Role Of Major Components In Data Science Projects/data-science-insights/role-of-major-components-in-data-science-projects

Role Of Major Components In Data Science Projects

Role Of Major Components In Data Science Projects

Data science continues to grow in popularity as a promising career path for this year. It’s one of the most exciting & attractive options available. Demand for data scientists is on the rise & according to recent reports, demand will skyrocket in the future as well. Data science encompasses a wide range of scientific methods, procedures, techniques & information retrieval systems to detect meaningful patterns in organized & unstructured data. Data science, at its core, is a practice that involves finding patterns within data. From these patterns, insight can be derived and used for business intelligence purposes or as the basis for creating new product features. All these outcomes of data science projects can be beneficial to product teams that are looking to differentiate their offerings in the market and provide customers with great value. This leads us to the discussion about; 


    Developing a plan or a data strategy is simply determining what data are you going to gather and why. Here, we’re not talking about the strategy for deciding what mathematical techniques we’re going to use or the technologies required. The focus is on the data we need to address the business problem/ opportunity and why. Hence, deciding on a strategy requires making a connection between the data you intend to gather and the business goals. Mind it, not all data is created equal. The effort that goes into gathering and formatting the data, getting rid of the ‘garbage data’ that doesn’t serve the business goal is a reflection of achieving mission-critical data for business goals. 
    Data mining basically implies analyzing data patterns in large batches of data using one or more software. It has applications in multiple fields like science and research. As an application of data mining, businesses can learn more about their customers as it helps them to be closer to them & develop more effective strategies related to business functions & leverage resources in an optimal & insightful manner. 
    Data engineering primarily involves the creation of software solutions for data problems that involve establishing a data system with data pipelines and endpoints within that system. Data engineering requires an in-depth understanding of a wide range of data technologies & frameworks along with creating data solutions to enable business processes. 
    Considered as the heart of data science, we can think of data analysis & mathematical models in terms of how to use data to extract insights or make business predictions & to create a tool that replaces or supplements what a human does. 
    Data visualization is not just presenting the analyzed data correctly; it involves understanding the raw data and what is needed to be visualized based on the needs and goals of users and the operations. Data operationalization involves real-time person decision/action, a long-term response, or a recommendation on a specific task. 

Data science is definitely a wicked game of numbers. You, as a business entity are required to stick around to each of the data science components in order to stay relevant, thrive & eventually taste business success. Talking about statistics, machine learning, data engineering, visualization, domain expertise, and programming, all of these constitute an integral part of the big numbers game called Data Science. Delving into each of them separately: 

  • Statistics- Descriptive and Inferential- both help in organizing & generalizing large data sets and applying probability before concluding, thereby focusing on characteristics of data providing parameters. 
  • Data Visualization- It absorbs information quickly, improves insights, helps make faster decisions, improves the ability to maintain audiences’ interest, eliminates the role of data scientists & eases out the distribution of information hence collected. 
  • Machine Learning- Frequently used in fraud detection & client retention and eases the process of making predictions with unforeseen/ future data easy. 
  • Deep Learning- These algorithms and multi-layered ANN require very powerful machines & are very useful in detecting patterns from input data. 
  • Domain Expertise- A high level of expertise in the area can vastly improve the accuracy of the model you want to build. 
  • Data Engineering- It leads to acquiring, storing, retrieving, and transforming the data, collecting them into a single warehouse representing the data uniformity as a single source of truth. 
  • Advanced Computing- The said capabilities are used to handle growing range of challenging science & engineering problems, most of which are data-intensive. 
  • Mathematics & Programming- Widespread usage of the most popular programming languages- Python, R, Java, and NoSQL ensures improved performance in storing huge data. 

Towards the close of the article, we can deduce that data science is an exciting interdisciplinary field that is revolutionizing the way companies approach every facet of their business in today’s competitive times. Through a communion of traditional statistics with fast-paced, code-first computer science doctrine & business acumen, Data science teams can solve problems with more accuracy and precision than ever before, especially when combined with soft skills in creativity & communication. Data science encapsulates both old and new, traditional and the cutting-edge. Many tools and techniques now described as data science have been around for decades, with ideas and concepts repurposed from not just one field but many. This has led to rapid advancements as the field’s interdisciplinary nature combines mathematics, statistics, computer science, and business knowledge in new and novel ways.  

Just as four wheels are important to balance a vehicle, these components when applied correctly can make the data science project a huge success and maximize the ROI of the intended business model. 

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