Data Science
A Girl using ipad and working with data | Image credit: chinnarach/Freepik

Data science and its applications in real-world

Author at TechGenyz Artificial Intelligence

Artificial Intelligence and Data Science are taking over the modern era. Fast-paced computing devices and game-changing evolutionary ideas have surrounded us, making the world a much better bubble to live in.
Artificial Intelligence and Data Science have enormous uses in the practical world and are solving multiple real-life challenges.

As experts have hailed Data Science as the ‘Hottest’ job of the present generation, it has attracted highly-skilled professionals who have pursued Data Science courses or have completed MSc in data analytics in India. These professionals are pushing Data Science’s implementation constantly in various fields through their innovative skills.

From healthcare to transport and from education to cooking, Data Science has changed our usage of specific technologies and decision-making abilities. Here we look at the four applications of Data Science in the real world.

1. Manufacturing

Data Scientists are the new factory workers. The manufacturing industry is using Data Science extensively to optimize production, reduce costs and boost profits. The advent of the Internet of Things (IoT) is a game-changer as Data Science has enabled companies to predict potential problems, monitor systems, and analyze the continuous stream of data.

Data science provides an impeccable customer-review analysis, helping the industries make better decisions and enhance their product quality. Automation is another valuable gift of Data Science to the manufacturing world. By using historical and real-time data, industries are developing autonomous systems that skyrocket manufacturing lines’ production. Machine learning technologies like reinforcement learning are at the core of powerful machines that have helped companies eliminate redundant jobs.

2. Transport

Data Science is making its mark in the transportation sector by creating a safer driving environment for drivers. Plus, it has optimized vehicle performance and has added greater autonomy to the drivers. Data science’s most effective application in the transport sector is the introduction of self-driving cars. Intelligent automobiles have become a reality thanks to reinforcement learning.

By analyzing facets like consumer profile, location, economic indicators, and logistics, Data Science has enabled vendors to optimize delivery routes and provide a proper allocation of resources.

Companies like Uber use powerful predictive tools to predict the fare price accurately.

3. Autocorrect and autocomplete

The Autocomplete, or word completion, feature predicts the rest of a word a user is typing. It is called predictive text in smartphones and has become a de facto feature used by almost all users. These predictive searches work on AI and use concepts such as natural language processing, machine learning, and deep learning.

The next word prediction for a particular user’s texting or typing saves time by understanding the person’s texting patterns. Virtual assistants also use this feature to complete specific sentences.

The ‘autocorrect’ is an automatic data validation function in word processors and text editing interfaces.

Based on AI methodologies, the autocorrect feature helps to achieve the best results while texting or typing to avoid incorrect statements or words. The feature automatically checks spellings and immediately corrects them to the nearest correct values.

4. Finance

The progress and advancements of Artificial Intelligence and Data Science in the field of finance is tremendous. Financial organizations use artificial neural network systems to detect charges or claims outside of the norm and flag them for human investigation. AI’s several applications in the finance world include Algorithmic Trading.

It involves using complex AI systems to make trading decisions at speeds higher than any human’s capability. AI technology has made it possible to automate processes to manage tasks such as understanding new rules and regulations or creating customized financial reports for individuals.

For example, IBM’s Watson can grasp complex regulations like additional reporting requirements of the Markets in Financial Instruments Directive and the Home Mortgage Disclosure Act.