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The Real Life Applications Of Data Science

  • Vrinda Mathur
  • Sep 25, 2023
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To discover the hidden actionable insights in an organization's data, data scientists mix math and statistics, specialized programming, sophisticated analytics, artificial intelligence (AI), and machine learning with specialized subject matter expertise. Strategic planning and decision-making can be guided by these findings of a growing number of data sources.

 

Data science is one of the fields with the quickest growth rates across all industries as a result of the increasing volume of data sources and data that results from them. As a result, it is not surprising that the Harvard Business Review named the position of data scientist the "sexiest job of the 21st century". They are relied upon more and more by organizations to analyze data and make practical suggestions to enhance business results.

 

Also Read | Is Data Science Still Relevant In 2023?


 

What is Data Science?

 

One of the most promising and in-demand career options for qualified professionals is data science which is still developing. Successful data professionals today are aware that they need to go beyond the conventional competencies of big data analysis, data mining, and programming. Data scientists need to possess a level of flexibility and awareness to optimize benefits at each stage of the process and grasp the complete spectrum of the data science life cycle.

 

In a 2009 McKinsey&Company article, Hal Varian, Google's chief economist and a professor of information sciences, business, and economics at UC Berkeley, discussed the significance of adapting to technology's influence and reshaping of various industries. The term "data scientist" was only recently coined when companies realized the need for data professionals who are adept at organizing and analyzing massive amounts of data. 

 

Effective data scientists are able to formulate pertinent questions, gather information from a wide range of data sources, organize it, convert the findings into solutions, and effectively convey their findings so that they have a beneficial impact on business choices. Since practically every industry needs these talents, skilled data scientists are becoming more and more valuable to businesses.

 

Data scientists are now commonplace in almost all firms and have become essential assets during the past ten years. These experts possess a broad range of talents, are data-driven, and possess a high level of technical proficiency. They are able to create sophisticated quantitative algorithms that organize and synthesize vast volumes of data utilized to provide answers to questions and guide strategy in their business. Additionally, this is combined with the leadership and communication skills needed to provide concrete results to numerous stakeholders throughout a company or organization.


 

Types of Data Science 

 

Working with data is a meta talent; to be successful in this field, one needs to possess a variety of abilities. Even if this opens up a lot of opportunities, hunting for a job might become stressful.

 

Companies hire for a variety of data science positions, and each of these positions calls for a specific set of skills. Therefore, our goal is to explain what each data professional does and how their contributions to a company vary depending on their job description.

 

Today, data science is a crucial component of everyday company operations. Large amounts of organized and unstructured data are transformed into an invaluable resource for the business by using techniques like data mining, cluster analysis, machine learning, visualization, etc. 

 

Recent years have seen a rise in the popularity of data science as an area of study, largely because it offers many chances for professional growth and specialization and is not just a passing fad. Data scientists are now employed by modern businesses to support a variety of functions, including Products, Marketing, Sales, and Engineering. Each position has different educational, professional, technical, and aptitude requirements. Some of the major types of Data Science roles are:

 

  1. Strategist for data:

 

A corporation should ideally hire a data strategist—a senior professional who is aware of how data may benefit businesses—before beginning to gather any data. Bernard Marr, a renowned data strategist, identified four key ways that businesses in various industries might apply data science:

 

  • They are able to make decisions based on data.
  • Data may be used to make products and services smarter.
  • Data can be used by businesses to enhance operational procedures.

 

They could create a new revenue stream via data monetization. Companies often outsource such data roles. They hire external consultants to devise a plan that aligns with the organizational strategy. Once a firm has a data strategy in place, it is time to ensure data availability. This is when a data architect comes into play.


 

  1. Developers:

 

Algorithms created by data developers can direct the development of products and price plans. Additionally, they support the extraction of patterns from big data inputs for demand forecasting, which is crucial for inventory and supply chain management. These individuals can help and collaborate with statisticians to tackle challenging problems since they possess good programming and machine learning skills.


 

  1. Data Architect: 

 

High-level database architectures are planned by a data architect (also known as a data modeler). In order to ensure the accuracy and accessibility of the information within a company, it must be planned, organized, and managed. They must also evaluate the requirements of business stakeholders and optimize schemas to meet those requirements. These data jobs are of utmost significance. Due to the database's tables' lack of coherence, important business questions might not be answered without a correct data architecture.


 

  1.  Exploratory and Quantitative:

 

They blend academic theory with quantitative and exploratory research to better technology and other products because they have a thorough understanding of data science. These are the people who apply sampling theories, create predictive models, conduct experimental design, and engage in other activities in an effort to understand how things work.


