It is quite hard for budding developers to decide whether they wish to become data scientists or if they wish to contribute to automation and building smarter machines. Both are highly desirable job roles with enormous scope for growth. Data Science and AI are both valuable implementations of computing and technology. The two fields are highly dependent on each other for viable business or IT solutions that are sustainable in the long run. Data Science and AI are massive fields on their own, boasting multiple branches and sub-divisions. Both Data scientists and AI engineers must be proficient in a few common skills and topics, however, both are tasked with an entirely different set of responsibilities. Data scientists focus on data, work with data and work for data, while AI engineers implement concepts (algorithms) such as Artificial Neural Networks to develop AI-supported services or machines. Picture by Burak Kebapci from Pexels . Fundamental differences between Data Science and AI The main difference between the two fields of study can be observed in the tools and techniques used by AI engineers and Data Scientists. While both the branches require mathematics, statistics, and programming, both the fields implement them differently. AI engineers are programming experts who program machines to implement automation or learning algorithms. Meanwhile, Data Scientists use statistical and analytical tools in order to extract data, store data and gain insights from data. Let us look at the most important differences between the two. In development, AI is generally the prime focus while Data Science simply supports or empowers IT and other developmental processes. AI engineers are required to work with programming and Machine Learning. Data Scientists use mathematics, statistics, and analytical tools, very rarely requiring programming. Data Science is a medium-level data manipulation process while AI is a high-level data manipulation process. AI uses algorithms and frameworks in order to be implemented while Data Science requires techniques and tools. Data Science uses patterns from data to gather insights while AI uses Machine Learning and advanced predictive abilities to provide insights. AI is just a tool for Data Scientists while AI is the prime focus of AI engineers. Finally, Data Science concentrates on data while AI focuses on modelling the data for further uses. The job scenario for Data scientists or AI engineers and the different job roles With many new companies and innovations entering the market every year that are backed by AI, the demand for AI engineers is higher than ever now. With most larger companies having adopted AI completely or at least partially, MNCs and conglomerates around the world are in dire need of skilled analysts, developers, managers and AI architects. A survey conducted by Garner had concluded that of all registered organisations have adopted AI in some manner. 37% The number of companies that had started using AI had seen an enormous 270% increase from 2016 to 2019. 2019 almost saw the number of enterprises implementing AI grow by 300% from 2018. However, many of these companies are being set back due to the lack of skilled AI engineers and wish to see talented developers in the fields of Robotics, IoT and especially Augmented technology. Even though the largest IT firms have a huge number of human resources to power their concepts and processes, they still require a huge number of skilled AI engineers to support their businesses and services. The demand for AI engineers will keep growing as the global AI market sees exponential growth in the coming years, reaching billion by 2026. The current value of the global AI market is $58.3 billion in 2021 and it will experience a Compound Annual Growth Rate (CAGR) of 39.7% till 2026. Even smaller companies and startups are quick to adopt AI into their business processes to either cut costs or provide services that can only be sustained using AI. $309.6 Let’s check the various job roles that AI engineers can fulfill: AI Data Scientist Machine Learning Engineer Applied Machine Learning Engineer AI Research Scientist AI Data Analyst Big Data Engineer (AI specialist) AI Developers AI Debuggers AI Product Manager AI Architect Robotics Scientist Business Intelligence Developer Data scientists are in massive demand as well, with large volumes of data being generated every second. Expert data scientists are required to extract, clean, and sort this unstructured data with various attributes so that companies can effectively use the data to either reflect on metrics or to make better data-centric decisions. Data scientists are not just required by IT companies or large businesses but are extensively being hired by smaller companies as well. Companies and employers are growing more data conscious, thus wishing to understand their businesses, customers and the market better, in turn hiring business analysts and data analysts to provide them with the required insights. The global Data Science market will experience a CAGR of during the period between 2020 and 2027. We have already entered an age where there are more Data Scientist job roles that need to be fulfilled but there are not enough skilled personnel. Companies have begun relying on Data Science incredibly more in the past few years and now they demand to employ more individuals to fill roles such as  Data analysts, business analysts or database administrators. 