Data is everywhere, including your social media preferences and the billions of questions that Google processes daily. However, almost every activity has the potential to be a data point that may be used to shed light on people and life as a whole. All this is nothing but data science!
This article explores the use of Ruby on Rails for data processing and visualization using a real-world example of analytics.
It can be quite challenging to deal with raw data because the human brain did not evolve to handle such huge, complex information. Making data actionable and competing successfully at all levels of modern economic life depends on effective visualization. As a fantastic tool for full-stack development, Ruby on Rails is also amazingly adaptable. It can be applied as a backend development solution to create successful, user-friendly APIs.
The emergence of Data Science
The emergence of the field of data science and its quick ascent to prominence are two effects of the information revolution. Although gathering data is not a new endeavor, it used to be as easy as seeing manufacturing workers at work and developing strategies to expedite each procedure.
Today, data science impacts almost every industry, from high-level planning and internal management to advertising. Few individuals, though, are masters at gathering, evaluating, or even understanding purely informational material. In essence, data visualization is taking something exceedingly complex and making it understandable.
This may appear to be performing the same thing in a new method, but the way the human brain processes information increases efficiency. Learning data visualization with a comprehensive data science course, opens the door to more efficient operations at all business and programming levels.
What Are the Benefits of Data Visualization?
When marketing professionals promote video content or suggest setting up a business account on a platform like TikTok, they frequently cite statistics. Over 90% of the information the brain processes is visual, and that visual information is processed 60,000 times more quickly than written information are two of the most compelling examples.
While a computer might process information faster than a human could fathom, the same person might process information in a visual style just as quickly. In reality, patterns rather than specific data points determine how relevant a data set is. Given this, the true value of data visualization is significantly larger than the 60,000 times figure because it presents generalized, useful trends immediately.
Any business that uses data can benefit from this benefit. Effective visualization benefits everyone, including middle managers, developers, marketers, and business strategists.
What Are the Popular Data Visualization Tools?
While pie charts and heatmaps are examples of data visualization Techniques, a tool is a software that developers and data scientists use to display data. Although it's a relatively simple illustration, Microsoft Excel already has these characteristics with the various charts and sheets it provides. Similarly, Google Charts is a popular, easily accessible tool for visualizing data.
Tableau and Zoho Analytics are more potent tools for data visualization. Each tool has a distinct attraction, such as Tableau's amazing scalability and capacity to handle data from countless sources. While Zoho Analytics implements capabilities that make it suited explicitly to presenting business data, it finds a broad niche in the market.
However, while the tools above are excellent for non-developers, developers may use many other tools to visualize data, including ChartJS, Plotly, Highcharts, and many others.
With this, with a responsive framework like VueJS or React, developers may significantly modify the user experience.
Popular data visualization tools typically have a lot in common despite their variances. Supporting a variety of data presentation methods is essential since different techniques are suited to different data kinds and operational purposes. Systems must handle heavy workloads and keep up with growth effectively. Therefore scalability is also crucial. This refers to both resilience to anticipated future demands and the scalability of existing needs. Any instrument that aims to simplify the difficulty must be easy to use. Master the data visualization tools by joining the data science course online available at an affordable price.
Making APIs That Make the Complex Simple
It takes several steps to create an API that provides complex information in an easy-to-understand manner. The original data must, first and foremost, be trustworthy and useful to the user. The API must then be able to quickly and efficiently pull data from various sources. The solution must be adaptable to your organization's workflow and communication requirements.
It is feasible to create an appropriate API to communicate complicated, rich data by keeping these needs in mind. However, other applications give programmers a mechanism they may use to create simple, attractive data presentation systems.
Use of Ruby on Rails
Instead of starting from scratch, it might be wise to use Ruby on Rails to integrate with organizations like Coinbase, Twitch, and Shopify. It is a full-stack development framework created specifically with programmers' needs and outstanding results in mind.
Reasons to Use Ruby on Rails for Data Science
Thanks to the way it capitalizes on the advantages of the language, Rails is a well-liked application for Ruby. In general, Ruby is used by developers to design sophisticated, user-friendly websites swiftly. It provides tools to make many of the most typical development projects today easier to complete and a straightforward prototype method that appeals to both large corporations and cash-strapped startups. SaaS services, eCommerce sites, stock exchanges, and other applications that use sophisticated RoR help dynamic information flourish.
Don't Repeat Yourself (DRY) and Convention Over Configuration are the two guiding concepts that make Ruby on Rails simple and effective. By reducing repetition, the DRY programming concept aims to increase the consistency and integrity of code. On the other hand, emphasizes convention Over Preparation that Rails come pre-configured and is ready to use without extensive configuration.
Data science and Ruby on Rails
Rails is divided into numerous opt-in modules to address various highly specialized issues, despite its monolithic origins. This built-in module is extremely extendable by utilizing a large ecosystem of Ruby gems.
A few of them are helpful for presenting this data as a REST API and for data science applications.
ActiveModel and ActiveRecord
These two modules comprise Rails' ORM layer and support a wide range of SQL databases.
It can connect to an existing schema, automatically recognizing the table structure and producing automatic helper methods and query functions based on the SQL table. It may also handle the development and maintenance of databases (via migrations).
Scopes of Active Records
Writing queries and organizing them in a fairly DRY manner are both made possible by active records. Scopes are composable techniques used to join together brief pieces of queries.
ActiveRecord contains a library called Arel Arel. It's a DSL for writing SQL code in Ruby that enables object-oriented logic to be used around SQL queries and code composition and reuse. To increase code reuse, you can separate and reuse portions of queries or create custom methods that wrap up SQL code.
Arel can be used to create queries for other SQL-compatible big data or time series databases, such as Cassandra or Clickhouse (like TimeseriesDB, QuestDB)
Gems The developer experience can be improved by a variety of fantastic gems. Here are a few illustrations:
Gems like active queryable or Ransack are excellent for quickly converting API arguments to Model scopes.
The Prophet is excellent for predicting time series.
Rgeo assists with handling geographic data and maps in the ORM.
Here is a detailed illustration of what ActiveRecord and Arel can be used for:
To encompass the most popular filters we apply to our data, we construct a highly detailed list of scopes. A "view" for the wrapping scope that we specify is the often-used information.
Hope this article was helpful. If you want to learn data science from scratch, many best data science courses in India are offered by Learnbay. Sign up today and get started with a lucrative career.
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