 

  1. Data Engineer:

 

Particularly in smaller firms, the roles of data engineers and data architects sometimes overlap. But there are significant variations.

 

To fit the use cases specified by the architect, data engineers construct the infrastructure, arrange tables, and set up the data. The so-called ETL process, which stands for Extract, Transform, and Load, is also handled by them. In order to do this, data must be retrieved, processed into a usable format, and then sent to a repository (the company's database). Simply put, they correctly stream data into tables.

 

They frequently work on ad hoc ETL-related activities, but they hardly ever have direct interactions with business stakeholders. For good reason, this is one of the highest-paying positions for data scientists. To fill this role, you'll need a wide range of abilities, including software engineer.  


 

Examples of Data Science:

 

Now let's look at some real-world data science applications in various fields. Let's concentrate on six instances of such applications in data science. We'll examine how data science benefits several industries, including manufacturing, sales, retail, logistics, marketing, and finance. Some of the examples of the data science industry have been listed below:
 

  1. Forecasting demand for the industrial sector:

 

Manufacturing is the earliest real-world example of data science. To estimate product demand, many firms rely on data science. They can improve supply chains and deliver orders without worrying about placing too many or too few. For your manufacturing business, data science may significantly reduce costs, especially when it comes to supply chain optimization. 


 

  1. Boosting sales:

 

Second, applying data science results in increased sales. Machine learning algorithms may look at historical data, compare it to current data, and analyze the market to determine how, when, and where your product or service will sell the most successfully. Additionally, data science can help you increase the accuracy with which you contact your target audience.


 

  1. Making Online Gaming Better: 

 

Activision Blizzard, which is well-known for creating games with cult followings like Call of Duty, World of Warcraft, Candy Crush, and Overwatch, uses big data to enhance online gaming experiences. One illustration of this is the company's game science division's analysis of gaming data to stop COD players from "empowerment," which is the attempt to lower someone else's sporting scores using deceptive tactics. The company also employs machine learning to find and track critical indicators for improving the quality of playing time as well as to detect power boosting.  


 

  1. Detecting Tax Fraud:

 

One estimate is that tax evasion costs the U.S. government $1 trillion a year, so it should come as no surprise that the IRS has updated its fraud-detection procedures for the digital age. To the chagrin of privacy activists, the agency has increased efficiency by creating multidimensional taxpayer profiles using information from several sources, including public social media data, metadata, email analysis, electronic payment trends, and more. The agency estimates 


 

  1. Analyses of sales:

 

Sales are the core focus of every business. Salespeople have a really difficult job. Numerous meetings, offers, follow-ups, phone calls, and presentations. Always rushing. each day. But they can also benefit from the use of data science! Think about two data science instances.

 

Sales professionals can choose which goods or services to recommend to a potential customer with the use of data science algorithms. Or, they may specify what kind of discount would be appropriate. Data science provides quick, precise, and indispensable support to buses. 


 

  1. Monitoring Tracking Sales: 

 

By analyzing cycle start dates, moods, stool type, hair circumstance, and many other indicators, the well-known Clue app uses data science to forecast users' menstrual cycles and reproductive health. Behind the scenes, data scientists use applications like Python and Jupyter's Notebook to mine this trove of anonymised data. Then, users receive automated notifications when they are fertile, about to start their period, or at an increased risk for illnesses like an ectopic pregnancy.


 

  1. Weather predictions in agriculture sector:

 

The final data science illustration involves agricultural weather forecasts. The way that farmers and other agricultural professionals make decisions today is changing as a result of data science. Weather significantly affects crop growth, development, and productivity, which has an impact on agricultural production. Farmers can obtain these aspects of agricultural weather prediction by using data science in the agriculture sector.


 

  1. Improved food delivery: 

 

The objective of the UberEats data scientists is to deliver hot food as soon as possible. But to make it happen nationwide, you need staff meteorologists, complex statistical modeling, and machine learning. The team must forecast how every conceivable element, from storms to holiday surges, will affect traffic and cooking time in order to optimize the entire delivery process.

 

Also Read | What is Data Privacy in Data Science?


 

Summing Up

 

Future-related issues are investigated by data scientists. Big data, which is characterized by the three Vs of volume, variety, and velocity, is where they begin. They then utilize it as fuel for models and algorithms. The most advanced data scientists create models that automatically improve as they learn from their failures and work on machine learning and AI.

 

Almost every industry has been transformed by data scientists. Their algorithms aid in the prediction of side effects in patients in medicine. With regard to sports, their models and analytics have redefined "athletic potential." With route-optimization models that capture typical rush hours and weekend lulls, data science applications have even taken on the problem of traffic. 

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