Learn Numpy, pandas, and pyspark for ETL testing from scratc

Learn Numpy, pandas, and pyspark for ETL testing from scratc udemy course

Numpy, Pandas, Pyspark for ETL and Machine Learning

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: Introduction

This section is an introduction to this course and Udemy.

Section 2: Getting started with Python and NumPy

This section covers initial Python and NumPy Installations and configurations and initial lessons about NumPy.

Section 3: Introduction to NumPy Attributes

In 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_rng

Section 5: NumPy Array Indexing and Slicing

This section describes NumPy Indexing and slicing in 1D, 2D, 3d and modifying array elements

Section 6: NumPy Operations and Broadcasting and filtering

This section covers basic operations in NumPy

Section 7: NumPy Reshaping and combining Arrays

This 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 Algebra

This section covers functions in NumPy related to Linear Algebra such as Determinant, Inverse, Eigenvalues and Eigenvectors

Section 9: NumPy and statistics

This section covers statistics in NumPy such as Normal, Uniform, Binomial, and Poisson distribution.

Section 10: PySpark

This section covers a starting point for PySpark and its functions for ETL testing

Section 11: Pandas

This section covers a starting point for learning Pandas