Artificial Intelligence in Data Science: 5 Definitive Facts

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. Artificial Intelligence in Data Science as a function has taken over technological automation but requires 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 Artificial Intelligence in Data Science applications. 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 Science and Artificial Intelligence. It will also provide the benefits and types of these 2 methodologies. Moreover, the article compares Data Science and Artificial Intelligence using 3 key factors. Read along to learn more about Artificial Intelligence in Data Science & 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.

What is Artificial Intelligence?

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 has 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 human mind’s flexibility.

Types of Artificial Intelligence

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

1. Artificial Narrow Intelligence(ANI): This is the most basic type of Artificial Intelligence. These systems are designed to solve one single problem efficiently. They have Narrow Capabilities which means they can excel in a specific task but in a very controlled environment and with limited parameters.
2. Artificial General Intelligence(AGI): This is a theoretical concept of Artificial Intelligence. Its main motive is a machine with a human level of intelligence across a variety of parameters like Language Processing, Image processing, and Computational Abilities. For AGI to function it requires multiple ANI to work together in harmony. Even with the most advanced computational pieces of equipment like Fujitsu’s K and IBM Watson, it took about 40 minutes to mimic one second of the human brain’s neuro-communications. This shows that our computational power is not sufficient and hence AGI is still theoretical in nature.
3. Artificial Super Intelligence(ASI): It is the most advanced theory made for Artificial Intelligence. This theory states that Artificial Intelligence will surpass human thinking capability by constantly adapting and being able to do multiple tasks at once. Since the computational capability has not yet reached the threshold required to mimic human intelligence, AGI is still a theory. Since ASI is an advanced version of AGI, its becoming reality is not evident in the near future.

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 human species to thrive in a beneficial way. Artificial Intelligence in Data Science has similar purposes.

Applications of Artificial Intelligence

With development still ongoing in this field the scope of applications increases with every iteration. Artificial Intelligence in Data Science is often 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 are tracked and based on the data, specific product advertisements are shown that may meet the user’s needs 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 the most information in the most human way possible. Artificial Intelligence in Data Science-based ChatBots function efficiently.

What is Data Science?

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, 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

For Artificial Intelligence in Data Science applications 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: Visualize the Results by using Data Reporting, and 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. Artificial Intelligence in Data Science methods works 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 for Optimising production, Reducing costs, and Boosting the profit. Also, the inclusion of data from sensors allows for finding the potential problems in systems. Also, data allows for 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, and 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 goods and services for maximum coverage. Using customer data, companies use sentiment analysis to find the feedback based on reviews.

Artificial Intelligence in Data Science: Understanding the Relationship

Now that you have a brief understanding of Data Science and Artificial Intelligence, let us talk about the roles and relationships between these 2 fields and their co-relation with Data Engineering.

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 the Extraction, Transformation, and Storing of data, it is the foremost step that is done. Artificial Intelligence in Data Science applications works on processed data hence can only function 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.

Role of Artificial Intelligence in Data Science

Artificial Intelligence (AI) and Data Science are closely interlinked, with each using the strengths of the other. AI relies on data science techniques to analyze data, find patterns, and improve algorithms, while data science uses AI to make predictions, automate tasks, and enhance decision-making processes. Here’s how AI plays a crucial role in Data Science:

• Analyzing Large Datasets: AI techniques such as machine learning, deep learning enable rapid analysis of vast amounts of data, uncover patterns, correlations, and trends hidden from manual identification.
• Predictive Analysis and Automation: Data Science uses AI-fueled tools like predictive algorithms in machine learning to allow the prediction of future outcomes via historical data. These regression and classification models are applied to give insights for deciding on further action.
• Improved Predictions: AI goes beyond predicting and supports the refinement of models with data, the discovery of anomalies, and the enhancement of the accuracy of forecast output for better business intelligence.
• Data Insights Enhancement: The automation of summarizing data findings helps businesses communicate key insights quickly to leadership and stakeholders through AI.
• Personalized Experiences: AI models can analyze user data to provide customized content and recommendations, which is significant for industries such as e-commerce and entertainment.
• Customer Experience Enhancement: Applications of AI such as sentiment analysis enable businesses to understand feedback from customers and improve their brand image by analyzing reviews and social media posts.
• Process Optimization: AI optimizes processes such as supply chain management by identifying inefficiencies and suggesting optimizations for better performance overall.
• Predictive Maintenance: AI-based systems can analyze data from sensors in machinery, enabling companies to predict when they are likely to break down and schedule maintenance ahead of time to minimize downtime.
• Quality Control: Quality control is automated through AI-powered systems, which check for defects and various deviations from set specifications, thus ensuring high-quality products.

Comparing Data Science and Artificial Intelligence

Data Science was created with the aim of discovering hidden trends in vast volumes of data. This discipline is useful for extracting raw data, processing it, and analyzing the data to attain a better understanding. This way the vast data can provide actionable insights on which you can make important business decisions. On the other hand, you can deploy Artificial Intelligence to manage data autonomously. This implies, that you can remove the human dependency from your task and automate it to the full extent.

To provide you with a thorough understanding, this section compares Data Science and Artificial Intelligence using the following 3 factors:

• Goals
• Fundamental Technologies
• Use Cases

Goals

The main goal of Data Science is to finalize a suitable problem statement, register business requirements, and use Data Analytics and Machine Learning models to develop a feasible solution. Furthermore, Data Scientists also perform Data Visualization to present the insights generated from their proposed solution.

Artificial Intelligence’s main goal is to imitate human intelligence using computers so that machines can make smart decisions in complex situations. To achieve this goal, AI Professionals work to develop new algorithms, optimize existing neural networks, and perform data automation for processing huge chunks of data.

Fundamental Technologies

Data Science leverages multiple statistical techniques to process and transform large datasets. This domain deploys Machine Learning models on source data to identify actionable insights. To achieve their goals, Data scientists deploy tools such as Tableau, Python Programming Language, MATLAB, TensorFlow Statistics, Natural Language Processing (NLP), and many more.

Artificial Intelligence is mainly dependent on machine learning-powered algorithms which are designed for distinct purposes. AI Professionals use a variety of tools to enhance the process of teaching decision-making to computers. the process of learning. All work done in the field of AI revolves around tools such as Keras, Spark, Tensor Flow, Scala, Scikit Learn, etc.

Use Cases

A key factor in the comparison of Data Science and Artificial Intelligence is their use cases. The following use cases are beneficial for deploying Data Science methodologies:

• Identifying patterns and popular trends in the market.
• Generating Statistical Insights to support decision-making.
• To perform Exploratory Data Analysis (EDA) for your business.
• Requirement of high-speed mathematical processing.
• Work-related to Predictive Analytics.

Artificial Intelligence is useful for deploying complex Machine Learning models for the following situations:

• Business data requires high precision.
• You need to speed up the decision-making process.
• You need to separate emotional and logical aspects of decision-making.
• Businesses require automation for repetitive tasks.
• You need to conduct a detailed risk analysis.

Conclusion

This article gave a brief overview of popular technologies like Data Science, and Artificial Intelligence. It also provided information on the relationship between Data Science and Artificial Intelligence. Moreover, it discussed the roles of Artificial Intelligence in Data Science and provided a comparison between the 2 technologies. Artificial Intelligence plays a crucial role in Data Science by providing it with advanced tools for proper Predictive Analysis and providing proper parameters for Data Engineering to be applied to software as well.

Stay ahead of the curve with our guide on AI trends that are set to revolutionize data science and analytics.

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FAQ on Artificial Intelligence in Data Science