AI and data science are two buzzwords that are used widely these days. Both these fields are related to data and advanced analytics. In this blog, we shall understand what is AI versus data science and its applications in solving real-worlds problems.
What is AI ?
AI stands for Artificial Intelligence and is made quite popular recently due to the launch of Chatgpt from Microsoft and Bard from google. These are examples of AI applications – specifically generativeAI – that offer to create new content of text, audio, video, code etc. GenerativeAI uses various algorithms and techniques including neural network and deep learning to identify patterns and generate new outcomes using it.
AI aims to create intelligent machines by using fields of computer science and engineering. AI involves the development of algorithms and models that can learn from data, make predictions and decisions, and improve their performance over time. Image and speech recognition, natural language processing, intelligent robots, autonomous vehicles are some popular applications of AI.
What is Data Science ?
Data Science is an interdisciplinary field that extracts insights and knowledge from large and complex datasets. In order to analyse data and produce insights that can be utilised to make educated decisions, it incorporates approaches from a variety of domains, including statistics, mathematics, computer science, and domain-specific knowledge. The data can be both structured and unstructured data, small or big, from businesses or governments.
Stages in data science
Data Science involves various stages, including data collection, data wrangling and processing, exploratory data analysis, modeling, visualization and deployment in production environment.
- In data collection, data scientists collect and collate data from various sources like ERP, CRM, supply chain applications as well as external data sources and includes structured and unstructured data.
- In the data wrangling stage, the data is processed, cleaned and transformed to remove errors, inconsistencies, and missing values.
- The processed data is then analyzed using various statistical and machine learning techniques to identify patterns, trends and relationships.
- Quantitive and machine learning techniques are employed to build predictive or prescriptive models on the data to address the business problem.
- Visualisation tools like graphs, charts, and dashboards, are used to display data in a way that is easy to comprehend and interpret.
Data Science is used in various fields such as healthcare, finance, e-commerce, and entertainment to generate insights that can help organizations make informed decisions, improve efficiency, and drive growth. Among the free and open source softwares (FOSS), R and python are used widely in data science.
Applications of data science
- Predictive maintenance: When a machine or a part fails in a manufacturing unit, it affects productivity. If such failures or repairs are predicted in advance then it helps save businesses money and time. In manufacturing industry, sensor data (example, data from IOT devices) and previous records are used to forecast when a machine might break down or require repair and plan maintenance of critical machines accordingly. In airline industry, based on sensor data gathered during flights, airlines predict when the aircraft engines could need maintenance and work on better utilization of their fleet.
- Fraud Detection: Financial institutions like banks and insurance companies use data science methods to detect fraudulent transactions by analyzing trends and patterns in large datasets of customer transactions. Machine learning models are trained to identify suspicious transactions and alert them for further diligence.
- Marketing: E-commerce marketplaces and companies use data science to analyze customer’s purchase data, browsing history, demographics, to personalize marketing campaigns. This data is also used to build recommendation engines that recommends similar products or services to prospective customers. This helps in increasing sales for the company and provide better user experience as well as choices to the customer. Market mix modeling is another application which is used for understanding the true impact of your marketing efforts on sales and allocating your marketing spend strategically for maximum growth..
AI applications in business
- Self-driving cars or autonomous vehicles use AI to perceive their environment and make driving decisions based on that information. Computer vision, machine learning, and deep learning algorithms are some of the techniques that are employed to design such systems that allow the vehicle to navigate and make decisions based on real-time data.
- Virtual assistants like Siri, Alexa, and Google Assistant and generativeAI applications like chatgpt, bard are examples of AI-powered technology. They are trained to comprehend user queries and make a response with useful information using natural language processing techniques.
- AI-powered image recognition technology is used in different types of applications, from using image of products for tracking inventory in retail, medical imaging in healthcare to using crop image to monitor crop health and fertilizer requirements in agriculture. This technology uses deep learning algorithms to identify static and moving objects like laptop, cars, people, and other visual data within an image.
Applications of data science and AI
While we are discussing AI versus data science, you can understand that there is an overlap between data science and AI. Image, text, voice – these are unstructured data which is handled by both data science and AI. An illustration of both data science and AI is a recommendation engine. In order to analyse user behaviour and provide individualised recommendations on relevant products or services, purchase and browsing data of users are collected, processed and analyzed. Then machine learning algorithms are used to generate predictions and offer suggestions. So recommendation systems are an excellent illustration of how data science and AI collaborate to produce insightful information and enhance user experiences. Open source programming languages like R, python are used during analysis as well as modelling as they have many libraries and packages. Also proprietary tools like Azure AI/ML, Amazon AI/ML, SAS etc. are used.
There are many such examples like fraud detection in credit card or banking transactions, spam mail identification, medical imaging for patients in healthcare to identify progression of a disease where data science, machine learning and AI technologies are used.