What is Data Science and its applications?

Let’s first see the simple definition to understand what Data Science is?

What is Data Science?

Yes, Data Science is the study of data like website data, social media data, etc. To gain or extract insights and knowledge from any type of structured as well as unstructured data. Combination of different fields of work in statistics and probabilistic computation to interpret data for decision-making purposes.

People often mistake Data Science for Statistics and Data Mining. 

Let’s understand how Data Science is related to Statistics problems. Although these two areas combine similar skills and share common goals (like using a large amount of data to reach final conclusions), they are unique in one clear aspect. Data science, which is a newer field, is heavily based on the use of computers and technology. It accesses information from large databases, using some programming to manipulate data, and visualizes the data in a digital format.

Statistics, on the other hand, it is generally used the theories that are established before and focuses more on the inferences of hypothesis testing. It is a more traditional technique that has a population and we use sampling technique to select a sample from that population, or from a broad perspective, changed little over the last decades, while data science has essentially evolved with the rising use of internet technologies and computers.

How Data Science is related to Data Mining?

Data mining is a subset of data science. It is a process of semi-automatically analyzing large databases to find patterns that are: Valid: (hold on new data with some certainty), Novel: (non-obvious to the system), Useful:(should be possible to act on item), and Understandable:(human should be able to interpret the patterns). Data science covers the entire scope of data collection and processing.
A lot of different fields like Artificial Intelligence, Machine Learning, Deep learning come together to contribute to the term ‘Data Science’. As a result, there is a lot of confusion out there.

Role of Data Science

Data science is nearly used in every field of business industries, organizations, and agencies in the country and across the world, so there is certainly the chance for specialization. Many data scientists will be heavily specialized in business, often specific segments of the economy (such as automotive or insurance) or business-related fields like marketing or pricing.

Applications of Data Science:

Internet Search:

So one we speak of search, we think google!. Right, Many other search engines like Google, Yahoo, Bing, a search engine is used to find the best result for a search query in a fraction of seconds considering the fact that Google process is more than 22 bytes of data every day.

Digital Advertising:

Digital advertisement or targeted advertising. If you thought the search would have been the biggest application of data science and machine learning challenges to the digital marketing spectrum starting on the display of banners on various websites to the digital boards are the report almost all of them are decided by using data science that is the reason. Why does Ash have been able to get a lot of international advertisements? It based on uses past behavior is the reason, why I am not explaining, why my friend as an Apparels in the same place at the same time.

Website Recommendation:

Website recommendations, who can forget the suggestions about similar products on Amazon. if today I search for Sound System, wireless headphones and probably going to get a bunch of the headphones to compare with this recommender system does not only help you find relevant products from billions of product available with them but also as a lot of User experience a lot of companies have probably this entry system to promote their products in accordance with users interest and relevance of information in a joint like Amazon, Twitter, Google Play, Netflix, LinkedIn, IMDB, and many more use the system to Chrome User experience the recommendations are made based on a previous searches of users.

Image Recognition:

We upload images with friends on Facebook and we start getting suggestions to tell your friends. This automatic that suggestion feature using the speech recognition algorithm. Similarly while using WhatsApp web scan a barcode in your web browser using your mobile phone in addition Google provide during the option to search for images by uploading that reduces image recognition and provides it related searches.

Speech Recognition:

Some of the best examples of speech recognition products are the Google voice, Siri,  Cortana, using the speech recognition feature even if you want in position to type a message you like to stop simply speak out the message and it will be converted into text.

Airline Routing System:

We have an Airline route plan is known to have losses, due and provider companies struggling to maintain the Occupancy ratio and operating profit with a high rise in prices and to offer discounts to customers as per theme the situation was awarded for long. Whenever IT companies started using data science to identify the strategic areas of improvement plan using data files the airline companies in protecting rice daily decide which class of agreed to buy weather to directly land at a destination of the halls in between two examples of light can have a direct route from New Delhi to New York. Alternative leave can also choose to call any country and finally effective drive customer loyalty programs Southwest Airlines, Alaska Airlines, are among the top companies that use data science to bring changes in their working.

Fraud and Risk Detection:

The first applications of data science originated from finance. The discipline company is the head of bad debts and losses every year. How were the head alone data which used to get collected? During the initial people work will sanction loan to bring in data science practices in order to rescue them out losses banking companies to divide and conquer data via customer profiling fast expenditure on other essential valuables to analyze the probabilities of Ruskin default it also helps them for Sebastian product based on customer purchasing power data science is also used for marketing finance human resources Healthcare government policies and every possible industry weather gets generated in addition to predicting the wall shelf of a customer which customers' like it was customer should be the highest value product and many other questions in easy answer by data science.

Medical Image Analysis:

Journal of research in this area and one of the major studies in Big Data Analytics in Healthcare publish and Biomed Research International according to this study popular imaging techniques such as an x-ray computed tomography in so won numerous method have been used to tackle the difference in Maternity resolution and dimensions of these images many more open the lock to improve the image quality extract data from images more efficiently and provide the most accurate interpretation of the deep learning-based algorithms increase the Diagnostic accuracy by learning for the trees examples and then suggest better treatment solutions.

Genetics and Genomics:

Genomics in Genesis enable Zenith Bank level of treatment personalization to use MapReduce allows leading genetic sequences mapping and short and the time for efficient data processing the goal is to understand the impact of DNA on health and find individual biological connections between genetic diseases and response genomics remarkable impact on the prediction of molecular effects on genetic variation is essential to DNA interpretation and this would not have been possible without their database table the scientist to understand how genetic variation scanning package code.

Creation of Drugs:

The Discovery process is highly complicated and involved when it is the creator of inbound it in the testing should financial and time expenditure on average it takes 12 years to get a drug officially submitted the data science and machine learning algorithms simplify and short and this process academic perspective that from the initial screening of the compounds to the production of success rates based on biological factors that algorithm can forecast how the compound will act on a body using advanced mathematical Modelling and simulations instead of lab experiments the idea behind occupational drug Discovery is to create a computer model simulation as a biological development network cable find the prediction of future Outlook with high accuracy it allowed using which experiments should be done and incorporate all new information in a continuous learning look.

Virtual Assistance for the patient:

We have virtual assistants population and customers support the optimization of the chemical processes buildup on the concept that so many reasons. It is not actually necessary for patients to visit doctors in person using a mobile application can give a much more effective solution by bringing the top 10 to the patient instead of the year award mobile app can provide basic health care support usually as chatbot using this sentence or question and then receives information about medical condition the right form of wide network of LinkedIn sentence two causes at a reminder to take medicines on time and we assign an appointment to the doctor this approach promote a Healthy lifestyle by in consultation to make healthy dishes at the time on heating and life an appointment and allows doctors to focus on this machine learning algorithm and its natural language processing and generation to provide correct information to display the most appropriate customer support it created which of these is not fully rely on machine the Healthcare and that's why it uses the wire date of the earth that is available in the healthcare department.

Data Scientist Education requirements

• Programming Skills: R, Java, Python are some of the basic must-know languages.
• Knowledge of Big Data frameworks like Hadoop, spark, hive, and spark for distributed computation of data.
• Database Management System: A good knowledge in database Management systems
  Maths and Statistics.
• Data Visualizations: Tableau, R - GGPlot, Python libraries like – Matplotlib, Seaborn, etc.
• Machine Learning / Deep Learning Algorithms
• A good Team Member and Observer of Excellent Communication Skills to present your results and storytelling skills.