Author List (in no particular order): Yash Wagh, Debbie Yuen, Elias Castro Hernandez, and Ikhlaq Sidhu
About (TL/DR): The following notebook introduces developers and data scientists to numerical analysis and data manipulation using NumPy. NumPy is the numerical analysis backbone to several popular open source analysis and machine learning packages.
Learning Goal(s): Fast and versatile, NumPy is the backbone to several machine learning, data science, and deep learning libraries. On it's own, NumPy is optimized C code that functions seamlessly with popular programing languages, such as Python, as well as legacy languages such as Fortran. This notebook an overview of the syntax and functionality of NumPy.
Associated Materials: None
Keywords (Tags): numpy, numpy-tutorial, numerical-analysis, ndarray, array-programming, array-broadcasting, numerical-python, data-x, uc-berkeley-engineering
Prerequisite Knowledge: (1) Python, (2) Matplotlib
Target User: Data scientists, applied machine learning engineers, and developers
Copyright: Content curation has been used to expedite the creation of the following learning materials. Credit and copyright belong to the content creators used in facilitating this content. Please support the creators of the resources used by frequenting their sites, and social media.
- m110_Intro_numerical_analysis_using_numpy -- NumPy structures and data manipulation.
- assets/homeworks/ -- Contains several exercises to help you master the material.
1) PART 1.1: INTRODUCING NUMPY ARRAYS
2) PART 1.2: ARRAY CONSTRUCTION - CREATING NUMPY ARRAYS
3) PART 1.3: MULTI-DIMENSIONAL ARRAYS
1) PART 2.1: ARRAY SLICING AND INDEXING]
2) PART 2.2: ARRAY OPERATIONS
3) PART 2.3 (OPTIONAL) NUMPY ARRAY VS PYTHON LIST
4) PART 2.4 (OPTIONAL) ARRAY BROADCASTING
5) PART 2.5 (OPTIONAL) NUMPY APPLICATION - PREDICTION USING ORDINARY LEAST SQUARES
There is much more than can be done with NumPy. Wanting to learn how NumPy is used in the development cycle for neural networks, or how it's used for rapid data preprocessing? Visit the Data-X website to learn more, or use the following links to curated topics of interest:
INTRODUCTION TO DATA ANALYSIS USING PANDAS (M120) Introduces syntax, structures, and manipulation operations for Pandas.
INTRODUCTION TO DATA VISUALIZATION USING MATPLOTLIB + SEABORN (m130): url needed Covers the process of using DataFrames to create engaging visualizations
SYNTHETIC DATA GENERATION USING PANDAS (m190): url needed ( Covers data augmentation (creating statistically valid data), and data correction (using ML to fill-in missing data)
