Data Science applications and examples

Data Science applications and examples Simply Data Science is the study of data. It is a blend of various tools, methods, algorithms and processes. Besides, it involves storing, managing and analyzing data to extract useful information from structured and unstructured data. It also includes machine learning principles to get insights from raw data. Today most companies have an unusual or large amount of data. It helps to use this huge data at the right place.

Data science brings in a lot of skills like mathematics, knowledge of business domain, statistics, etc. to help the companies in various ways. It also helps in predictive analysis and making important business decisions. It can be used for weather forecasting. Here it collects data from different sources to build models and predicts the occurrence of natural calamities.

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Before we start data collecting under Data Science, we need to analyze and understand the requirements of business problems.

Components of Data Science life cycle Data Science includes a life cycle where data needs to pass through for smooth functioning of any business project. There are different components of the Data Science life cycle. Such as; Business requirements Data discovery Data processing Data exploration Predictive modelling

Testing model Communicating results Furthermore, we will elaborate on the above life cycle to understand it better.

Business requirements: Before starting a Data Science project, it is necessary to understand the data requirements and issues. It needs to identify the objectives of the project to make valuable predictions. This knowledge will make it clear what to acquire and what to leave.

Data discovery: After understanding the project requirements, it’s time to gather data. Data mining is the best process to gather any kind of data from different sources. Here, we need to think about what kind of data we actually require? From where it has to pull out? How to obtain and store this data? All these things have to clear before we extract data.

Data exploration: Later, in this step, we have to extract useful data by the cleaning process. It is the most time-consuming step. It includes removing unnecessary data, preparing useful data from raw data, avoiding inconsistencies that affect here, etc.

There may be some missing values or mixed data that creates problems while exploration. So, it needs to be dealt with carefully.

Predictive modelling: Here, we need to determine the methods and techniques to build a model that shows relation between different variables. This phase of Data Science life cycle includes some tools for planning a model. These are SQL Analysis Services, R, and SAS. All these tools help in predictive modelling of the life cycle.

There are many tools available in the market but R is the best useful tool among them.

Testing Model: Next, we build some data sets for training and testing the models.

Here we will come to know whether the existing tools are sufficient to run the models or not. It needs to test the existing model to find out any discrepancies within. Lets See Data Science Training Communicating results: Finally, we came to the end of the project life cycle. Here it is important to evaluate the things that we have achieved from the start of this cycle. We need to communicate the results of our experiment to the stakeholders to find the success or failure of this process.

Data Science tools and techniques There are different kinds of tools and techniques evolving in it. The main feature of these tools is that it doesn’t require any programming languages to implement. It comes with predefined mathematical functions and algorithms. These tools also help in building Machine Learning models.

Some tech giants and start-up companies are working on building and developing many tools in a user-friendly manner. Since, it is a vast process it is not enough to use a single tool.

Furthermore, there are many tools used in various stages in this process. Such as; Apache Hadoop for data storage, Informatica PowerCenter for data analysis, Rapid Minor, DataRobot, Tableau and QlikView for data visualization and so on. In addition to these, there are Data warehousing tools such as; Amazon Redshift, Google BigQuery, MS Azure, etc. Data Analysis tools like Alteryx, Domino Data Lab, etc.

Next, we will discuss some techniques of Data Science. There are few techniques that it uses. Such as; Linear Regression, Test of hypothesis, Density estimation, Logistic Regression, Pattern Recognition, Clustering, Time series, Decision trees, Neural Networks, Support Vector Machine, etc. Moreover, there are Deep learning, Imputation, Game Theory, Survival analysis, Cross validation and so on. These are a few techniques that most Data Scientists use for their daily work. Whether it could be finding solutions to any business issue, preparing any model or tool.

Data Science applications in finance Most companies uses it in the field of Risk Management and its analysis. It is also used for managing customer portfolio for trend analysis. Here it uses Business Intelligence tools. There are some breathtaking applications in Data Science that support finance sector in data analysis. This is the best example of Data Science application.

Fraud Detection: Fraud detection is the most critical part of any financial industry.

In this area Data Science and AI are often used together. Even small malfunctions and glitches may lead to financial loss. Real time predictive analysis helps in the enhancement of fraud detection as well as Cyber Security. With the help of Data Science, the companies are providing their financial services effectively. This technology helps them to identify potential fraud transactions that held during the time of any activity. It helps to block the session in case detection of any unusual financial activity.

Data Management: Managing enormous amount of data is a challenging task for any finance specialist. He receives a large amount of data from various sources.

Most of the data is unstructured. Digitizing documents may resolve this issue somehow. But it is up-to some extent only.

Data Science allows to extract the real insights from this raw data using some techniques. Such as; data mining, text analysis, language processing, etc. This helps to take better decisions which give more profits and efficiency in return.

Risk Management: Risk management is one of the essential factors that impact the financial industry very much. The risk involves competitor’s behaviour, market

trends, politics, etc. Data Science provides extraordinary software for underwriting smart decisions. It helps in determining the creditworthiness of potential clients. It helps to review the customer data in various aspects and determine whether the customer is trustworthy.

Furthermore, there are some other applications that help financial industry are Data-based decision making, advanced customer service, personalization and so on. These factors help to implement Data Science in finance sector to provide seamless services with high safety.

Data Science examples The following things can be considered as the examples of Data Science. Such as; Identification and prediction of disease, Optimizing shipping and logistics routes in real-time, detection of frauds, healthcare recommendations, automating digital ads, etc.

Data Science helps these sectors in various ways. It provides service in more advances that keeps it growing with the need of every sector.

Finally, we go to know about Data Science applications and its examples from the above writing. Today Data Science is in most demand for every sector. As it applies Machine Learning techniques in different sectors to prove the worth of its use. Its fraud detection technique helps the financial industry many more. Its application to the healthcare sector is at its best in recommendations.

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