29.8% The US Bureau of Labour Statistics reports that there will be a increase in employment till the end of this decade. The job prospects are excellent for both freshers and experts, especially with the growth rate being one of the fastest among all occupations. 15% Data scientists fulfill various job roles that allow companies to forecast events and plan out strategies with the help of data as well. Let us check the different roles that data scientists are offered. Data Analyst Business Analyst Database Administrator Data Governance Specialists Data Engineers Machine Learning Engineer (Data and modelling specialist) Data Architect Business Analyst Statistician Big Data Engineer Data and Analytics Manager The salaries offered to professionals working in Data Science According to the Economic Research Institute, AI engineers in Germany and France get compensated annually with an average salary of 84,899 euros and euros, respectively. 75,291 They have also reported that AI engineers in India get paid an average of Rs. 1,100,287 annually. This figure has been predicted to go up to Rs. 1,479,390 by 2026, an unprecedented 34% increase. Glassdoor reports similar numbers, with AI engineers in India declaring an annual salary of Rs. on average. Taking the salaries of AI engineers from the US into account, the national average salary is about . AI engineers from the UK get a handsome salary of annually on average while Australian AI engineers get paid about per annum on average. 8,90,870 $1,17,041 £55,093 $1,06,000 In India, Business analysts are paid Rs. annually on average while senior Business analysts are paid around Rs. annually. Data engineers in India earn salaries that are quite close to the business analysts while senior Data engineers earn about Rs. annually on average. Freshers and junior Data engineers enjoy an average annual salary of Rs. . Data analysts earn an average of Rs. per annum while experienced Data analysts can earn Rs. annually on average. 7,000,000 10,00,000 14,23,677 7,87,489 5,75,500 8,15,595 Compensations paid to AI engineers across the world Data scientists too, are offered very attractive salaries by employers around the world. The Economic Research Institute reports that Data scientists in the US are paid an average of a year and might see an increase of 13% by 2026, making it an average of $131,247 annually. Indian Data scientists are being paid Rs. annually on average and will experience a 34% growth by 2026 in terms of their salaries, similar to what the AI engineers will experience. Data scientists from the United Kingdom enjoy respectable salaries as well, being reported to get paid an average of annually. $116,003 1,149,131 £68,846 Glassdoor reports similar numbers for both and the . India US AI engineers enjoy respectable salaries in tech-centric countries such as India, with junior machine learning engineers earning about Rs. annually on an average and their senior counterparts enjoying an average salary of about Rs. per annum. 7,50,000 13,00,000 Big Data architects who are experienced with AI or work with AI are offered salaries of Rs. per annum on average. Big Data engineers who work with AI are paid about Rs. per annum in India on average. 22,23,454 8,00,000 Applications running on Data Science and AI There are multiple applications that are either based on the concepts of Data Science or are built to promote Data Science methodologies and techniques. Let’s look at some of the more common applications that Data Science had a heavy influence on. Microsoft Excel Excel does promote concepts of Data Science into its ecosystem. Ranging from filtering data to handling errors, Excel does it all. Many Data Science-backed processes are executed using Excel while Excel itself follows many Data Science methodologies. Excel, along with its advanced filtering functions and range determination capabilities allows data scientists to conduct analysis as well. Database Management Systems and Server Tools DBMS or these systems that allow the storing and manipulation of data are all built on the foundations of Data Science. Starting from simple CRUD operations to advanced server applications, Data Science is the fundamental core of these applications. MySQL, Microsoft Access, Oracle, SQL Server and RDBMS are all DBMS applications that function by paying heed to the concepts of Data Science. Business Analysis Applications Applications that are used for business analytics and data analytics are all implementations of Data Science. This is due to the data-backed nature of the insights and the evaluation methods being data-centric. Data Science allows these tools to standardise factual elements, consider variables and provide metrics for financial activities, market conditions, profit, sales and many other areas. Data Science arms these applications with the tools to work with data and build valuable insights. For example, Microsoft Power Bi and SAP Business Objects. Survey and Feedback tools Survey services and feedback are all supported by Data Science. Starting from how the data is generated to organising and structuring the data, these services are heavily dependent on Data Science to function effectively. For instance, Google forms use a simple implementation of Data Science to divide, organise, and categorise results. AI can be observed everywhere now, especially with thousands of services and applications being powered by AI or being automated. AI can be noticed in web services, cloud