How Data Science and AI Overlap?

The Fourth Industrial Revolution, also known as Industrial 4.0 has become the talk of the town since its inception. The fourth stage of the Industrial Revolution is characterized by a fusion of various technologies, blurring the lines between the digital, physical, and biological spheres. This revolution is fundamentally anchored in automation, substantial data volumes, robotics, and the Internet of Things. 

To be precise here, Data Science and Artificial Intelligence, the two fields gaining momentum in the current scenario, are the prime contributors to driving the revolution forward. These two emerging concepts interact in a symbiotic manner. Garnering and harnessing the power of data is at its peak now. Data Science and Artificial Intelligence are fields that share a common ground as they overlap in several factors and hence, are similar in some manner. But simultaneously, we cannot deny the differences between these fields.

Let us grab a detailed insight into the similarities and differences between these two emerging fields and get an overall idea that throws light into the importance of both fields in the current context. Garnering the knowledge of twin subject matters with an MSc. IT in Ahmedabad will boost your career prospects as a professional. 

Similarities between Data Science and Artificial Intelligence

Data Science and Artificial Intelligence, the two revolutionizing fields, are the buzzwords of the hour. These two fields are currently dominating career prospects, with endless demands in job opportunities. Students willing to pursue a career in these fields should be aware that these two prominent fields share a chunk of similarities. 

Let us understand the similarities that can help you gather a better perspective on your prospects. 

#Data

Both are data-predominated fields. It means that they heavily deal with data. Data science emphasizes managing, processing, and interpreting big chunks of data that effectively inform decision-making. Similarly, AI must be continuously fed with data to learn and improve decision-making. 

#Machine Learning

Machine learning is a vital link that connects data science and AI. Both heavily rely on the usage of machine learning. Data Science uses machine learning to discover patterns and further automate the data analysis process. Machine learning, which happens to be a subset of AI, automatically sanctions a machine or system to function in a way that learns and improves from experience.  

#Interdisciplinary

Both are interdisciplinary. They require the utilization of knowledge and skills from other disciplines, such as mathematics, computer science, statistics, and domain expertise.

#Use of tools

Both fields are equipped with the same programming tools and technologies, such as Java, Python, R, SQL, TensorFlow, Scikit-learn, and Jupyter Notebooks, for data analysis and model building.

#Problem-solving

Both fields are oriented toward problem-solving, be it in terms of solving real-world problems, enhancing customer engagement, reducing fraud, and predicting stock prices. 

#Goals

The central goal concerning the two fields involves researching a problem, analyzing it, and finding an algorithmic solution. In addition, both run on cloud-based platforms.  

#Skills

Both fields require professionals who possess soft skills, such as strong and sound communication and interpersonal skills, analytical thinking, leadership skills, problem-solving abilities, time management, decision-making, critical thinking, etc. 

#Qualifications

The qualification requirement for both fields is the same. Both careers can be pursued with a bachelor’s or master’s degree in software engineering, computer science, or any other professional having a knack or a passion for data science and AI.

#Serving Businesses

Both fields are dedicated to serving business intentions. Data science is used by organizations to advance processes, enhance customer experience, and garner a competitive edge. Similarly, AI also caters to business growth by creating intelligence systems that smoothly perform tasks, interact with users, and extend recommendations. 

#Ethical and privacy concerns

Both fields are required to consider and undertake ethical and privacy concerns respectively. Data science needs to carefully handle personal and sensitive data to ensure privacy. Similarly, AI systems are also developed and installed ethically without giving rise to any unintended consequences on any ground.

#Technological Innovation and Research

Both fields have their fair share of contributions to enhance technological innovation and research. Data science-related innovations lead to advances in techniques and insights in data analysis. Similarly, innovations in AI will lead to the development of nuanced algorithms and models for better functioning. 

Differences between Data Science and Artificial Intelligence

You must have gained a clear idea about the similarities between the two fields. Now let us figure out how they differ from each other. 

1. The significant difference lies in the idea that data science involves itself in pre-processing the analysis, making predictions, and engaging in visualization. On the contrary, AI implements a predictive model to foresee events.  

2. Concerning the precision of roles under the two fields, Data science involves statistical techniques, design techniques, and development methods. On the contrary, Artificial intelligence deals with the design of algorithms, and simultaneously moves on to development, maintaining efficiency and conversions, and supervising how these designs and products are installed.

3. Data science serves the purpose of finding hidden patterns and trends in data. It aims to extract useful data, process it, derive meaning from it, and ultimately use it to make important decisions. In contrast, artificial intelligence has an autonomous take on the data as it removes the human element from the entire task and creates self-sustenance.

4. Search engines like Google and Yahoo primarily utilize data science applications. These applications are also prevalently used in numerous data-centric sectors such as marketing, advertising, IT, banking, and more. Conversely, industries such as finance, healthcare, transportation, manufacturing, logistics, and others employ artificial intelligence applications.

5. Data science leverages all types of data: structured, unstructured, and semi-structured. In contrast, artificial intelligence exclusively utilizes standardized data formats.

M.Sc. IT Can Be Your Pathway to Data Science and AI-based Jobs

By now, you must be familiar with the similarities and differences between the two prominent and expanding fields. Given the majority of the similarities between the two fields, we certainly cannot deny the potential that both fields will have when clubbed together. 
An M.Sc. in Information Technology provides you with the benefit of exploring both fields simultaneously. SKIPS University is one such university that offers M.Sc. IT, where you can get knowledge of both emerging fields and build a career out of it.