 41: 5.7 ARGMAX() AND ARGMIN() 07:35
  • 42: 6. BYTESWAP COPIES AND VIEWS 25:00
  • 43: 7.1 STRFUNCTIONS IN NUMPY 13:32
  • 44: 7.2 STRING FUNCTION IN NUMPY ADD() MULTIPLY() 05:59
  • 45: 7.3 NUMPY CENTER() 08:19
  • 46: 7.4 CAPITALIZE() CENTER() IN NUMPY 12:17
  • 47: 7.5 STRING FUNCTIONS 1 17:37
  • 48: 7.6 STRING FUNCTIONS 2 08:27
  • 49: 8. NUMPY MATRIX LIBRARY 18:25
  • 50: 9. NUMPY JOINING ARRAYS 21:01
  • 51: 10.1 LINEAR ALGEBRA 1 13:55
  • 52: 10.2 LINEAR ALGEBRA 2 13:39
  • 53: 10.3 LINEAR ALGEBRA 3 11:21
  • 54: 10.4 LINEAR ALGEBRA 4 06:46
  • 55: 10.5 LINEAR ALGEBRA 5 08:40
  • 56: 10.6 LINEAR ALGEBRA 6 16:45
  • 57: 10.7 LINEAR ALGEBRA 7 16:32
  • 58: 11.1 RANDOM MODULE 1 14:49
  • 59: 11.2 RANDOM MODULE 2 19:02
  • 60: 11.3 RANDOM MODULE 3 22:42
  • 61: 11.4 RANDOM MODULE 4 07:14
  • 62: 11.5 RANDOM MODULE 5 13:49
  • 63: 11.6 RANDOM MODULE 6 12:56
  • 64: 11.7 RANDOM MODULE 7 11:18
  • 65: 11.8 RANDOM MODULE 8 11:22
  • 66: 11.9 RANDOM MODULE 9 15:03
  • 67: 11.10 RANDOM MODULE 10 10:35
  • 68: 11.11 RANDOM MODULE 11 10:04
  • 69: 11.12 RANDOM MODULE 12 09:21
  • 70: 11.13 RANDOM MODULE 13 43:00
  • 71: 11.14 RANDOM MODULE 14 21:26
  • 72: 11.15 RANDOM MODULE 15 22:47
  • 73: 11.16 RANDOM MODULE 16 09:20
  • 74: 11.17 RANDOM MODULE 17 20:18
  • 75: 11.18 RANDOM MODULE 18 30:27
  • 76: 11.19 RANDOM MODULE 19 27:58
  • 77: 11.20 SECRETS MODULE 1 21:42
  • 78: 11.21 SECRETS MODULE 2 16:29
  • 79: 11.22 RANDOM MODULE UNIFORM FUNCTION 22:14
  • 80: 11.23 RANDOM MODULE GENERATE NUMBER EXCEPT K 13:57
  • 81: 11.24 SECRETS MODULE GENERATE TOKENS 08:29
  • 82: 11.25 RANDOM MODULE GENERATE BINARY STRING 21:46
  • 83: 12.1 NUMPY MODULE REVISE 1 15:53
  • 84: 12.2 NUMPY MODULE REVISE 2 12:36
  • 85: 12.3 NUMPY INDEXING 14:56
  • 86: 12.4 NUMPY BASIC OPERATIONS 14:41
  • 87: 12.5 UNARY OPERATORS IN NUMPY 08:41
  • 88: 12.6 BINARY OPERATORS IN NUMPY 11:24
  • 89: 12.7 UNIVERSAL OPERATORS IN NUMPY 11:57
  • 90: 12.8 NUMPY FILTER ARRAYS 16:35
  • 91: 13.1 NUMPY MODULE PROJECTS_1 21:24
  • 92: 13.2 NUMPY MODULE PROJECTS_2 19:26
  • 93: 13.3 NUMPY MODULE PROJECTS_3 19:44
  • 94: 13.4 NUMPY MODULE PROJECTS_4 10:07
  • 95: 13.5 NUMPY MODULE PROJECTS_5 15:37
  • 96: 13.6 NUMPY MODULE PROJECTS_6 22:12

Numerical Computing in Python with NumPy - Course Syllabus

Module 1: Getting Started with NumPy

• Introduction to NumPy and its features
• Installing NumPy and setting up the Python environment
• NumPy arrays: Creating, indexing, and slicing arrays
• Broadcasting: Understanding how NumPy handles array shapes
• Basic array operations: Arithmetic, aggregation, and element-wise functions

Module 2: Working with Multi-dimensional Arrays

• Multi-dimensional arrays and their properties
• Array reshaping and stacking
• Universal functions (ufuncs): Applying functions element-wise
• Array broadcasting: Understanding how broadcasting works
• Masked arrays: Handling missing or invalid data

Module 3: Advanced NumPy Operations

• Array manipulation: Concatenation, splitting, and resizing arrays
• Advanced array indexing and slicing techniques
• Fancy indexing: Selecting specific elements or subsets from arrays
• Linear algebra operations with NumPy: Dot products, matrix operations
• Statistical computing with NumPy: Mean, median, variance, and more

Module 4: Data Processing and Visualization with NumPy

• Reading and writing data using NumPy
• Introduction to NumPy's subpackage 'numpy.random'
• Simulation and sampling using random numbers
• Vectorized computation: Benefits of using NumPy over loops
• Basic data visualization with NumPy and Matplotlib

Everyone

Passion and determination to achieve your goals!

• Python Developer
• Lead Python Developer
• Data Scientist
• Machine Learning Engineer
• Python Developer API AWS
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• Senior Software Engineer (MongoDB/Python)
• Embedded Python Developer
• DevOps Engineer
• DevOps CI/CD Automation Engineer

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