What are the top Data Science applications in trending domains?
What is Data Science?
Data science is a discipline that combines domain knowledge, programming skills, and math and statistics knowledge to extract meaningful insights from data. Machine learning algorithms apply to numbers, text, images, video, audio, and other data to create artificial intelligence (AI) systems that perform tasks that would normally require human intelligence. As a result, these systems generate insights that analysts and business users can turn into financial value.
Why Data Science is Important?
More businesses are realising the value of Data Science, AI, and Machine Learning. Organizations that want to stay competitive in the age of big data must develop and implement data science capabilities efficiently, regardless of industry or size to use the full potential of the data.
Top Data Science Applications.
Fraud and Risk Detection
Advanced Image Recognition
Banks are one of the first industries to use data science. Every year, businesses face horrible debts and losses. However, there will be a need of collect lot of data during the initial paperwork for loan approval. Banks decide to hire data scientists to help them recover from their losses. Banking companies have learned to divide and conquer data over time using customer profiling, past expenditures, and other critical variables to assess risk and repay probabilities. Furthermore, data science assisted them in promoting their banking products based on the purchasing power of their customers.
1. Medical Image Analysis
Various methods and frameworks, such as MapReduce, are using in procedures such as tumour detection, artery stenosis, and organ delineation to find optimal parameters for tasks like lung texture classification. For solid texture classification, it uses machine learning techniques such as support vector machines (SVM), content-based medical image indexing, and wavelet analysis.
2. Genetics & Genomics
Through genetics and genomics research, Data Science applications also enable a higher level of treatment personalization. The goal is to discover individual biological connections between genetics, diseases, and drug response to better understand the impact of DNA on our health. Data science techniques enable the integration of various types of data with genomic data in disease research, allowing for a better understanding of genetic issues in drug and disease reactions. With the help of Data science doctors will gain a better understanding of human DNA. Advanced genetic risk prediction will be a significant step toward more personalized care.
3. Drug Development
The drug discovery process is highly complicated and involves many disciplines. The greatest ideas are often bounded by billions of testing results by huge financial and time expenditures. On average, it takes twelve years to make an official submission for the drug to reach the market.
Data science applications and machine learning algorithms simplify and shorten this process, adding a perspective to each step from the initial screening of drug compounds to the prediction of the success rate based on the biological factors. Such algorithms can forecast how the compound will act in the body using advanced mathematical modelling and simulations instead of the “lab experiments”.
Yahoo, Bing, Ask, AOL, and other all these search engines including Google use data science algorithms to deliver the best possible result for our searched query in a matter of seconds. Google processes over 20 petabytes of data every day.
From the digital marketing world, the display banners on various websites to the digital billboards at the travel stations and marts, almost everything decided by using data science algorithms. This is the reason digital ads have been able to get a much higher CTR (Call-Through-Rate) than traditional advertisements. Data science can target based on a user’s past behaviour.
Data science not only helps to find relevant products among billions of products on the market, but it also improves the user experience significantly. Many companies are using this search engine to promote their products based on the user’s interests and relevance of information. This system used by internet powerhouses such as Amazon, Twitter, Google, Netflix, LinkedIn, IMDb, and many others to improve the user experience. The recommendations are based on the user's previous search results.
Upload images with friends on Facebook and you will start getting suggestions to tag friends. This automatic tag suggestion feature uses a face recognition algorithm. In their latest update, Facebook has outlined the additional progress they have made in this area, making specific note of their advances in image recognition accuracy and capacity.
The best examples of speech recognition products are Google Voice, Siri, Cortana, etc. Using the speech-recognition feature, even if users are not able to type a text message, they can simply speak out the message and it will be converting to text.
Games are now gaining traction with the help of machine learning algorithms that improve and upgrade themselves as the player moves up to a higher level. In motion gaming as well, the opponent computer analyses previous moves and accordingly shapes up its game. EA Sports, Zynga, Sony, Nintendo, and Activision-Blizzard have led the gaming experience to the next level using data science.
Considering a VR headset contains computing knowledge, algorithms, and data to provide you with the best viewing experience. A very small step towards this is the high-trending game of Pokémon GO. The ability to walk around and look at Pokémon on walls, streets, and objects on the screen does not exist and designed to excite gamers.
Predictive analytics used by Tesla, Ford, Volkswagen, and many other leading automakers in their new wave of autonomous vehicles. Thousands of tiny cameras and sensors are useful in these cars to convey information in real-time. Self-driving cars can adjust to speed limits, avoid dangerous lane changes, and even take passengers on the shortest route using machine learning, predictive analytics, and data science. The AI Revolution in the Automobile Industry is Powered by Artificial Intelligence in Cars.
Data science is taking its turn in all possible directions. It is everywhere in the daily needs of humankind, fundamentally reshaping the way of life and the way we do business and governing the flow of social growth. Data science unlocks new eras in scientific research areas where theoretical data and practical data mutually support each other. This enables companies to understand the market flow better and can focus and design new strategies to outperform competitors, which drives the companies to focus more on innovations and work efficiently.