Data Science: A Next Generation Science

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     Data Science is the field of study that manages vast volumes of data by making use of modern tools and techniques to derive meaningful information, find unseen patterns and make important business decisions. It is an important part of any industry today as in almost all industries massive amounts of data are produced daily. The popularity of this domain has increased manifold and companies all over the globe have started implementing data science tools and techniques to grow their business. Data Science makes use of complex machine learning algorithms for building predictive models. The data that is used for analysis can be taken from various sources and can be presented in multiple formats. Thus, Data Science has also helped companies by providing increased customer satisfaction.

What is Data Science?

Data Science is a far-sighting approach which is a combination of various tools, techniques, algorithms and machine learning principles that is designed to make data prediction and decision-making simpler, faster and more accurate. In simpler words, Data Science is an interdisciplinary domain of study that makes use of data by working on large volumes of such data which may come from different types of sources such as financial logs, multimedia files, sensors and instruments, marketing forms, text files, etc. transform it into valuable outputs and resources to create business and IT related strategies.

Why do we need Data Science?

During the traditional period, the data that was used by companies was way too small in size and mostly structured and hence could easily be analyzed using simpler BI tools. However, with the technological changes taking place, this data increased tremendously and became unstructured and semi-structured due to the incapability of handling the data. This was because the data generated was now coming from different sources like financial logs, multimedia files, sensors and instruments, marketing forms, text files, etc. The simple BI tools were not capable enough of analyzing and processing this huge magnitude of data. This is when the need was felt for more complex and advanced analytical tools, techniques and algorithms for analyzing, processing and drawing insights. With Data Science coming in, all of these problems were solved and also it resulted in more accurate, precise, simple and faster data analysis and processing. Due to all the advantages Data Science has, more and more companies are realizing its importance and also many companies have already started using it for their business purposes. Now regardless of the industry size or business functioning, organizations that want to remain competitive in the market need to efficiently develop and start implementing data science capabilities for their businesses.

What can be done using Data Science?

Data Science manages vast volumes of data by utilizing modern tools and techniques to derive meaningful information, for predictive analysis, finding unseen patterns and making important business decisions. By making use of Data Science, one can:

  1. Perform exploratory study and analysis of any data. 
  2. Find the problems in any work and by asking the right questions can also find the leading cause of the problem. 
  3. Model any data using multiple algorithms. 
  4. Communicate and visualize the outputs using graphs, dashboards, charts, etc. 
  5. Decide what products to choose for better efficiency and life. 
  6. Offer any promotional offers or discounts on any product. 
  7. Build predictive analytical models for business forecasts.

How does Data Science work?

Data Science is the extraction of actionable outputs from raw data. It involves a variety of disciplines and expertise areas for producing holistic, refined and thorough-looking output data. Data Scientists are the people who are employed for the Data Science work. They need to be skilled in everything from mathematical modelling, statistical modelling, and data engineering to advanced computing and visualizations to be able to effectively work on the muddled masses of information and then process them into the vital bits that can help drive innovation and efficiency. Data Science makes use of various prerequisites like Artificial Intelligence, Machine Learning, and Deep Learning to create models, predict data and make useful insights using various tools, techniques and algorithms.
Data Science is a general lifecycle consisting of various stages which include:

  • Data Gathering:
    The most initial step in Data Science is data collection which could be in any form. This data is then fed into the system followed by the signal reception process and then data extraction.
  • Data Maintenance:
    After the data extraction is done the data needs to be properly stacked. Here data cleansing, data staging, data processing and data architecture take place while warehousing the data. 
  • Data Processing:
    Then comes the important step of data processing. Here Data Mining, Data clustering/classification, data modelling and data summarization take place.  
  • Data communication:
    Once the data processing is done, it is then to be sent ahead for decision-making. So here data reporting, data visualization, business intelligence and based on all decision-making take place. 
  • Data Analyzing:
    Here, after the decision-making is done exploratory or confirmatory analysis takes place. Even predictive analysis, regression, text mining, and qualitative analysis are carried out here. Here, most of the functioning is carried out using the Machine Learning algorithms.

All of the five stages involved here are then again and again repeated as it is a lifecycle process. And most importantly all of these stages make use of different techniques, tools, algorithms, programs and skill sets.

Use of Data Science

With the advent of Data Science, the data analysis and processing task has been greatly revolutionized. Almost everything that we make use of today, may it be any service or product has gone through some sort of Data Science process. Data Science has made it possible to achieve some of the major goals that either were impossible or way too difficult earlier. The uses of Data Science are multiple and some of them are mentioned below:

  • Forecasting in the sales revenue and customer retention department. 
  • Anomaly detection in the fields of crime, disease, object defect, etc.  
  • Automation and decision-making for background checks, creditworthiness, etc. 
  • Classifications in any email server, any object segregation, etc. 
  • Pattern detection in the case of financial market patterns, weather patterns, etc. 
  • Biometric detection i.e., recognition of face, voice, text, handwriting, etc. 
  • Recommendations based on learning preferences, recommendation engine referrals, etc.

Many industries today are making use of Data Science for varied purposes. Some of the sectors that have developed Data Science and have started implementing it are:

  • Logistics:
    Many companies like UPS turn to Data Science for maximizing profit and efficiency. They use data science-backed statistical modelling for delivery, driving, weather, construction, traffic, etc, forecasts.
  • Healthcare:
    Data Science has made several breakthroughs in the Healthcare sector. From personal fitness trackers to understanding diseases, practising preventive medicines, diagnosing diseases and treating them, Data Science has made a mark in the industry. 
  • Self-Driving Cars:
    Many automotive giants like Tesla, Ford, and Volkswagen have made predictive analyses in the creation of a new wave of self-driving cars. 
  • Entertainment:
    May it be Netflix, Amazon Prime, YouTube or any other entertainment field company, all have made use of Data Science to predict binge-watching. They now with the help of Data Science promote binge-watching using the recommendations technique of the Data Science. 
  • Finance:
    For many finance-related industries, the advent of Data Science has proved to be a boon. With the tools, techniques and algorithms that Data Science provides, the finance sector has saved millions and enormous amounts of time. 
  • Cybersecurity:
    In the Cybersecurity field. Data Science has made a great mark. They use Data Science techniques for malware detection, for any virus detection, sorting and elimination of them. Even in the future, Data Science can prove to be very essential in the safety and security sector.

Data Science has changed the lives of many by the tools, techniques and algorithms that it provides. The Data Science and related sectors over the last five years have generated millions of job vacancies and are on the verge of creating even more. The important job roles in the field are that of Data Scientist, Machine Learning Engineer, Data Consultant, and Data Analyst.

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