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Data Science – 6 Pointers on How Web Developers Can Leverage Data Analytics to Solve Problems

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6 Ways How Developers Can Leverage from Data Science
What are different ways website and mobile app developers can leverage data science to create solutions for real world problems?

In its simplest definition, data science is a method used to solve complex problems. This often involves a multidisciplinary blend of information extraction, data inference, program development, and application of technology.

In its technical description, data science is the practice of learning, mining, storing, moving and using data that comes in different forms. This includes troves of hard information, stored data, unstructured and semi-structured data such as text files, financial logs, multimedia forms, instruments ad sensors. Since this includes a diversity of data in huge volumes, simple BI tools are incapable of processing it. With data science, developers are able to use more complex procedures with analytical tools and advanced algorithms, to analyze this data and generate meaningful insights out of it. When used in creative ways, data science can build turn-key solutions and generate great business value for the user.

In this article, we will share 6 ways developers can leverage from data science in their programming and product building operations.

6 Ways How Developers Can Leverage from Data Science
What are different ways website and mobile app developers can leverage data science to create solutions for real-world problems?

Understanding User Behaviors

Developers use data science to discover important information on user behaviors. This includes, how users are interacting with a particular website? What are their most commonly visited pages? Which products do they buy the most? What improvements do they want on the website?

This allows developers to effectively build patterns in consumer behaviors and integrate self-learning programs in websites so customers get exactly what they want when they interact with it. This up-gradation process can be automated on regular intervals so the demands of customers are met without the developer needing anything to do.

Data Visualization

Another way developers use data science to their advantage is by producing visual data from raw data. With data science, developers are able to visualize data using different methods and data visualization tools such as d3.js, ggplot, Tableau, and Matplottlib. This allows them to easily convert complex results from different projects in a structured format that is easy to understand and present. As a result, organizations are able to manage data head-on and grasp all the insights that are necessary for acting on a particular business opportunity. Its gives a broader range of knowledge and builds the organization a competitive edge in the market.

Machine Learning

Learning data science helps developers attain a deeper understanding of machine learning and develop better algorithms for programs. Data science gives exposure to a range of learning such as neural networks, adversarial and reinforcement learning, decision trees, supervised machine learning and logistic regression to solve problems that are the core of algorithmic architectures. Machine learning uses large amounts of data sets which data science can help translate and transform. Developers that are skilled in data science make great resources for AI development in organizations.

Machine learning algorithm
Machine learning algorithm in action showing how it is possible for an artificial intelligence system to gather data for storing, sorting, measuring, and proper usage.

SQL Database/Coding

Since NoSQL and Hadoop are now a central component of data science, developers are able to code and solve complex SQL queries more effectively. With Structured Query Language (SQL) programmers can carry out complex operations for adding, extracting or deleting data from different databases. It also helps in executing analytical functions and transforming database structures more effectively. Learning data science gives developers the edge they need SQL, allowing them to access, write, present data in any database. SQL uses concise commands which saves the developers time by shortening the amount of coding for different queries.

Python Development

Data science shares a lot of benefits for Python coders and is very commonly practiced in their programming tasks. Due to its versatility, Python as a language has a close connection with data science and is used in several stages of data inference. Python developers use data science to understand and move different formats of data and import SQL tables straight into their coding. With data science, a Python developer can create datasets and search for almost any form of dataset required on Google.Python Development

Web App and Extension Development

Web app developers use data science for remembering user’s preferences and study them to understand their lifestyle. Once all this data is collected and translated, these apps become ideal private assistants, smart repositories and even trustworthy partners. Using datasets, web app developers can program these apps to give uses reminders on their daily activities, provide suggestions, and identify gaps to prevent harmful habits. With data science, the dawn of AI-based web apps has already begun as developers are using data-driven strategies and developing self-learning extension for websites. Many Magento 2 extensions are developed using data science for predictive analysis for the merchant like, frequently bought together products, frequently viewed together products, etc.

Conclusion

Data Science is not just about managing data, it’s about store it, moving it one place to another, and ultimately using it and produce clever things. With its innumerable benefits in development, we have shared 6 methods ways how developers can leverage from it and use it to derive scientific methods, improve their coding, design algorithms, refine data, and improve user experience through their web platforms. 

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