Watch Working with Big Data in Python

Working with Big Data in Python is a comprehensive course offered by Packt Publishing that delves into the intricacies of working with large sets of data using Python. The course provides an in-depth understanding of the various tools and libraries utilized to handle big data, making it an important asset for anyone who wants to master the art of big data processing.

The course is designed to cater to the needs of all levels of Python developers, right from beginners who are just starting with Python to those who already have some experience in working with Python programming. The instructor of the course is a data expert who takes you through the various concepts and tools needed to handle big data in Python, including data processing, cleaning, visualizing, and analyzing large datasets.

Working with Big Data in Python is a hands-on course that provides you with real-world examples from industry, making it easier for you to understand how big data can be used in practical applications. The course covers various big data frameworks such as Hadoop, Spark, and Dask, giving you a complete understanding of the big data ecosystem and how different tools fit together. The course also includes sections on machine learning, deep learning, and neural networks, providing you with an insight into how these techniques can be used to analyze and uncover hidden patterns within big data.

The course begins with an introduction to big data and its applications in various industries, including healthcare, finance, and retail. It then dives into Python programming basics, covering topics such as data types, functions, loops, and conditional statements. The course then covers Python libraries such as NumPy, pandas, matplotlib, and seaborn, providing an in-depth look at how they can be used to process, clean, and visualize large datasets.

The course then moves on to big data frameworks such as Hadoop and Spark, providing an overview of their architecture and how they can be used to process large datasets. The course covers Apache Hadoop components such as HDFS (Hadoop Distributed File System) and YARN (Yet Another Resource Negotiator). The course also covers Spark components such as Spark SQL, Spark Streaming, and Spark MLlib, providing an insight into how Spark can be used for big data processing and machine learning.

The course then covers Dask, a parallel computing library in Python designed to handle big datasets. The course provides an overview of Dask's architecture and how it can be used to process large datasets in parallel. The course also covers machine learning algorithms such as linear regression, decision trees, and random forests, providing an insight into how these techniques can be used to analyze and predict trends and patterns within big data.

The course then moves on to deep learning and neural networks, providing an overview of these techniques and how they can be used to analyze big data. The course covers popular deep learning frameworks such as TensorFlow and Keras, providing an insight into how these frameworks can be used to build deep learning models for big data.

Working with Big Data in Python is a comprehensive course that covers all aspects of big data processing using Python. The course provides an in-depth understanding of big data frameworks, libraries, and techniques, making it an important asset for anyone who wants to work with big data. The course is designed for all levels of Python developers and provides real-world examples from industry, making it easier for learners to apply the knowledge gained in practical applications. Overall, Working with Big Data in Python is an essential course for anyone interested in mastering the art of big data processing using Python.

Working with Big Data in Python is a series that ran for 1 seasons (19 episodes) between February 20, 2018 and on Packt Publishing