Data Science and Artificial Intelligence: 2 Definitive Facts

on Artificial Intelligence, data crunching, Data Engineering, data infrastructure, Data Integration, data management, data mining, Data Modelling, Data Science • July 26th, 2021 • Write for Hevo

Data Engineering, Data Science, and Artificial Intelligence are hot topics in the current digital age. These technologies have changed the way humans interpret a problem. These technologies work on data, but utilize it for different outcomes. Data Science and Artificial Intelligence are technologies that correlate with each other in many ways. Data Science and Artificial Intelligence have taken over technological automation but require Data Engineering in symphony to function properly. There are constant advancements in the fields of Data Science and Artificial Intelligence and they are said to bring the 4th revolution in the industry.

The technologies are related to each other in more ways than one. Data Engineering deals with the collection and preparation of data so that it can be used by Data Science and Artificial Intelligence. Data Science utilizes this data and predictively and analyzes it to gain insights. Artificial Intelligence deals with working on data by using tools to develop Intelligent systems. Data Science and Artificial Intelligence work on data to produce similar outcomes dealing with analysis.

This article will give you an overview of Data Engineering, Data Science and Artificial Intelligence. It will provide the relationship between Data Engineering, Data Science and Artificial Intelligence It will also specify the role of Artificial intelligence in the fields of Data Science and Engineering.

Table of Contents

Introduction to Artificial Intelligence

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The ability of digital computers to perform tasks that are commonly performed by humans is called Artificial Intelligence. AI ( Artificial Intelligence ) tries to mimic the human mind by incorporating Problem-Solving, Decision-Making, and Reasoning abilities into machines.

The development in the field of Artificial Intelligence started soon after the development of computers in the 1940s. Development in the fields of Data Science and Artificial Intelligence have moved up in pace since then. Since then there is a large improvement in how well machines perform complex tasks. Still, despite continuing with these advancements, computers have not been able to match the flexibility of the human mind.

Types of Artificial Intelligence

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There are 3 different types of Artificial Intelligence, namely as follows:

Purpose of Artificial Intelligence

The main purpose of Artificial Intelligence is to aid human capabilities and predict the far-fetched consequences that the human brain cannot process. Artificial Intelligence has the potential to reduce the hardships of human labor and make a possible pathway for the humans species to thrive in a beneficial way. Data Science and Artificial Intelligence have similar purposes.

Applications of Artificial Intelligence

With development still ongoing in this field the scope of applications increases with every iteration. Data Science and Artificial Intelligence are collectively used in real-life use. A few prominent applications of Artificial Intelligence are:

  • Personalized Online Shopping: The search trend and search history of the user is tracked and based on the data, specific product advertisements are shown that may meet the user need and expectations.
  • Enhanced Imaging and Surveillance: The features of images are enhanced using computer vision, which is used by apps like Snapchat and Instagram. Image Enhancement is also used by the security and military services for surveillance. 
  • Video Games: computer games consists of bots that are controlled by the system. These characters are adaptable i.e they change the difficulty level based on the real player. This works on Artificial Intelligence’s adapting ability.
  • Healthcare: It is the sector that has adopted Artificial Intelligence the most. The Automated Systems that ease the development of medicine have helped in finding a cure for more diseases. Also applying Artificial Intelligence to historical data has helped in predicting the outcome of bacteria and viruses.
  • ChatBots: The inclusion of optional chatbots in websites and online stores has become a must. These provide most information within the most human way possible. Both Data Science and Artificial Intelligence are used for ChatBots to function efficiently.

To know more about Artificial Intelligence, click here.

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Introduction to Data Science

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Data is a boon to organizations, only if processed efficiently. The study of Data, its origin, its value, and transformations to gain valuable insights is what comprises Data Science. The present businesses run on large amounts of data and standard Business Intelligence tools fall short when processing large amounts of data at once. Data Science has more advanced features, that can process such large amounts of Unstructured Data. It can process data from sources such as Financial Logs, Multimedia Files, Marketing Forms, Sensors, and Instrumental values, and Text Files.

The below image depicts the process data goes through for data science to work efficiently. It is also called as Life-Cycle of Data Science.

Life Cycle of Data Science
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Life-Cycle of Data Science

For Data Science and Artificial Intelligence to work, there are predefined methods. These methods are called the Life Cycle of Data Science. Below mentioned are the methods followed in Data Science.

  • Capture the Data: Capturing the data means gathering raw data. The Data is Acquired from various sources like Data Entry, Signal Reception, and follows Data Extraction Process. 
  • Maintain the Data: The Data is Stored in Data Warehouse after Data is Cleaned and processed.
  • Process the Stored Data: The Data from the warehouse is Processed, Mined clustered, and summarised.
  • Analyze the data: Exploratory analysis by performing Regression, Predictive Analysis, and Qualitative Analysis.
  • Communicate the Results: Visualizing the Results by using Data Reporting, Business Intelligence tools.

Purpose of Data Science

The main purpose of Data Science is to find patterns in data. It is used to analyze and gain insights using various Statistical Techniques. The present data and historical data are used to predictively analyze future outcomes. These valuable predictions and Insights provide an opportunity for businesses to thrive and adapt based on market trends.

Applications of Data Science

Data Science works on the data and since the quantity of data is growing at a very fast pace, its benefits are also increasing at a good pace. Data Science and Artificial Intelligence work prominently on this growing data.

