Kempegowda International Airport (KIA) operator BIAL (Bangalore International Airport Limited) have partnered with KPMG-India to leverage GenAI (Generative AI).
The BIAL said this strategic GenAI partnership with KPMG will significantly revolutionise airport operations, improve efficiency, and set new global aviation standards through adaptable and innovative AI models.
The partnership seeks to introduce a cutting-edge GenAI platform, which is uniquely designed for BIAL’s operational environment. This platform could really change the aviation industry, transforming how airports work and greatly improving customer experience.
This platform quickly handles and analyses huge amounts of live data using adaptable tech. It spots patterns and predicts trends, giving airports insights to prevent problems and keep things running smoothly.
By automating tasks, providing insights, and offering real-time AI support, this platform will make the airport more efficient, improve decisions, and increase passenger satisfaction.
The advanced platform will enhance airport operations with a consistent impact through strong data privacy, responsible AI, robust cybersecurity, and scalability.
George Fanthome, chief digital and information officer, BIAL, said, “At BIAL, we are committed to driving innovation at every level, and our association with KPMG in India is a significant step in unlocking the immense potential of GenAI at Kempegowda International Airport. GenAI’s capabilities, like real-time decision-making, predictive analytics, and adaptive intelligence, enhance our operations, leading to greater efficiency and sustainability”.
BIAL, already known for adopting cutting-edge technology, says its focus on data privacy, responsible AI, and scalability will make it a leading airport in the future.
Whether you are a student, a professional seeking a career transition, or an aspiring data scientist, you need to follow the right path to reach your desired goal. Which isn’t easy!
This is why we are here to help you with this blog, serving as your data science roadmap for 2025. Check it out and build a flourishing career in this thriving industry!
Data scientist roles are forecasted to grow by 36% between 2023 and 2033 globally, this pace is considerably quicker than the average for all occupations.
But!
What is the scope of data science in India?
The Indian data analytics market, valued at $3,551.8 million in 2024, is expected to surge to $21,286.4 million by 2030, driven by a strong growth trajectory.
This figure clearly illustrates that data science in India has a promising future. This powerful growth indicates an opportunity-filled and favourable career for aspiring data science professionals.
However, to carve a career in this field, you need a clear path that leads you to success. This is where the top data science AI course comes into play. This blog is your data science roadmap for 2025, guiding you through fundamental concepts, necessary tools, and more that you need to master this field. But first, let’s understand the basics of data science and AI (artificial intelligence)
Data Science & AI: Understanding the Basics
2025 is dramatically shaping the technological landscape with data science and AI (artificial intelligence). As the volume of data is exploding, the demand for skilled professionals is escalating rapidly. Before diving into these fields, you should understand what it exactly means.
What is Data Science?
Data science is a versatile field used to extract knowledge and valuable insights from both structured and unstructured data with the help of scientific methods, algorithms, processes, and systems.
Data scientists leverage various tools and techniques to:
Collect and clean data
Analyse data
Interpret data
Communicate findings
What is AI?
AI, or artificial intelligence, is a broader field which primarily focuses on creating computer programs and machines that are capable of performing various tasks that require human intelligence. This includes:
Learning
Reasoning
Problem-solving
Perception
Understanding language
Simply put, data science is all about extracting valuable information from sets of data, and AI is about building smart systems capable of performing human-like tasks.
Key Skills Required to Become a Data Scientist
You must have already seen the incredible impact of Data science and AI in 2025. This cutting-edge technology is driving innovation and valuable insights across numerous industries.
Let’s get down to the key skills you need to become a data scientist.
Domain Knowledge
Having a good understanding of the specific field or industry you will be working with, such as healthcare, marketing, finance, etc., is crucial. This knowledge helps you identify the right problems, understand the data limitations and context, and ensure that your solutions are actionable for businesses.
Computer Science/Programming Languages
This includes your programming abilities to handle, process, analyse, and model data using data science programming languages like Python, R, and tools like SQL. This also involves knowing computational concepts like data structures and algorithms, which are necessary for creating data pipelines and implementing models.
Mathematical Skills
Having a solid foundation in areas like Linear Algebra, Calculus, Probability, and Statistics is important. These skills provide you with a theoretical foundation for understanding how machine algorithms work, quantify uncertainty, interpret statistical results, and build deep knowledge of data and models.
Communication Skills
You should be able to clearly explain complex data findings and technical concepts easily to both non-technical and technical stakeholders, as well as your colleagues. Effective communication skills, including data storytelling and visualisation, help people understand, trust, and make decisions from your insights.
Learning Resources
You can acquire the aforementioned skills through many resources, including a data science AI course. Our full-stack data science with GenAI & ML course offers a structured curriculum with a good balance of theory and practical projects. You can acquire hands-on skills in this field and become an expert data scientist in 6 months.
Roadmap to Data Science & AI
To attain proficiency as a data scientist, you need a strong command of numerous disciplines, starting with foundational math and extending to advanced machine learning. The below-mentioned list is your data science roadmap that highlights key areas you need to explore in your journey to data science AI courses.
