Skip to main content

Data Science: A Next Generation Science

Raw Data

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 the industries massive amounts of data are produced on daily basis. 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 of 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 the 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 with the goal 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 and 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. But with the technological changes taking place, this data increased tremendously and became unstructured and semi-structured due to the incapability in 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 analyzing and processing. Due to all the advantages Data Science had, 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 on any data. 
  2. Find the problems in any work and also 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, discounts on any product. 
  7. Build predictive analytical models for business forecast.

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 a 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, data engineering to advanced computing and visualizations so as 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, 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 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 takes 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 takes 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 takes place. 
  • Data Analyzing:
    Here, after the decision making is done exploratory or confirmatory analysis takes place. Even predictive analysis, regression, text mining, qualitative analysis is 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 skillsets.

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 have 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, credit worthiness, etc. 
  • Classifications in any email server, any object segregation, etc. 
  • Pattern detection in 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 engines referral, etc.

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

  • Logistics:
    Many companies like UPS turns to Data Science for maximizing profit and efficiency. They use data science backed statistical modelling for delivery, driving, weather, construction, traffic, etc, forecast.
     
  • Healthcare:
    Data Science has made up a number of breakthroughs in the Healthcare sector. From personal fitness trackers, to understanding diseases, practicing preventive medicines, diagnosing diseases and treating them, the Data Science has made a mark in the industry. 
  • Self-Driving Cars:
    Many automotive giants like Tesla, Ford, Volkswagen have made predictive analysis in the creation of 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 the binge watching. They now with the help of Data Science do promote binge watching using the recommendations technique of the Data Science. 
  • Finance:
    For many finances concerned industries, the advent of Data Science has proved to be boon. As with the tools, techniques and algorithms that the Data Science provides, the finance sector has saved millions and enormous amount of time. 
  • Cybersecurity:
    In Cybersecurity field. Data Science has made a great mark. They use the 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 lives of many by the tools, techniques and algorithms that it provides. The Data Science and related sector over the last five years have generated millions of job vacancies and is on the verge of creating even more. The important job roles in the field are that of Data Scientist, Machine Learning Engineer, Data Consultant, Data Analyst.

Comments

Popular posts from this blog

DNS: An Intermediate Resolver

The Domain Name System (DNS) is a centralized part of the internet that provides a way to match the names of the website that you are seeking to find to the address or number of the same website. It is a hierarchical naming system for web associated device such as computers, laptops, mobile phones, and services or other resources that are connected to the internet or any other private network. So, in short, Domain Name System associate domain names that are assigned to all the entities to the address of that entities and thus in a way to the information that is associated with that entity.

Coding: Roadmap For Beginners

          Coding is basically a process used for creating software instructions for computers using various programming languages. With the help of computer coding, we can program websites, apps and various other technologies that we interact with in our everyday life. In coding we use several languages to give a computer instruction based on which specific functions are performed by the programmed machines. There are various types of codes and each code has its specific function and then depending on what is to be developed the codes are programmed for those machines. All the popular technologies that we have today like Facebook, Instagram, Electric Vehicle, Robots, Smartphones, Browsers are all developed using some specific code.

Compiler: A Digital Conveter

A Compiler is a computer-based program that translates coding statements or code written in one programming language to another programming language that the computer processor can understand. It is a computer software that compiles a source code written in a higher-level language like C, C++, Java, etc. into a set of programming instructions or lower-level language that can be understood by the computer’s processor and based on which then various functions are carried out by the digital machine. Compilers are very large programs with the ability of error-checking and various other functions. Some compilers compile high- level language into low level language directly but then there are some compilers that translate higher-level language into an intermediate assembly language and then this intermediate language using some set of assembly programs or assembler is compiled into lower-level language or machine code.