Sep 06, 2021
7 minutes read
Data science is a big data analysis process that involves collection, storage, cleaning, integration, analysis, and visualization of data. It uses advanced techniques from statistics, computer programming to extract meaningful information from the data. Data science has created an entirely new industry through its innovative approach of using data to understand customer behaviors, improve products and services through data-based decision making.
The increasing influence of the internet and technology has made it easy to carry out data analysis. Not only is there a vast amount of existing data available over the internet, but there are tools such as Python which can be used to harness that large chunk of information. Data science has become an essential part of many fields including medicine, business analytics, and public policy. The demand for Data scientists is increasing day by day and the above examples show how these data scientists are working to make human life better.
Data science is a discipline of computer science that has gained prominence in the recent past due to its ability to help organizations enable business insights from all available data which seems like an impossible task.
Data science has been viewed as the next big thing in technology by various companies like Google, Amazon, and IBM. These major conglomerates have employed this concept to give rise to a new breed of data scientists that specialize in building algorithms for analyzing large volumes of structured or unstructured data. It is being used widely today not only by companies but also by researchers, scientists, and analysts who rely on data to drive decision-making processes.
In short, data science is a branch of computer science that focuses on statistical analysis to extract meaningful information from raw data. It is considered the future of technology and will be used widely in many industries such as medicine, government, and business.
Data science, through its unique ability to solve problems involving large volumes of data that were previously out of reach, has brought about new insights into medical science. It has helped in identifying genetic mutations and the potential for drug development which was not possible earlier. Data mining and predictive modeling techniques have also made it possible to identify new trends in various diseases like cardiovascular and neurological disorders.
Related: Data science and machine learning in healthcare
Data science has also helped doctors in saving lives as it can be used to identify the onset of various diseases much before it shows up. The ability of data science to keep track of medical records and use them in a database has completely revolutionized how we fight health-related issues. Here are some of the major benefits of applying data science in healthcare:
Many big data analysis projects have been carried out on hospital records and clinical trials across the world. Data science was able to identify gene variants that may be responsible for some common diseases. This has led to the development of new drugs and screening techniques that were not possible earlier.
Data Science has been used widely in detecting drug resistance, allergies, and other related diseases. This has increased awareness among patients about the symptoms of a disease and helped them avail treatment early on thus saving many lives.
It also helps in detecting various complex human conditions that cannot be observed visually or through physical examination methods. One such condition is epilepsy that has been identified by analyzing brain waves and sleep patterns of patients. This has led to the discovery of new drugs for this condition.
Data science helps in dealing with big data by analyzing it using various machine learning algorithms such as neural networks and support vector machines that learn from the historical patterns of disease occurrence. These methods can be used to make predictions about future outbreaks or epidemics and help healthcare providers manage them effectively. This method is also helpful for diagnosing difficult cases, and identifying new biomarkers specific to the disease or its progression.
Data science can be used to identify various lifestyle diseases that are becoming common such as depression and obesity. This data can then be used for prevention campaigns or by doctors for early intervention in these types of diseases. Data science has also helped in identifying risk factors for many diseases like cardiovascular disease, stroke, oncology, etc.
The ability of data analysis to involve all available data sources has made it possible to provide personalized healthcare recommendations which are fairly accurate. This has helped in developing drugs that are targeted to the specific genetic makeup of the human body. It also helps in providing the most appropriate treatment for each patient that can be monitored in real-time and adjusted as per the need. This ensures that fewer patients are sent back to the hospital and medical expenditures are down.
Due to the availability of a large amount of health data it has become possible to develop medical devices which are more effective and offer specific assistance based on the patient’s unique needs. For example, Philips developed Hue lighting which changes its color based on heart rate tracking via the iPhone camera focus app. This has helped in providing assistance to patients who live alone and have irregular heartbeats.
In 2014, Google used data science to predict 3.3 million outbreaks of flu. Based on the search queries people made, they were able to detect that there was a high chance for flu outbreaks in various regions across the world including Hong Kong, Ireland, and Norway. This ability to predict flu outbreaks in an early stage could help health authorities plan and prevent epidemics by identifying the regions that would most likely be affected.
Researchers at Google Brain used deep learning to detect a correlation between low temperatures and people’s search queries for flu-related terms. Based on this they were able to predict that these regions would have a higher chance of flu infection.
Recently, a team working at the baylor college of Medicine used data science to identify genes that are responsible for neuropsychiatric disorders like autism and schizophrenia. They analyzed genome-wide single-nucleotide polymorphisms in over 1 million individuals using advanced machine learning techniques which allowed them to identify different genetic markers for different diseases. This will help researchers to develop new drugs that can be specifically targeted towards the identified cause of a disease.
In a similar study, researchers at the University of Washington developed a system using data science that could predict schizophrenia from DNA. They used data collected from a number of studies and were able to develop a model which was 95% accurate in predicting various neuropsychiatric disorders including autism, depression, and schizophrenia.
Data science is used in drug development processes like drug repurposing, finding new potential uses for existing drugs, or discovering safer alternatives to existing drugs. Data science helps in collecting all available data about a drug and analyzing it to find new indications for that particular drug. This is an expensive process but using advanced machine learning techniques allows researchers to save money and time in the process of finding the best alternative route for a drug.
Data science has been used in identifying new uses for existing drugs like aspirin and anesthetic lidocaine which has helped in saving millions of dollars. Similarly, identifying safer alternatives for existing drugs has also paved a way for safer drugs that can be used without side effects.
Data science is one of the hottest trends in the world right now and everyone wants to get their hands on this lucrative career option. This includes healthcare industry workers as well who want to use data science to better understand patient needs.
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