Mathematics
Knowledge of mathematics builds a bedrock for understanding algorithms and models in data science and AI.
Linear Algebra, Calculus, and Math Analysis: These are essential for understanding the core principles of various machine learning algorithms, particularly in optimisation and dimensionality reduction.
Differential Calculus: This is crucial for understanding techniques of optimisation used in training machine learning models.
Statistics
In-depth statistics knowledge provides tools for understanding data distribution, evaluating the performance of models, drawing inferences, etc.
Statistics & Central Limit Theorem: These are core concepts for understanding variability and creating statistical inference about populations from samples.
Probability & Sampling: Important for understanding the likelihood of events and how to pick good small groups from a big sample of data.
Hypothesis Testing: It is a fundamental and essential methodology in statistical inference and data analysis, which provides a structured approach to decision-making.
A/B Testing: It is a practical application of hypothesis testing, which is used for comparing different versions of the product or a feature.
Increasing Test Sensitivity: This is a technique to improve the power of AB testing to identify meaningful differences.
Econometrics
The econometrics field applies statistical methods to economic data, which offers key methods to analyse connections and predict future trends.
Fundamentals of Econometrics: This covers the main models and ideas to analyse economic data.
Regression: It is a powerful tool used for modelling the relationship between variables and making forecasts.
Time Series Analysis: These tools are used to understand data over time, which is crucial for predicting and identifying patterns.
Identifying Distributions: This technique is used for determining the probability distribution that represents a given set of data in the best way.
Coding
Having a strong foundation in programming is critical for data manipulation, analysis, and modelling.
Python Programming: Both experts and beginners prefer Python for data science as this is a dominant language in the AI and data science field due to its ease of use and extensive libraries.
Data Structures and Algorithms: These are core computer science concepts that help in building efficient solutions and handling data efficiently.
SQL Programming: This programming language is essential for interacting with databases and extracting meaningful data from them.
Exploratory Data Analysis
EDA, or exploratory data analysis, is another crucial step that includes understanding the data through statistical summaries and visualisation.
Data Understanding, Data Analysis and Visualisation: These are initial steps to understand the characteristics, quality, and insights from a set of data.
EDA with Python and Pandas: Using Python’s powerful Pandas library, you can do data manipulation and exploration.
EDA for Machine Learning: It focuses on insights that inform feature engineering and selection of the model.
EDA with Seaborn: Using the Seaborn library, you can create visually appealing and informative statistical graphics.
Machine Learning
ML, or machine learning, is the engine of AI. It empowers systems to learn patterns, make predictions, and enhance their performance, based on data.
Classic ML (Supervised, Unsupervised): This covers core algorithms for tasks such as classification, regression (supervised), clustering, and dimensionality reduction (unsupervised).
Advanced ML (Ensembles, Neural Networks): It explores more modern techniques, such as combining multiple models (ensembles) and introduces basic neural networks to improve performance.
Deep Learning
Deep learning is a branch of machine learning that uses AI neural networks with multiple layers to learn complex patterns from large datasets.
Fully connected networks: They form a basic structure of various neural network architectures.
Convolutional Neural Networks (CNN): These are designed to understand spatial relationships to effectively analyse image and video data.
Recurrent Neural Networks (RNN): These are designed to handle sequential information, which makes them ideal for tasks involving text, audio, and time-dependent data.
Long Short-Term Memory (LSTM): This is an advanced RNN that excels at capturing long-range dependencies in sequential data.
Transformers: The latest and highly effective architecture for NLP (natural language processing) and other types of data.
Transfer Learning: This technique uses knowledge from a pre-trained model to help a new model learn faster and better with less data on a similar task.
MLOps
MLOps, or machine learning operations, focuses on the practical factors of deploying and maintaining ML models in real-world applications.
Deployment Models: These are strategies and technologies used for creating trained models usable and accessible.
CI/CD (Continuous Integration/Continuous Deployment): It involves integrating automated processes to build, test, and deploy machine learning models.
Practical Application & Career Development
This step involves putting your theoretical knowledge into practice, which is crucial in a data science and AI course.
Importance of Hands-on Projects: To solidify your understanding, you need to apply your knowledge to real-world problems. This also helps in building a strong portfolio.
Building a Portfolio: You need to showcase your skills in data science and AI through a solid portfolio.
Competitions and Community Engagement: You should engage in data science and AI competitions to acquire practical experience. Engage yourself in the community to discover new tools and learn from experienced professionals. This will help you grow your practical skillset. You can also enrol on our data science course in Kolkata to get hands-on training in the field and acquire job-ready skills.
In Summary
The data science and AI field offers numerous career opportunities in 2025 and beyond.
From foundational knowledge to advanced techniques and practical application, this data science roadmap outlined the critical steps that will greatly help you in your career.
The present is data-driven, and the future is expected to be more analytics-driven. With a focused approach and consistent hard work, you can become a data scientist with expertise in AI.
Our 6-month blended program can help you pave your way to becoming an expert data scientist.
