Role of Machine Learning in Data Science

Machine Learning and Data Science

Our future relies on the technologies like machine learning and artificial intelligence which are currently soaring across the digital lake. From simple voice assistance to the complex predictive algorithm is the outcome of valuable data sets. All the advanced ML and AL models are nothing without the required data sources.

That’s where the role of a data scientist comes into play. The word learning in machine learning points toward the data. It means the designed algorithm is dependent on data leveraging training data sets as parameters. It is very helpful in ML techniques like naive Bayes, unsupervised and supervised clusters, regression models, etc.

A data scientist has the potential to fetch useful information from a massive unstructured information. They can modify, visualize through statistics, charts, and colors, interrelate, analyze, process, and do many more operations with the data.

They can accurately fetch small to small information hidden inside those data which is very useful for an organization in terms of improving process throughput, revenue, optimization, maintenance, and many more related tasks.

In the United States, it was recognized that most of organizations lack the data scientist talents in their work culture. They are looking for experts who know well how to treat large data sets through varieties of advanced machine learning algorithms for regression, classification, plotting, analyzing, and other related tasks in data science.

Currently, the programming languages like Python, R, and Java offer varieties of ML packages that are driving innovations and other sensitive works in the data-oriented industries. One can leverage the libraries like matplotlib, Mallet, NumPy, SciPy, TensorFlow, etc., for performing your work and visualizing the data in a required form.

Whether it is about making graphs, tables, statistics, etc., or taking useful insights- they will help. So machine learning in R, and Python, like languages is getting the first choice for beginners and professionals because they are getting everything they need for crunching and analyzing data.

In the stock market, ML is transforming the old ways where complex queries demand a huge infrastructure and workforce for execution. Even after getting it all, they were unable to deliver accurate results.

But, now machine learning-based data models process all the given data sets and provide us with the most favorable result leading us to accurate decision making. Thus, companies can process the data collected from historical stock marketing records and real-time trends to buy or sell future stocks. This way they can earn a good amount from the stock market among competitors.

Talking about the sports sector, team management is also dependent upon data science and machine learning like resources for creating strategies, plans, team allocation on the field, identifying opponent’s move, etc the information collected from past data sources such as a player’s current and past performances, home field advantage, a team’s record, etc.

You can see its live example in Germany winning FIFA 2014. They use the SAP business intelligence tool HANA and IBM’s machine learning tool for processing a huge amount of data gathered from multiple sources. They even used real-time data with trends and surveys to create the most accurate strategy for winning.

Similarly, the healthcare industry is using this information in managing and optimizing their work. Modern healthcare’s designed with advanced IoT devices transferring the entire patient’s information over the network. Thus, an administrator can monitor every patient and avail useful services from time to time.

The Electronic Health Records (EHRs) used by doctors is also storing all patient-related information in the real time. This way doctors can keep an eye on their patients’ providing them with the best prescription according to the observed behaviors.

The use of data is not limited to these EHRs; big medical organizations are also using it in their research. A few are involved in finding a cure for diseases like cancer by processing the data from the very past research, cancer patients’ information, behavior, etc., to identify anomalies and patterns. Tools like IBM Watson are also helping doctors in diagnosis, finding a cure, best decision-making, etc., with the power of data.

Moving on to the retail sector, the role of data science is very huge. You must have listened about the Amazon Go store recently launched. It is the first completely automated retail store, where a customer only has to grab the product and go away.

The used sensors and cameras are powerful enough to provide all the data to the AI-enabled admin panel. Once all the details are organized the billing cost will be automatically debited through the customers’ credit card.

The manufacturing sector is using data science in streamlining its operations and processes to achieve maximum throughput. With data, they can perform predictive maintenance, manage supply and distribution, market demand, process optimization, edge computing, PLCs, etc.

Thus, we can see how machine learning empowers data science to reduce human effort in most businesses and other useful areas. It has emerged as the most valuable asset for driving innovations on the planet through pattern recognition, predictions, analysis, and various efforts. Maybe one fine day we will be able to uncover the mysteries of this universe with this data.

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