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所在平台: Udemy |
课程主页: https://www.udemy.com/course/learn-numpy-pandas-and-pyspark-for-etl-testing-from-scratc/
课程评论：没有评论
本课程是一门完全实践的课程，旨在教授 NumPy、Pandas 和 PySpark。课程特别强调 NumPy，并设有专门的 PySpark 和 Pandas 部分，帮助初学者入门。本课程的设计目标是为 ETL 和机器学习工作做准备。 课程内容全面覆盖 NumPy，因为 NumPy 的概念与 PySpark 和 Pandas 相似，有助于更好地理解 Pandas 和 PySpark 中的 DataFrame。 课程还包含一个专门针对 PySpark 的章节，旨在帮助学习者克服在个人 Windows 电脑上入门 PySpark 所面临的主要挑战。 另一个专门的 Pandas 章节也将帮助学生入门并克服学习 Pandas 过程中可能遇到的主要挑战。 课程共包含 11 个章节：
This course will be a completely hands on course to learn NumPy, Pandas, and PySpark. There's going to be emphasis on NumPy and there will be an entire section on PySpark and Pandas to get you started. This course is designed to prepare for ETL and Machine Learning jobs.There's a complete coverage of NumPy because the concepts in NumPy are similar to PySpark and Pandas and will get you started to better understand DataFrames in Pandas and PySpark.There's an entire Section in this course about PySpark to help overcome the main challenges in getting started with PySpark in personal Windows Computer.There's an entire Section in this course about Pandas to get the student started and overcome the main challenges.There are 11 sections in this course. 9 sections are dedicated to Numpy as such:Section 1: IntroductionThis section is an introduction to this course and Udemy.Section 2: Getting started with Python and NumPyThis section covers initial Python and NumPy Installations and configurations and initial lessons about NumPy.Section 3: Introduction to NumPy AttributesIn This section NumPy Attributes are described such as shape, dtype, size and ndim.Section 4: NumPy Special Arrays.This section describes NumPy special Arrays such as eye, diag, random, default_rngSection 5: NumPy Array Indexing and SlicingThis section describes NumPy Indexing and slicing in 1D, 2D, 3d and modifying array elementsSection 6: NumPy Operations and Broadcasting and filteringThis section covers basic operations in NumPySection 7: NumPy Reshaping and combining ArraysThis section covers reshaping and combining Arrays using functions like reshape, flatten, ravel, transposing axes, concatenate, stack, vstack, npstack and hsplit, and vsplit.Section 8: NumPy and Linear AlgebraThis section covers functions in NumPy related to Linear Algebra such as Determinant, Inverse, Eigenvalues and EigenvectorsSection 9: NumPy and statisticsThis section covers statistics in NumPy such as Normal, Uniform, Binomial, and Poisson distribution.Section 10: PySparkThis section covers a starting point for PySpark and its functions for ETL testingSection 11: PandasThis section covers a starting point for learning Pandas
