Think your way to AI — Part Two: Untangling the Knot between AI and Data Science
In the first part of this blog, we were able to explore and understand Artificial Intelligence and its role in real-world applications. Now, let’s dive deeper into the field of data science and its integral role in leveraging AI to extract insights and drive intelligent decision-making.
A Few of the main branches of Artificial Intelligence
Machine Learning
The popularity of Artificial Intelligence(AI) has caused people to use the terms “machine learning” and “AI” interchangeably. In reality, ML is a subset of AI, which means that all machine learning is AI, but not all AI is machine learning. An “intelligent” computer uses AI to think like humans and perform tasks with minimal human intervention. Machine learning is where the computer’s intelligence originated and thrived.
Deep Learning
Deep learning (DL) is a subset of both AI and ML. It is a form of ML algorithm that uses deep neural networks (more than one hidden layer/Advanced algorithm than ML) to analyze big data and assist AI.
DL algorithms are like a “black box” which identifies patterns and relationships in the data. It is a brain-like logical structure of algorithms!
Natural Language Processing
Natural language Processing (NLP) helps machines to understand human “interacting” language. It also significantly plays a role in a machine’s ability to generate and manipulate human-readable language to interact with humans, for example, ChatGPT (chatbots), Alexa (virtual assistants), and many more. It deals with extracting insights from textual data in order to improve the performance of the machine’s interpretability with humans.
Robotics
Robotics is a specialized branch of AI that focuses on the design, development, and application of robots. AI robots take actions based on their surroundings. They operate in the real world by making intelligent decisions. They are mostly utilized to do repetitive or dangerous tasks which could be helpful for humans, eg: assisting surgeries with doctors, automating assembling lines, etc.
Expert Systems
Expert systems simulate the behavior of domain expertise and make judgments with the help of AI and machine learning algorithms. They gain a knowledge base by adding experiences and facts to an interface. They are used in several domains such as healthcare, finance, manufacturing, and many more. One example of its use in the finance sector is predicting different market behaviors and financial indicators and generating decisions about asset management.
Computer vision
Computer vision helps machines to understand and get meaningful insights from digital images or videos. If AI helps machines to think, computer vision helps machines to see. This has been utilized in several applications for face recognition, image segmentation, object detection, and many more.
The Power of Data Science
Data Science in Practice
What is Data Science (DS)?
While exploring data science, it is common to keep drowning in doubts about the difference between AI and data science. Data science is not the same as AI but uses AI to perform its operations.
Data science extracts statistical insights from the data and uses AI as a tool to build the predictive/data model.
A combined knowledge of statistics, machine learning algorithms, and data conditioning makes a complete data scientist. This means they will understand data science, which helps them choose an ML model to gain insights and predictions.
There are practical applications of data science across various industries. Applying data science to the industry’s data can give valuable insights such as predicting customer behavior, optimizing supply chain operations, fraud detection, or sentiment analysis.
Further, Spotify collects data from millions of its users to perform analytics to provide better-personalized music recommendations. In the education sector, teachers’ effectiveness can be measured using huge volumes of data that are collected by measuring student achievements, student demographics, and many other variables. In terms of energy conservation, companies providing smart grids analyze big data smart meters, grid equipment, weather, GIS, and storm data to conserve energy and utilize it efficiently.
Data Science Workflow
A data science workflow is a series of steps that provides a pathway for completing a data science project. It helps to organize and plan the work required to be completed by the members of the data science team. There are different workflows in data science, but the most common workflow is CRoss-Industry Standard Process for Data Mining (CRISP-DM)
By adhering to the CRISP-DM methodology, organizations can achieve several benefits. In the first phase, project goals and scope are clarified to ensure that the business objectives align with the data mining efforts. The next phase provides a systematic approach for data collection, gathering, organizing, and analyzing data, guaranteeing that the information utilized for modeling is accurate and relevant. Thirdly, the data scientist experiments with the specific dataset with various machine learning algorithms and finds a better fit after doing an evaluation of these ML models.
CRISP-DM emphasizes the importance of addressing practical considerations, such as deployment, monitoring, and maintenance of the data mining solution. This ensures that the insights and ML models generated through the process can be effectively utilized and integrated into the organization’s operations
Where is the tie between AI and Data science?
As mentioned above, machine learning is a branch of AI. Machine learning consists of various complex algorithms such as logistic regression, decision trees, Support Vector Machines, Neural networks, and many more. These algorithms have the ability to extract patterns from data and predict the patterns of data.
Further, in today’s age, the amount and complexity of data have reached unprecedented levels. Data scientists are now faced with the challenge of working with diverse data formats to extract insights and make predictions. To address this, data science has emerged as a field that combines complex machine learning techniques with various tools to enhance the efficiency and speed of data analysis. Data science leverages machine learning to automatically analyze large volumes of data. It automates the data analysis process and enables real-time predictions without human intervention. Machine learning algorithms play a crucial role in the data science lifecycle by automatically building data models and training them for accurate predictions.
Overall, data science integrates advanced machine learning techniques and tools to empower data scientists in decision-making, pattern discovery, and predictive analysis. It enables efficient analysis of vast and complex datasets, contributing to faster and more informed insights.
Check out some exciting recent innovations using AI(2023) 🤩.
