Data collection and analysis have become mandatory skills for many professions and businesses. In this article, we will talk about three tools that cover all the steps of working with data. Best of all, these tools are free! They simplify and accelerate the extraction of information, like Scrapy, which offer simple yet powerful assistance. To reap the most benefits from gathered data, we will overview KNIME, a tool that analyzes and systematizes information. Last but not least, we will dive into Tableau Public, a great data visualization platform with a user-friendly interface.
Let’s learn more about these tools to simplify our future data analysis tasks. However, if you’d like to dig deeper into data collection in general, check out Smartproxy blog first.
Coding is a skill that is valuable in many workplaces. There are a plethora of data analysis courses that teach the basics of Python, which is the most popular, beginner-friendly programming language. Understanding these fundamentals allows you to use Scrapy – a free open-source data extraction framework written in Python.
Scrapy allows collecting data from the websites. Initially designed for web scraping, it can also be used to extract data with APIs. Such a tool brings value on many levels: learning to set predetermined rules for a scraper teaches user interest and engagement. With a bit of training, you can give specific orders and let Scrapy do the job for you. It is a simple yet fast and powerful tool that you can use for a wide array of useful applications.
The beauty of open-source tools, such as Scrapy, is their incredible community. Reddit and Stack Overflow communities do a great job answering the questions of beginner Scrapy users or new coders in general. There is also an IRC channel that allows you to communicate with other users and developers. Finally, enthusiastic users have a chance to contribute to the further development of Scrapy.
To fulfill the users’ needs, the framework is extensible by design. Users can attach new, flexible functions without affecting its base.
Scrapy is a great tool for both beginners and advanced users. With enough training and support from its great community, anyone can use it effectively to extract desired data.
Even the relatively small amounts of data are often misinterpreted and mismanaged. So it’s important to analyze and systemize collected data properly, and KNIME is a great tool for that. With it, you can study extracted data to identify tendencies better and draw conclusions.
KNIME allows users to create data workflows to visualize and separate data analysis steps. After a selective execution, these nodes present processed results, deviations, or potential visualizations. Breaking down analysis into steps helps you to see the potential in your data, predict future changes, and make timely adjustments.
Tutorials and workflow examples show KNIME’s true potential and possible applications. However, there is no better way to learn to use it than to test it yourself. Indeed, KNIME analytics platform brings a much more efficient and ergonomic approach to data analysis.
Suggested Reading: The role of data analytics in digital marketing and advertising
Attractive presentation of the analyzed data to convey a proper message is an underrated skill. Visualizing information makes it easy to interpret and understand. There are many data visualization software with attractive features. Unfortunately, not all of them properly handle huge amounts of information. Luckily, Tableau Public solves this problem.
Learning to use Tableau’s functions offers a much more impressive alternative to basic data presentation. You can create visualizations that are beautiful but easy to understand. This makes your analyzed data even more engaging. Moreover, this platform encourages people to share their work!
Many tutorials can help you to understand Tableau Public tools, and dabbling with the interface is a good way to practice for future projects. You can also enjoy a public gallery of the best visualizations.
KNIME users love the data modeling part of the platform but switch to Tableau Public for proper visualization. Because so many data analysts prefer this shift, there is a way to connect these tools. Utilizing them both in the same environment can make data analysis even more comfortable.
Combining these three free tools makes data analysis a much more efficient and pleasant experience. Creating practice projects to go through all the data analysis steps helps the user realize the potential and usefulness of the information.
Every tool we have discussed teaches valuable user skills. Scrapy shows how you can extract data knowing just the basics of Python programming.
Data modeling with KNIME encourages the user to improve their analytical skills. Tableau Public helps users to explore their creative side and attract people’s attention with visualized data.