How Should Data Scientists Evolve with the Rise of AI?

How Should Data Scientists Evolve with the Rise of AI?

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Jaya Sharma
Assistant Manager - Content
Updated on Dec 30, 2022 05:12 IST

Artificial Intelligence (AI) is the talk of the technology town, with everyone becoming excited over it lately. Though AI is still in the exploratory mode, there have been various areas where it has established itself as a vital component and has brought revolutionary changes in some industries. The best example is that of ‘automation in the IT industry, which some say is the cause of massive layoffs seen in various organizations.


In order to achieve better profitability and revenue growth, some of the prominent IT companies in India have started automating low-skilled jobs. Wipro became the first Indian IT firm to launch an artificial intelligence platform, Holmes, last year. After that, TCS launched its artificial intelligence platform, Ignio, and Infosys rolled out its artificial intelligence platform – Mano.

This article will cover:

What is Artificial Intelligence?

Artificial intelligence (AI) is an area of computer science that emphasizes the creation of intelligent machines that work and react like humans. Some of the activities computers with artificial intelligence are designed to include:

  • Speech recognition
  • Learning
  • Planning
  • Problem-solving

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Machine learning is the connection between data science and AI. It is the process of learning from data over a period of time. AIs require a huge pool of data to do even the simple of things. Hence the connection between data science, machine learning, and artificial intelligence.

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How is AI becoming a threat to data scientists?

Just 4 years ago Harvard Business Review dubbed ‘Data Scientist’ as “the sexiest job in the 21st century” and it became one of the most lucrative job titles. Due to the shortages of good data science professionals, a lot of companies pay attractive packages to hire an expert.

Fast-forward to the current scenario in the technology space, AI has suddenly created deep impacts on various areas. It appears to be a threat to the jobs of data scientists; as shown by MIT’s “Data Science Machine”. It offers the capability to build predictive computer models by identifying relevant features in the raw data. However, according to an expert, “some humans can beat the machine and it would be naïve to say that data scientists do not have any value.”

The convergence of big data with AI has also led to the belief that it will replace the pool of data scientists who are limited to working on sample sets of data.


Data Science and Artificial Intelligence

For any Data Scientist, Artificial Intelligence is a procedure that lies on top of other methodologies for analyzing the data. This is analogized via Maslow’s Hierarchy of needs in which each component in the pyramid represents a data operation which is performed by a Data Scientist.

Why and How should data scientists evolve?

Data scientists need to evolve in order to survive along with the world of AI. The role of data scientists will assume a different level of importance and they should, instead, take the help of AI to create better predictive models. The young generation of data scientists should be prepared for the AI revolution and get trained in more advanced deep learning approaches. The same path as software development has been followed by data scientists, automating the lower level tasks and moving up to the abstraction level. They should start focussing on the higher level and complex tasks.

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AI and other technologies can bring revolutionary changes in the future. However, professionals should always be ready to keep their skills at the highest level. Advanced level data science courses developed by experts in the industry and will help you to keep up-to-date with the latest skills and techniques.


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How can you pursue a career in artificial intelligence?

For pursing a career in AI, you would be required to have technical skills including: Aspirants with a solid understanding of software engineering and computer science can go for a career in artificial intelligence.u00a0 Knowledge of Python and R programming languages Command over Linux and Unix environments. Work experience in ML, NLP, neural network architectures, image processing and deep learning.

What skill sets are required for becoming a data scientist?

This is a technical role and, thus, needs technical expertise. To become a data scientist, you would be required to: Have a background in mathematics and statistics. Be skilled in programming languages such as Python and R. have in-depth knowledge on the usage of data management, its cleaning and mining. know about relational databases such as SQL. Be acquainted with data visualization tools such as Tableau and QlikView. Have a strong grasp of big data tools, including Hadoop, Hive, Pig and Spark.

How is artificial intelligence used in data science?

Artificial intelligence is a set of technologies that excel at extracting patterns and insights from large sets of data. Based on this information, AI makes predictions. This includes data analytics from Google Analytics, content management systems, automation platforms, CRMs, etc.

Differentiate between Data Science and Artificial Intelligence?

Data Science and Artificial Intelligence differ on the basis of the following six factors: Data Science is a process that involves analysis, preprocessing, visualization and prediction. Artificial Intelligence, on the other hand, is the implementation of predictive models for forecasting future events. Data Science comprises a number of statistical techniques while AI uses computer algorithms. It involves multiple steps for data analysis and generating insights based on it. This is the reason why Data Science makes use of more tools in comparison with the ones used in AI. Data Science is about identifying hidden patterns within the data. AI is all about imparting autonomy to data models. We build models that use statistical insights through Data Science. On the other hand, AI is used to build models that emulate cognition as well as human understanding. AI involves a high degree of scientific processing, whereas data science does not.

How does AI relate to data science?

Data Science has several statistical techniques. On the other hand, AI uses computer algorithms and is about imparting autonomy to data models. We can build models that use statistical insights using Data Science. AI is used for building models that can emulate cognition and human understanding.

What are the threats associated with AI?

Even if there are many advantages of Artificial Intelligence, few disadvantages still persist: Job loss with replacement of manual tasks with AI-based tools. The algorithmic bias Market volatility. Weapons automatization. Privacy violations with clever invasive techniques Misuse of 'Deepfakes'

How does AI benefit data scientists?

AI automates the process of report generation and makes the data easy to understand with the use of Natural Language Generation. When Natural Language Query (NLQ) is used, AI enables everyone within the organization to find answers intuitively and extract insights from the data, thereby leading to improvement in data literacy. This also allows free time for data scientists.

About the Author
Jaya Sharma
Assistant Manager - Content

Jaya is a writer with an experience of over 5 years in content creation and marketing. Her writing style is versatile since she likes to write as per the requirement of the domain. She has worked on Technology, Fina... Read Full Bio