After Scraping – Next Steps and Data Analysis

So far, we have learned how to scrape the web, analyze the data collected, and apply machine learning (ML) algorithms using Python programming.

This chapter will provide a basic introduction to some emerging concepts and technologies that are becoming crucial in the field. Being able to draw insights and knowledge from data is in high demand, and even supply-related scopes have been growing exponentially. From the fields of research to business, the importance of data has grown rapidly. Artificial intelligence (AI)-based systems, powered by ML logic, will continue to consume and generate more data.

In this chapter, we will learn about the following topics:

• What happens after scraping?
• Web requests
• Data processing
• Jobs and careers

Technical requirements

A web browser (Google Chrome or Mozilla Firefox) is required to explore the provided resources.

What happens after scraping?

Web scraping is a method of collecting and supplying quality data, and applying various techniques that depend on data. Any faculty or domain that involves data provides the opportunity to learn and grow as data collected from scraping is used for many other tasks such as analysis, reporting, dataset creation, and mining. Web-scraping-related techniques have always been dynamic and challenging, alongside the growth of web-based technologies.

Systems based on data, returning raw data, processed data, and visualization plots, in the form of images and videos, are growing by involving global audiences in multiple forms. Information technology (IT)-driven systems are core applications used in all industries, and Python programming has played a major role in the development and processing of data-related systems.

Earlier chapters of this book presented various steps that consolidated a few main concepts and actions, as listed here:

• Demand for data...

Web requests

Throughout the chapters of this book, the requests Python library has been used to establish communication between the code and the web. Plenty of Python libraries can be found at https://pypi.org/ if we search for ones similar to requests.

The following subsections list some Python libraries and technologies and provide brief introductions to them.

pycurl

The pycurl Python library (http://pycurl.io/) is a wrapper on top of the popular libcurl library. libcurl is one of the earliest Python libraries that was used to communicate with websites on the internet, based on the curl tool (also known as cURL).

curl (https://curl.se/) is a command-line tool that is used to connect and transfer data over the web. curl is the basis of network communication; it’s a core implementation that is used with the help of a wrapper across different operating systems (OSs), browsers, and machines that communicate with the internet. The curl command is machine-independent...

Data processing

Data processing, in the context of web scraping, refers to storing, handling, managing, and analyzing the data that is generated from scraping. In previous chapters of the book, we focused on the concept of effective and efficient scraping with code examples.

As the demand for data is growing, technologies are also evolving and adapting to new changes. Currently, as there has been a boom in AI/ML-based systems, there is competition to provide easy and quick solutions to problems without compromising on quality.

In the coming sections, we will introduce some technologies that help with data processing.

PySpark

The Python library for Apache Spark, pyspark (https://spark.apache.org/), is used to process and analyze data, especially of a large volume. Spark is a framework that is used to handle big data (data with variety, volume, and velocity) and is more effective than Hadoop (https://hadoop.apache.org/), a framework for parallel processing, scheduling, and...

Jobs and careers

In this book so far, we have covered various topics on Python programming and web scraping. We have learned different techniques, particularly focused on data, including searching, acquiring, mining, transforming, analyzing, and visualizing.

The availability of job opportunities that allow individuals to implement the learned skills while also acquiring additional and up-to-date ones serves as a strong motivation to continue to learn and develop their skills. Data-related careers offer attractive prospects globally, with excellent salaries offered.

In this section, we will provide a list of job titles related to web scraping and Python programming for your reference. This is especially relevant for those interested in developing data-related skills (such as data science and AI/ML), considering the current demand in the field.

Here are some job titles sourced from various job sites across the globe:

• Senior Python programmer
• Expert Python programmer...

Summary

In this chapter, we explored some of the hot technologies on the market and in the field of data science. Technological frameworks and tools are always evolving. Therefore, it is the developer’s duty to keep up with the latest updates in technologies.

Web scraping or data extraction is one of the core fields of data science, though data processing and analyzing tasks closely follow. Collecting, gathering, and storing data from target websites using scraping techniques used to be core aspects of web scraping. However, as there have been various breakthroughs in systems that interact with data, providing quality data that can be directly implemented in the systems, stored in formats required by the customer, applicable for real-time processing, and more are also to be considered.

Data or datasets are made available using APIs, through sites dedicated to providing tools for research, and more. Web scraping comes into play when we require specific data from a target...

Further reading

• Data science:
  • https://aws.amazon.com/what-is/data-science/
  • https://www.ibm.com/topics/data-science
• Apache Arrow: https://arrow.apache.org/docs/python/
• Apache Spark: https://spark.apache.org/docs/3.3.1/api/python/index.html
• Data science – career:
  • https://hbr.org/2012/10/data-scientist-the-sexiest-job-of-the-21st-century
  • https://www.coursera.org/articles/data-science-career
  • https://www.discoverdatascience.org/career-information/
  • https://www.montecarlodata.com/blog-the-future-of-big-data-analytics-and-data-science/
  • https://emeritus.org/in/learn/scope-of-data-analytics-in-the-future/
  • https://www.knowledgehut.com/blog/data-science/data-scientist-future
• Python concurrency:
  • https://docs.python.org/3/library/concurrent.futures.html
  • https://vegibit.com/how-to-perform-parallel-programming-with-pythons-concurrent-futures-library/
• AI and data:
  • https://www.mygreatlearning.com/blog/difference-data-science-machine-learning-ai/
  • https://www.forbes.com/sites/nishatalagala...