A few prominent applications of Data Science are:

  • Banking: Data Science allows Banks to utilize the resource efficiently based on the data. Data Science allows for risk management and risk modeling based on customer data. Also predicting the customer churn and fraud detection using the data.
  • Manufacturing: Data Science allows in Optimising production, Reducing the cost, and Boosting the profit. Also, the inclusion of data from sensors allows in finding the potential problems in systems. Also, data allows in optimizing the quality and production capacity.
  • Transport: Data Science helps in creating the systems for self-driving cars using sensory data. Data Science allows extensive analysis of fuel consumption patterns, driver monitoring, and path selection helps in optimizing the industry.
  • Healthcare: Data Science helps in predictive analysis of a diagnosis, drug discovery based on disease data, Medical image analysis to predict diseases from images.
  • E-Commerce: Data Science helps in finding potential customers. It helps in optimizing the customer base and clusters them based on the trends. It is also used for predictively analyzing the good and service for maximum coverage. Using customer data, companies use sentiment analysis to find the feedback based on review.

To know more about Data Science, click here.

Introduction to Data Engineering

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Data Engineering is a field that deals with the Collection, Validation, and Transformation of data into the format that is used by the Data Science and Artificial Intelligence to gain significant insights and Predictive Analysis. The transformed data is used in both Data Science and Artificial Intelligence. Data Engineering is a broad field comprising of various modules like Data Infrastructure, Data Mining, Data Crunching, Data Acquisition, Data Modelling, and Data Management.

Modules of Data Engineering

Data Engineering is a broad field, consists of various modules that deal with many parameters, from extracting, structuring, storing, and transforming data. The transformation of data is done for use in Data Science and Artificial Intelligence. Below mentioned are a few modules of Data Engineering:

  • Data Infrastructure: It is a digital infrastructure that is known for promoting Data Consumption and Sharing. Data Infrastructure is a collection of data assets. Data Infrastructure is the body that maintains data and guides the utilization of the collected data.
  • Data Mining: It refers to finding the anomalies and correlations in data to predict insightful outcomes from it. It involves exploring large amounts of data and analyzing it to gain meaningful patterns.
  • Data Crunching: It refers to the initial process of preparing raw data for analysis. It includes removing unwanted details in data, formatting, transforming, and structuring the data into a required format.
  • Data Acquisition: It is the process of converting real-world signals into digital format for storage and analysis. The Sensor records the data, which is passed through the DAQ device and is shown in digital format on computer screens.
  • Data Modelling: It is the process of creating models for data to be stored. It is a visual representation that helps in understanding the data and applying required Business Regulations. The data model ensures consistency in semantics and naming conventions. It also ensures Data Security.
  • Data Management: It is the process of managing the acquired data. It includes acquiring, validating, storing, protecting, and processing the data. It ensures that data is accessible, reliable, and available in real-time.

Purpose of Data Engineering

The main purpose of Data Engineering is to prepare data for further processing. It deals with creating the infrastructure for the Data Platform. The raw data cannot be processed by Data Science and Artificial Intelligence engineers, hence extracting the raw data, transforming, and Storing the data for Predictive Analysis and Statistical Representation is done in Data Engineering.

To learn more about Data Engineering, click here.

Relationship between Data Engineering, Data Science and Artificial Intelligence

Now that you have a brief understanding of Data Engineering, Data Science, and Artificial Intelligence, let us talk about roles and relationships between these 3 fields.

All the 3 technologies are highly correlated to each other. Data Science and Artificial Intelligence are highly related. As depicted in the image below all have data as a common element. Since, Data Engineering deals with Extraction, Transformation, and Storing of data, it is the foremost step that is done. Both Data Science and Artificial Intelligence work on processed data hence can only be done after the raw data is Engineered. 

The image below depicts the products that are generated due to the correlation of the 3 concepts. Machine Learning is formed by Data Science and Artificial Intelligence. Software Engineering is formed by Artificial Intelligence and Data Engineering.

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Role of Artificial Intelligence in Data Science

  • Machine Learning is a Supervised version developed by the combination of Data Science and Artificial Intelligence, where a limited amount of data is put into the system to predict the possibility. For proper Predictive Analysis Machine Learning algorithms like Regression and Classification are used.
  • Understanding the role of Artificial Intelligence in Data Science, Data Science and Artificial Intelligence are the words that are used interchangeably because of their working, but Artificial Intelligence is a tool for Data Science. Artificial Intelligence is not completely represented by Data Science because Data Science only deals with predictive analysis and uses Machine Learning tools for it. Machine Learning is just a subset of Artificial Intelligence, and AI can provide many more complex tools for analysis.

Role of Artificial Intelligence in Data Engineering

Talking about the relationship between Artificial Intelligence and Data Engineering, the concept of Software Engineering comes into the picture. Software Engineering deals with the development of well-defined software products using a pre-defined set of rules and principles.

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  • Understanding the role of Artificial Intelligence in Data Engineering, they both are not very much related in terms of working but for Artificial Intelligence to work Data Engineering is required. Without proper data, Artificial Intelligence is of no use. Since Data Engineering is a subpart of Data Science, Data Engineering also represents some parts of Artificial Intelligence but not all.
  • As the image depicts Data Engineering deals with the moving and storing of data. Data Science deals with the exploring, optimizing, and labeling of data for insights. Artificial Intelligence runs advanced tools on data science.


This article gave a brief overview of hot-technologies like Data Engineering, Data Science, and Artificial Intelligence. It also provided information on the relationship between Data Science and Artificial Intelligence. It also discussed the roles of Artificial Intelligence in Data Science and Data Engineering. Artificial Intelligence plays a crucial role in Data Science by providing it with advanced tools for proper Predictive Analysis and provides proper parameters for Data Engineering to be applied onto software as well.

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Share your experience of learning about Data Engineering, Data Science and Artificial Intelligence in the comments section below.

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