Importance of Data Science: A Simple Guide in 10 Steps


Data Science is one of the most popular emerging domains and most sought-after career options. According to a report by LinkedIn, Data Science dominates its emerging job ranking with a whopping 37% hiring growth over the past three years. Data Science is revolutionizing almost every industry and gaining popularity day by day. But, why is Data Science so important? This article will answer this question for you.

  1. Why Is Data Important In Science?
  2. What Is Data Science & Why Is It Important?
  3. History of Data Science
  4. Why Is Data Science Important?
  5. Importance of Data Science In Business
  6. Benefits Of Data Science
  7. Importance Of Data Science In IT Industry
  8. Why Is Data Science Interesting?
  9. Importance Of Python In Data Science
  10. Future of Data Science

1. Why Is Data Important In Science?

Data is a precious asset of any organization. It helps firms understand and enhance their processes, thereby saving time and money. Wastage of time and money, such as a terrible advertising decision, can deplete resources and severely impact a business. The efficient use of data enables businesses to reduce such wastage by analyzing different marketing channels’ performance and focusing on those offering the highest ROI. Thus, a company can generate more leads without increasing its advertising spend.

2. What Is Data Science & Why Is It Important?

Data is meaningless until its conversion into valuable information. Data Science involves mining large datasets containing structured and unstructured data and identifying hidden patterns to extract actionable insights. The importance of Data Science lies in its innumerable uses that range from daily activities like asking Siri or Alexa for recommendations to more complex applications like operating a self-driving car.

The interdisciplinary field of Data Science encompasses Computer Science, Statistics, Inference, Machine Learning algorithms, Predictive Analysis, and new technologies.

3. History Of Data Science

In 1962, John Tukey wrote about the convergence of Statistics and computers to devise measurable outputs in hours. In 1974, Peter Naur mentioned the term ‘Data Science’ multiple times in his review, Concise Survey of Computer Methods. In 1977, the International Association for Statistical Computing (IASC) was formed to link modern computer technology, traditional statistical methodology, and domain expertise to convert data into knowledge. In the same year, Tukey composed a paper, Exploratory Data Analysis, that briefed the importance of using data.

By 1994, organizations had started gathering tremendous individual data for new showcasing efforts. In 1999, Jacob Zahavi stressed the need for new devices to deal with the gigantic chunk of organizational data. In 2001, William S. Cleveland presented an activity plan depicting how to create a specialized understanding and scope of Data Scientists and indicated six regions of studies for offices and colleges.

In 2002, the International Council for Science published the Data Science Journal focusing on Data Science issues like data systems explanation, application, and more. In 2003, Columbia University published the Data Science Journal to set a platform for data teams. In the year 2005, the National Science Board published an existing collection of digital data, and in 2013, IBM revealed that 90% of the global data had been created in the past two years. By this time, organizations realized the importance of Data Science to convert huge data clusters into usable information to gain crucial insights.

4. Why Is Data Science Important?

According to IDC, by 2025, global data will grow to 175 zettabytes. Data Science enables companies to efficiently understand gigantic data from multiple sources and derive valuable insights to make smarter data-driven decisions. Data Science is widely used in various industry domains, including marketing, healthcare, finance, banking, policy work, and more. That explains why Data Science is important.

5. Importance of Data Science In Business

There are various reasons why Data Science is important in business. Data Science enables enterprises to measure, track, and record performance metrics for facilitating enterprise-wide enhanced decision making. Companies can analyze trends to make critical decisions to engage customers better, enhance company performance, and increase profitability. Data Science models use existing data and can simulate several actions. Thus, companies can devise the path to reap the best business outcomes. Data Science helps organizations identify and refine target audiences by combining existing data with other data points for developing useful insights. Data Science also helps recruiters by combining data points to identify candidates that best fit their company needs.

6. Benefits Of Data Science

The organizational importance of Data Science is continuously increasing. According to one study, the global Data Science market is expected to grow to $115 billion by 2023. Some of the many Data Science benefits include the following:

  • In the healthcare industry, physicians use Data Science to analyze data from wearable trackers to ensure their patients’ well-being and make vital decisions. Data Science also enables hospital managers to reduce waiting time and enhance care.
  • Retailers use Data Science to enhance customer experience and retention.
  • Data Science is widely used in the banking and finance sectors for fraud detection and personalized financial advice.
  • Transportation providers use Data Science to enhance the transportation journeys of their customers. For instance, Transport for London maps customer journeys offering personalized transportation details, and manages unexpected circumstances using statistical data.
  • Construction companies use Data Science for better decision making by tracking activities, including average time for completing tasks, materials-based expenses, and more.
  • Data Science enables trapping and analyzing massive data from manufacturing processes, which has gone untapped so far.
  • With Data Science, one can analyze massive graphical data, temporal data, and geospatial data to draw insights. It also helps in seismic interpretation and reservoir characterization.
  • Data Science facilitates firms to leverage social media content to obtain real-time media content usage patterns. This enables the firms to create target audience-specific content, measure content performance, and recommend on-demand content.
  • Data Science helps study utility consumption in the energy and utility domain. This study allows for better control of utility use and enhanced consumer feedback.
  • Data Science applications in the public service field include health-related research, financial market analysis, fraud detection, energy exploration, environmental protection, and more.

7. Importance Of Data Science In IT Industry

In the present world, the generation and application of information is a critical economic activity. Data Science facilitates it with its power to extract information from large volumes of data. Information Technology makes our life easier by gathering and processing more data quickly and efficiently to provide results in hours contrary to days and weeks.

8. Why Is Data Science Interesting?

In the ever-evolving field of Data Science, new advancements and discoveries in research accelerate rapidly. Thus, you always get to learn something new, making every day’s work exciting. New Data Science skills can be acquired endlessly, giving you a competitive edge with knowledge and expertise. This is why Data Science is interesting.

9. Importance Of Python In Data Science

Data scientists use Python and R for data preparation and statistical analysis. Compared to R, Python is used for general purpose, more readable, simpler, and offers more flexibility while learning. Moreover, Python is used in several verticals other than Data Science and offers you various applications.


Companies now have enormous datasets at their disposal thanks to the documentation of every aspect of customer interaction. The role of data science is significant in analyzing and creating machine learning models based on this data. This is because these datasets are used to generate valuable insights. So, it’s reasonable to assume that as analysis and machine learning improve, so will the demand for data science.

As the field expands, more jobs should become available as more data scientists are needed for analysis. Individuals wishing to pursue a career in data science can look forward to a bright future with data science. Data Science has a huge scope across all industries. 

Another important aspect in the future of data science is Artificial Intelligence. AI is likely to be the most powerful technology that data scientists will have to deal with in the future. To put it another way, the future of data science will align itself to make it better in the long run. Artificial intelligence is already helping businesses make decisions and keep things running smoothly. Applied to real-world situations, artificial intelligence will use automated solutions to screen through enormous amounts of data to uncover patterns that aid current businesses in making better decisions.


With its footprint in practically every industry, Data Science job-demand is estimated to rise several-fold in the future. The importance of Data Science is growing with every coming day. Jigsaw Academy offers various quality Data Science courses to train tomorrow’s Data Scientists. Learners can enroll in the Full Stack Data Science Program (FSDS) if they are looking for a comprehensive Full Stack Data Science program. This 6-month-long online program is an industry recommended and validated course aligned to the SSC NASSCOM curriculum.

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