Data Science – Everything That One Should Know About This Cutting-edge Technology

If there is one thing that can change the face of an organization in no time is the quality data and its effective utilization. You may not know this but by simply using the internet around 2.5 quintillion bytes of data every day. Imagine the picture when data generated from all kinds of resources is taken into account.   

While there is no shortage of data in today’s world, its effective utilization is the only thing that will make data existence worth full.  

Data science is what makes it possible. From the day data has been considered as a vital growth-driving factor, data science has come to the forefront. So, what’s data science? What types of data science exists? What’s its lifecycle? 

These are some of the key questions that we’ll answer in this blog. So, stay tuned for more. 

Data Science – Knowing the Basics and Definitions 

Speaking of data science definition, it’s a multidisciplinary approach used for extracting useful insights from the set of given data. It involves multiple tasks like data discovery, preparation, data analysis, predictions, and data reporting to get the desired results. These tasks are also known as the lifecycle of data science.  

Have a look at these steps of data science from close: 

  • Data discovery is the process of finding out resources to extract data for the organization. This phase involves framing the business problem and formulating the initial hypotheses (IH) to test. 
  • Data preparation is the process of cleaning the data set, choosing the accurate data, data aggregation, and manipulation of data. Its key aim is to prepare the data for further processing. Performing ETLT (extract, transform, load, and transform) is a key step of data preparation.  
  • Data analysis is the second stage which involved the usage/development of algorithms, analytics skills, an AI model to extract information from the prepared data set. It’s widely done using the help of software and technologies. 
  • Data model planning is the next phase of data science. It involves determining the methods and techniques used for establishing correlation or relations between different variables to frame the foundation of the algorithms used in the next phase. This phase involves the use of visualization tools and statistical formulas for performing quality Exploratory Data Analytics (EDA).  
  • Data model building is that phase of data science wherein a data scientist has to prepare the datasets for testing and training purposes. At this stage, you must confirm if your tools are sufficient for model execution or there is a need for more powerful tools. Let’s say, you might need a more robust environment for speedy and accurate processing of the data in parallel. For this, clustering, association, and classification like model-building techniques are used. 
  • Data prediction means combining the data analysis results to conclude something. It involves the usage of data visualization tools to represent the results.  

Data science – overall – involves handling all sorts of data such as raw data, unstructured data, and structured data. With time, the face of data science has changed. When it came into being, it was the job assigned to mathematicians or statisticians. 

Presently, there are data scientists and data analysts handling the job of making data work for the organization in a positive way. Technologies like machine learning, deep learning, and artificial intelligence or AI are used for data analysis these days.  

On a general basis, data scientists are professionals having an ideal combination of computer and pure science skills to handle data in an expected manner. For an organization, a data scientist can handle the below-mentioned tasks.  

  • Applying given and suitable mathematics, statistics, and scientific methods to extract results from a given dataset.  
  • Using the offered or available tools and techniques, evaluating and preparing the data.  
  • Extracting useful insights from the given data.  
  • Writing applications for automating data processing and calculations.   
  • Telling and illustrating ways to convey the meaning of data processing results.  
  • Describing ways in which results can be used for addressing the business problems.  

Key Data Science Tools to Use  

As quoted above, data science is a job that can achieve accuracy and excellence only by using certain kinds of tools and technologies. Without their presence, it’s not possible to handle a huge database. From data discovery to data analysis, tools are here to speed up the process and bring excellence.  

  • Python 

Python is another very famous programming language (high-level) used for general purposes. It makes code readability effortless. There are several Python libraries, designed for supporting various data science tasks. For example, use Numpy when you want to handle large dimensional arrays. Matplotlib is good for data visualization, and Pandas can be utilized for data manipulation & analysis, and so on. 

  • R 

R is one of the most commonly used data science tools. It’s an open-source programming language used for statistical computing and graphics generation. As it offers assorted libraries and tools for data cleaning, preparation, and visualization, it’s the first choice for many data scientists.  

  • Apache Spark and Apache Hadoop 

These two are the most loved data processing platforms making things easier than ever for data scientists.  

  • Purpose-build data visualization tools 

Data visualization is a key stage of the data science lifecycle and there is no dearth of custom tools for this job. Tableau, Microsoft PowerBI, D3.js, and RAW Graphs are some of the key ones.  

  • Model building tools 

Tools like SAS Enterprise Miner, WEKA, SPCS Modeler, and MATLAB are used widely in the data model building stage.  

Data Science Use Cases In Practical World  

  1. Data science has become an indispensable part of today’s digital world. From developing the apps to generating useful platforms, data science is leveraging things at every front.  
  1. Using machine learning-powered credit risk models and hybrid cloud computing, a bank has created a mobile app to support the on-the-spot decisions for loan applicants.   
  1. Stridely, as a leading robotic process automation solution provider, has used a high-end cognitive business process mining solution to trim down the incident handling times.  

The Final Word 

Data Science and its scope for various types of businesses can be considered to be boundless. The more skilled professionals and more complex problems your business will have, its implementations will be more helpful and precise.  

Hire experts at Stridely Solutions for your next Data Science project and see how potent a technology can be.

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