services, development and especially in games. Let us check some popular applications that use concepts of AI, Machine Learning, or automation. Video Games AI is used extensively in video game mechanics and to simulate real-world physics in games. Advanced NPCs or Non-Player Characters in video games are all powered by AI. Even computer opponents in simple games such as chess are all implementations of AI. Chatbots and custom Bots Chatbots are an advanced implementation of AI that are slowly making human-machine conversations more effective. Machine Learning powers technologies that promote the development of bots that can not only converse with humans but also interact with business processes and tools. For instance, bots are used for opening mails and documents or for relaying information in SaaS applications. Testing bots are also used for finding errors and evaluating the viability of solutions. Music and Video Platforms Most applications or websites built for music or video streaming are all powered by AI. From recommendations based on user preferences and history to sorting music or video depending on their genres and tags, these platforms are all governed by AI. Youtube, Spotify and most other platforms use AI. Forecasting Tools and Business Intelligence applications Most variants of tools that offer predictions and analytics are reliant on AI. Without AI, tools will not be able to effectively use the available data to determine probable events or results. For example, many ERP software and SaaS cloud solutions provide forecasting abilities using AI. Industries using AI and the demand for AI The demand for AI is increasing on a daily basis, with more organisations from public or private sectors beginning to adopt AI into their ecosystem and businesses. The different applications of AI can be observed in advertising, Social Media, chatbots, smart devices, games, development, engineering and production. For instance, AI powers automated machines, empowering the unsupervised operation of factory systems ( production lines). AI has a huge presence in online services, allowing smart recommendations of music, videos, products alongside extracting accurate or personalised search results. Online E-commerce giants such as Flipkart use AI in order to provide smart product recommendations to their customers while Indian tech-giants such as Data Consultancy Services or Wipro utilise AI to maintain employee records and determine the perfect human assets for various projects. Even news channels or government departments such as the Indian Meteorological Institute use AI to predict the weather and understand seismic activities. AI supports various business processes for companies as well as allows them to provide automated services to their customers. Due to this reason, AI is not limited to any particular industry and can be observed being used across all kinds of sectors. Whether it is agricultural, medical, technological, financial or government sectors, AI can be noticed everywhere now. Companies use AI to increase productivity as well as accuracy, thus adopting frameworks such as RPA or Robotic Process Automation, slowly moving closer to hybrid workforces and decreasing the need for human supervision in every field. Max Healthcare, in India, uses RPA to streamline many high volume processes like data entry and claims processing. Industries using Data Science and its growing importance Data science is growing equally, especially with the advent of Big data and massive amounts of data generated every day. More companies are becoming data-conscious and wish to incorporate the knowledge that can be acquired from data. Companies need to effectively generate, extract and utilise this data for their benefits and for deep insights, thus, creating a dire need for data scientists and individuals skilled in Data Science. In the case of Data Science as well, the range of industries using Data Science tools and techniques is not limited. Companies and businesses from all sectors use Data Science to conduct analytics, handle data, build strategies and then forecast. The scope of Data Science is limitless, from financial firms using Data Science to study market fluctuations or financial situations, the applications of Data Science can be seen even in advertising, research, or healthcare. Pharmaceutical companies and research institutes in India or across the globe use Data Science to power medical research and understand the relationship between diseases, medication, and patient behaviour. For instance, Tricog Health Services Private Limited uses Data Science to eliminate time constraints while understanding heart diseases and also to come up with safer and more effective systems or drugs. RBI or the Reserve Bank of India uses Data Science to maintain financial records, analyse the market or economic data, and to be able to approve transactions, build policies or fight inflation effectively. Conclusion Even though many of us confuse the two branches of science with each other, it is very easy to understand how these two fields are immensely different. They are focused on different aspects of computational processes and are geared towards bringing different advantages to companies or development. AI is arguably more processing-intensive and complex, however, without Data Science, data will lose its quality, efficiency and cannot be used effectively even when developing AI.

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