Selenium is a browser automation library. Most often used for testing web-applications, Selenium may be used for any task that requires automating interaction with the browser. This can include web-scraping.
The following tutorial will be a user-led guide of best practices for web-scraping using selenium. I have listed my 5 top tips that will help the user scrape any data they request as efficiently as possible, using as little code as possible.
To extract the Coronavirus global headlines from BBC news.
To follow this tutorial, you will need to:
pip install selenium
To begin our web scraping task, we must first navigate to the following page, ‘ https://www.bbc.co.uk/news’. This step can be achieved in as little as three lines of code. First we import the webdriver from selenium, create an instance of the chrome webdriver, and finally call the get method on the webdriver object named driver.
To make this code short and readable, the chromedriver executable can be placed in a user chosen folder. This destination can then be add to PATH under your environmental variables. The webdriver is then ready to go, simply using webdriver.Chrome() with no arguments passed to Chrome in the parentheses.
#data-science #machine-learning #selenium
If you accumulate data on which you base your decision-making as an organization, you should probably think about your data architecture and possible best practices.
If you accumulate data on which you base your decision-making as an organization, you most probably need to think about your data architecture and consider possible best practices. Gaining a competitive edge, remaining customer-centric to the greatest extent possible, and streamlining processes to get on-the-button outcomes can all be traced back to an organization’s capacity to build a future-ready data architecture.
In what follows, we offer a short overview of the overarching capabilities of data architecture. These include user-centricity, elasticity, robustness, and the capacity to ensure the seamless flow of data at all times. Added to these are automation enablement, plus security and data governance considerations. These points from our checklist for what we perceive to be an anticipatory analytics ecosystem.
#big data #data science #big data analytics #data analysis #data architecture #data transformation #data platform #data strategy #cloud data platform #data acquisition
Welcome to my Blog , In this article, you are going to learn the top 10 python tips and tricks.
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The opportunities big data offers also come with very real challenges that many organizations are facing today. Often, it’s finding the most cost-effective, scalable way to store and process boundless volumes of data in multiple formats that come from a growing number of sources. Then organizations need the analytical capabilities and flexibility to turn this data into insights that can meet their specific business objectives.
This Refcard dives into how a data lake helps tackle these challenges at both ends — from its enhanced architecture that’s designed for efficient data ingestion, storage, and management to its advanced analytics functionality and performance flexibility. You’ll also explore key benefits and common use cases.
As technology continues to evolve with new data sources, such as IoT sensors and social media churning out large volumes of data, there has never been a better time to discuss the possibilities and challenges of managing such data for varying analytical insights. In this Refcard, we dig deep into how data lakes solve the problem of storing and processing enormous amounts of data. While doing so, we also explore the benefits of data lakes, their use cases, and how they differ from data warehouses (DWHs).
This is a preview of the Getting Started With Data Lakes Refcard. To read the entire Refcard, please download the PDF from the link above.
#big data #data analytics #data analysis #business analytics #data warehouse #data storage #data lake #data lake architecture #data lake governance #data lake management
What exactly is Big Data? Big Data is nothing but large and complex data sets, which can be both structured and unstructured. Its concept encompasses the infrastructures, technologies, and Big Data Tools created to manage this large amount of information.
To fulfill the need to achieve high-performance, Big Data Analytics tools play a vital role. Further, various Big Data tools and frameworks are responsible for retrieving meaningful information from a huge set of data.
The most important as well as popular Big Data Analytics Open Source Tools which are used in 2020 are as follows:
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Companies across every industry rely on big data to make strategic decisions about their business, which is why data analyst roles are constantly in demand. Even as we transition to more automated data collection systems, data analysts remain a crucial piece in the data puzzle. Not only do they build the systems that extract and organize data, but they also make sense of it –– identifying patterns, trends, and formulating actionable insights.
If you think that an entry-level data analyst role might be right for you, you might be wondering what to focus on in the first 90 days on the job. What skills should you have going in and what should you focus on developing in order to advance in this career path?
Let’s take a look at the most important things you need to know.
#data #data-analytics #data-science #data-analysis #big-data-analytics #data-privacy #data-structures #good-company