What is Data Science?

Given the enormous volumes of data being produced today, data science is a crucial component of many sectors and is one of the most hotly contested topics in IT. Since data science has become more and more popular, businesses have begun to use it to expand their operations and improve consumer happiness. Learn about data science and how to become a data scientist in this post.

To discover the hidden actionable insights in an organization’s data, data scientists mix math and statistics, specialised 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.

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 Harvard Business Review named the position of data scientist the “sexiest job of the 21st century” (link resides outside of IBM). They are relied upon more and more by organizations to analyze data and make practical suggestions to enhance business results.

What Is Data Science?

The field of study known as data science works with enormous amounts of data using cutting-edge tools and methods to uncover hidden patterns, glean valuable information, and make business decisions. Data science creates predictive models using sophisticated machine learning algorithms.

The information used for analysis can be given in a variety of formats and come from a wide range of sources. Let’s examine why data science is crucial to the current IT landscape now that you are familiar with what it is.

Why Data Science is Important?

Data science, AI, and machine learning are becoming increasingly important to businesses. No of their size or industry, businesses must quickly create and deploy data science capabilities if they want to be competitive in the big data era. Otherwise, they run the danger of falling behind.

The data science lifecycle involves various roles, tools, and processes, which enables analysts to glean actionable insights.


  • Data ingestion: The data collection phase of the lifecycle starts with gathering raw, unstructured, and structured data from all relevant sources using a number of techniques. These techniques can involve data entry by hand, online scraping, and real-time data streaming from machines and gadgets. Unstructured data sources like log files, video, music, photos, the Internet of Things (IoT), social media, and more can also be used to collect structured data, such as consumer data.
  • Data storage and data processing: Companies must take into account various storage systems depending on the type of data that has to be captured because data can have a variety of formats and structures. Creating standards for data storage and organization with the aid of data management teams makes it easier to implement workflows for analytics, machine learning, and deep learning models. Using ETL (extract, transform, load) jobs or other data integration tools, this stage involves cleaning, deduplicating, transforming, and merging the data. Prior to being loaded into a data warehouse, data lake, or other repository, this data preparation is crucial for boosting data quality.
  • Data analysis: Here, data scientists analyse the data in an exploratory manner to look for biases, trends, ranges, and distributions of values. The generation of hypotheses for a/b testing is driven by this data analytics exploration. Additionally, it enables analysts to evaluate the data’s applicability for modelling purposes in predictive analytics, machine learning, and/or deep learning. Organizations may depend on these insights for corporate decision-making, enabling them to achieve more scalability, depending on the model’s accuracy.
  • Communicate: Finally, insights are presented as reports and other data visualizations that make the insights—and their impact on business—easier for business analysts and other decision-makers to understand. A data science programming language such as R or Python includes components for generating visualizations; alternately, data scientists can use dedicated visualization tools.

Prerequisites for Data Science: 


  • Machine Learning: Data science is built on machine learning. Data Scientists require a thorough understanding of ML in addition to a foundational understanding of statistics.
  • Modeling: You may quickly calculate and predict using mathematical models based on the data you already know. Machine learning also includes modeling, which is determining which algorithm is best suited to handle a certain issue and how to train these models.

  • Statistics: The foundation of data science is statistics. Having a firm grasp of statistics can help you get greater insight and produce more significant results.

  • Programming: A certain knowledge of programming is necessary to carry out a data science project successfully. Python and R are the most popular programming languages. Because it’s simple to learn and provides a variety of libraries for data science and machine learning, Python is particularly well-liked.

  • Databases: A competent data scientist must be familiar with databases‘ operations, management, and data extraction.

Data science and cloud computing:


By giving users access to more processing power, storage, and other resources needed for data science projects, cloud computing grows the field of data science.

Tools that can scale with the quantity of the data are crucial since data science routinely makes use of big data sets, especially for projects that must be completed quickly. Access to storage infrastructure that can easily process and ingest enormous volumes of data is made available by cloud storage solutions like data lakes. These storage solutions give end users flexibility by enabling them to quickly create sizable clusters as needed. In order to speed up data processing tasks, they can also add incremental compute nodes, which enables the company to make short-term sacrifices for a better long-term result. To satisfy the needs of their end user—whether they are a major business or a tiny startup—cloud platforms often feature a variety of pricing models, such as per-use or subscriptions.

Data science toolkits frequently employ open source technology. Teams don’t have to install, configure, manage, or update them locally when they are hosted in the cloud. Additionally, a number of cloud service providers, like IBM Cloud®, provide prepackaged toolkits that let data scientists create models without writing any code, further democratizing access to technological advancements and data insights.


Data science use cases:


Data science offers various advantages to businesses. Process optimization through intelligent automation and improved targeting and customisation to enhance the client experience are typical use cases (CX). However, more precise illustrations consist of:

Here are a few examples of data science and artificial intelligence application cases:

  • An worldwide bank uses sophisticated and secure hybrid cloud computing architecture, machine learning-powered credit risk models, and mobile apps to provide speedier lending services.
  • Future driverless cars will be guided by extremely strong 3D-printed sensors that an electronics company is developing. To improve its real-time item detection capabilities, the system uses data science and analytics tools.
  • A vendor of robotic process automation (RPA) solutions created a cognitive business process mining solution that cuts client firms’ problem-handlinghandling times by 15% to 95%. In order to prioritize the emails that are most important and urgent, the solution is taught to comprehend the content and sentiment of customer emails.
  • An audience analytics platform was developed by a provider of digital media technology, allowing its clients to monitor what TV audiences are interested in as an increasing number of digital channels are made available to them. The system uses machine learning and deep analytics to gather data on viewer behavior in real time.
  • Tools for statistical incident analysis were developed by a city police force to assist officers in deciding how and when to use their available resources to reduce crime. The data-driven solution generates reports and dashboards to improve field officers’ situational awareness.
  • An AI-based medical assessment platform that can analyze current medical records to categorize patients based on their risk of having a stroke and that can forecast the success rate of various treatment plans was developed by Shanghai Changjiang Science and Technology Development using IBM® Watson® technology.

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