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The Importance of Machine Learning for Data Scientists

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3 minutes, 7 seconds read

The concept of Machine Learning, Artificial Intelligence (AI), Big Data has been around for a while. But the ability to apply algorithms and mathematical calculations to big data is gathering momentum only recently.     

In this article we will discuss the importance of Machine Learning and why every Data Scientist must master it.

What is Machine Learning?

Simply put, we’re contributing to Machine Learning through our day to day interactions on the internet. Whether you search your coffee maker on Amazon, “top tips to lose weight” In Google, or “friends” in Facebook you see Machine Learning in action, but you don’t realize it.

It is the Machine Learning technology that lets Google, Amazon, and Facebook search engine offer relevant recommendations to the user.

These companies are able to keep tabs on your day to day activity, search behavior and shopping preference with the help of ML technology.

Machine Learning is also one of the main components of Artificial Intelligence.

Who is a Data Scientist?

Before assessing the importance of Machine Learning for Data Scientists, here’s a brief note on who Data Scientists are. We’ll also discuss how one can become a Data Scientist.

Data Scientists draw meaningful information from a huge volume of data. They identify patterns and help build tools like AI-powered chatbots, CRMs, etc. to automate certain processes in a company.

With a sound knowledge of different Machine Learning techniques and contemporary technologies like Python, SAS, R, and SQL/NoSQL database, Data Scientists perform in-depth statistical analysis.

The role of Data Scientist might sound like that of Data Analyst, but, in fact, they are different.

Difference between a Data Scientist and a Data Analyst

  • Data scientist predicts future based on past patterns. Whereas, a Data Analyst curates meaningful insights from data.
  • Data scientist’s work involves “estimation” (or prediction) unknown facts; while an analyst investigates the known facts.
  • Data Analyst’s job is more geared towards businesses. Data Scientists’ work is integral to innovations and technological advances.

Why Machine Learning is So Important for a Data Scientist?

In a near future, process automation will superimpose most of the human-work in manufacturing. To match human capabilities, devices need to be intelligent and Machine Learning is at the core of AI.

Data Scientists must understand Machine Learning for quality predictions and estimations. This can help machines to take right decisions and smarter actions in real time with zero human intervention.

Machine Learning is transforming how data mining and interpretation work. It has replaced traditional statistical techniques with the more accurate automatic sets of generic methods. 

Hence it is imperative for Data Scientists to acquire skills at Machine Learning.

4 Must Have Skills Required to Become a Machine Learning Expert

To become an expert at Machine Learning every Data Scientists must have the following 4 skills.

  1. Thorough knowledge and expertise in computer fundamentals. For example, computer organization, system architecture and layers, and application software.
  2. Knowledge of probability is very important because Data Scientists’ work involves a lot of estimation. Analyzing statistics is another area that they need to focus on.
  3. Data modeling for analyzing various data objects and how they interact with each other.
  4. Programming skills and a sound knowledge of programming languages like python and R. A quest for learning new database languages like NoSQL apart from traditional SQL and Oracle.

Conclusion

Data is the new oil.

IBM predicts that the global demand for Data Scientists will rise 28% by 2020. Finance, Insurance, Professional services and IT sectors will cover 59% of the Data Science and Analytics job demand.

In the coming future, Machine Learning is going to be one of the best solutions to analyze high volumes of data. Therefore, Data Scientists must acquire an in-depth knowledge of Machine Learning to boost their productivity.   

This article is contributed to Mantra Labs by Jenny Hayat. Jenny is an established blogger and content writer for business, career, education, investment, money-making ideas and more.

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Wearable Technology: Tracking Health in Real-Time

Imagine a wearable device that can predict health emergencies before they occur or one that can analyze your dietary habits and offer personalized nutrition recommendations. With continued innovation, wearable technology is revolutionizing preventive healthcare and empowering individuals to take control of their health like never before. According to Statista, the global market for wearables is poised to skyrocket to $62.82 billion by 2025, driven by a surging demand for health and fitness tracking devices. Coupled with advancements in AR and VR technology, wearables are on the brink of a revolution, with IDC predicting global shipments of AR and VR headsets to soar to 39.2 million units by 2023.

Wearable technology is becoming a game-changer in healthcare, helping individuals to monitor their health in real-time. From fitness trackers to smartwatches, these devices offer an array of features designed to enhance wellness and provide valuable insights into one’s physical condition. Seamlessly integrating into daily life, wearables are putting the power of health management directly into the hands of consumers like never before.

Utilizing Wearable Technology for Health and Wellness

Wearable technology offers a wide range of applications in promoting health and wellness, with various use cases spanning fitness tracking, disease management, remote patient monitoring, and more. Let’s explore some of the key scenarios where wearable devices play a pivotal role:

Fitness Tracking and Optimization:

  • Marathon runners can monitor their heart rate, mileage, pace, and route using fitness trackers, enabling them to optimize their training regimen for better performance.
  • Individuals on a weight loss journey can track their daily activity and calorie expenditure, allowing them to adjust their diet and exercise routine accordingly for sustainable weight loss. Additionally, sleep tracking features can help users understand the impact of sleep quality on their overall health and fitness goals.

Health Monitoring and Disease Management:

  • Diabetic patients can use wearable glucose monitors to continuously track their blood sugar levels, receive real-time alerts for high or low glucose levels, and manage their condition effectively. This continuous monitoring reduces the need for frequent finger pricks and empowers individuals to make timely adjustments to their insulin dosage or diet.
  • Individuals with chronic conditions such as hypertension or respiratory disorders can monitor key health metrics like blood pressure or oxygen saturation, enabling early detection of abnormalities and proactive intervention. For example, wearable devices equipped with SpO2 sensors can detect episodes of low oxygen levels, alerting users to potential respiratory issues or sleep apnea.

Telemedicine and Remote Patient Monitoring:

  • Wearable devices equipped with fall detection sensors provide peace of mind for seniors and their caregivers, automatically alerting designated contacts or emergency services in the event of a fall. This feature is particularly valuable for elderly individuals living alone or those with mobility issues, ensuring prompt assistance in case of emergencies. 
  • Healthcare providers can remotely monitor patients’ health status and track their progress using wearable devices, facilitating virtual consultations and reducing the need for frequent clinic visits, particularly in the context of the COVID-19 pandemic. Remote patient monitoring enables early intervention and personalized care delivery, improving patient outcomes and reducing healthcare costs.

How Wearable Technologies Can Revolutionize Risk Management

The integration of wearable technology into healthcare has significant implications for the insurance industry. By leveraging the wealth of data generated by wearable devices, insurers can gain valuable insights into individuals’ health and lifestyle habits, enabling them to offer more personalized insurance plans and pricing models. Wearable technology serves as a powerful tool for risk assessment and underwriting, allowing insurers to better understand an individual’s health status and risk profile. This enables them to tailor insurance policies to meet the specific needs of each customer, offering incentives for healthy behaviors and proactive health management.

  • Some insurance companies like John Hancock Vitality offer discounts or rewards to policyholders who use wearable devices to track physical activity levels, sleep patterns, and other health metrics.
  • Insurers can partner with healthcare providers to offer remote monitoring programs for individuals with chronic conditions, providing them with wearable devices equipped with health monitoring sensors.
  • Wearable technology facilitates early intervention and preventive care, ultimately reducing healthcare costs for insurers.

Challenges Ahead

While wearable technology offers significant benefits in healthcare, it also raises important considerations that need to be addressed:

Data Privacy and Security: As wearable devices collect sensitive health data, it’s crucial to ensure robust data privacy and security measures are in place to protect users’ information from unauthorized access or breaches. Encryption, secure authentication mechanisms, and adherence to regulatory standards such as GDPR and HIPAA are essential to safeguarding user privacy and maintaining trust in wearable technology.

Accuracy and Reliability: The accuracy and reliability of data collected by wearable devices are paramount, as inaccurate or unreliable information could lead to incorrect health assessments or decisions. Manufacturers must ensure that their devices undergo rigorous testing and validation to maintain high standards of accuracy. Calibration of sensors, regular software updates, and adherence to industry standards for data accuracy are essential to ensure reliable performance and user satisfaction.

Regulatory Compliance: Wearable technology in healthcare is subject to regulatory requirements and standards to ensure safety, efficacy, and data protection. Manufacturers and healthcare providers must comply with relevant regulations and guidelines to mitigate risks and ensure the quality of care. Obtaining regulatory approvals, conducting clinical trials, and adhering to standards such as FDA regulations for medical devices are critical steps in bringing wearable healthcare products to market and ensuring their safe and effective use.

The Future 

The future of wearable technology holds immense promise, with ongoing advancements in sensor technology, data analytics, and artificial intelligence. From fitness tracking to disease management and telemedicine, these devices have the potential to improve health outcomes, enhance patient engagement, and revolutionize the delivery of healthcare services.

Moreover, by embracing wearable technology, insurers can harness the power of data to offer more personalized insurance plans and pricing models, ultimately improving the health and well-being of their customers. Together, through the integration of smart, wearable devices, we can build a healthier, more resilient future for all.

Further Reading: How Smart Technology in the USA is Improving Efficiency in Healthcare

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