So, don’t wait anymore. Contact us today: 9836 423 755/ 6289 562 294 to take the first right step towards a lucrative data science career!
FAQs
What is the future of data science in 2025?
Data science is expected to be leveraged and implemented in more industries with more focus on specialised applications like real-time analytics and explainable AI. The need for skilled data scientists will remain significant.
What is the scope of AI in 2025?
AI is projected to see wider adoption in automation, solving complex challenges, personalised experiences, and intelligent systems. You will see advancements in fields like computer vision, NLP, robotics, etc.
What is the roadmap for data science AI courses?
Data science roadmap for AI courses begins with foundational mathematics, programming, statistics, and advances to data analysis, deep learning, machine learning, and specialised AI applications. Remember, working on practical projects is crucial to building a strong foundation in these fields.
Will AI replace data science jobs?
AI is expected to automate various human tasks, but it is likely to augment their roles, not replace them. Data scientists will remain in demand for solving complex problems and extracting strategic insights.
Which is the best artificial intelligence and data science course?
The best artificial intelligence and data science course for your career depends on your goals, prior knowledge, and educational background. You need to look for courses that offer industry-relevant projects, a comprehensive curriculum, career support, and experienced instructors like Karmick Institute’s data science with GenAI & ML program.
How to learn AI in 2025?
You can start with learning a programming language like Python, statistics, and mathematics. After that, move to AI fundamentals, machine learning, deep learning, hands-on projects, and consistent practice.
Which is the most in-demand AI career?
Roles like machine learning engineer, natural language processing engineer, AI scientist, and computer vision engineer are highly demanded across various domains.
Is data science worth in 2025?
Absolutely! Data science is worth it in 2025 and beyond, driven by its growing applications. Due to the increasing volume of data, there will be a strong demand for data scientists across various sectors in 2025 and in the coming future.
What skills does a data scientist need?
Data scientists need to possess essential skills, including programming (Python), machine learning, statistics, data visualisation, data wrangling, communication, and domain knowledge.
How long does it take to become a data scientist?
Becoming a data scientist depends on several factors, including educational background, prior knowledge, grasping power, mode of learning, data science AI course curriculum, etc. However, you can expect to master this field between 6 months to a few years.
BAT VC, an early-stage venture capital firm headquartered in New York. This company has revealed its intention to invest a whopping $100 million through its latest fund. Their main focus will be on Indian startups in the AI (artificial intelligence) and deep technology sectors, with a specific interest in fintech and B2B SaaS (software-as-a-service) companies.
A newly launched India-focused initiative from BAT VC will be under the leadership of three general partners, including Manish Maheshwari, who previously headed X India, formerly known as Twitter. Aditya Mishra and Ravi Metta are the other two partners.
BAT VC says India’s AI is booming, calling it a “golden era.” It’s growing fast at 32% each year and is expected to hit $23 billion by 2027. Enterprise software (SaaS) for businesses is also doing great, growing even faster at 35% yearly and is bigger than the global average.
The VC firm highlighted that investing in AI companies with connections between the US and India is very promising. This area saw a huge increase to $4.7 billion in 2023 because of the talented people, available funding, and access to markets in both countries.
Manish Maheshwari, General Partner and India Head at BAT VC, said, “My move to Bengaluru underscores our conviction in India’s potential to lead the next wave of AI-driven global growth”.
The VC firm believes that the strong interest in Fund II from big investors in the US and India shows that it is now a central investment for global players, not just a developing market.
Ravi Metta, General Partner, BAT VC, said, “Our technical depth enables us to identify high-impact AI startups early and guide them through global scale-up”.
Monday 12, May 2025: According to a report, close to 50% of Indian tech professionals receive AI (artificial intelligence) skilling support from their companies.
The job portal Naukri conducted a survey on National Technology Day, which highlighted a significant trend in India’s tech ecosystem.
With responses from more than 16,000 technology professionals across various sectors, the survey indicates a clear shift towards planned upskilling in addition to continuous self-learning.
The report indicates that AI upskilling is now mainstream among Indian professionals at all levels.
In major tech cities like Bengaluru and Gurugram, over 50% of the new graduates surveyed said they either know some basic AI or are fully involved in AI training.
In some cities, about 33% of workers are being actively trained in AI by their companies. IT/software workers get more training because their field changes fast and requires it more.
For professionals with 10-15 years of experience, GenAI was the top skill focus for 42%, increasing to 46% for those with over 15 years of experience. At the same time, many young professionals are also interested in learning about Cloud computing, DevOps, and Data Engineering. This shows that there’s a general interest in keeping up with new technologies.
Apart from job losses (worrying 18% of tech workers), the survey found bigger concerns: not enough time to learn new skills, using old tech, and small pay raises. Specifically, 20% said they don’t have enough to dedicate to upskilling.
The report also highlighted that outdated tech stacks were a significant obstacle for 17% of respondents. Furthermore, 46% of technology professionals expressed major concerns regarding low salary increments.
According to Deloitte’s recent report, India’s AI surge will require 45–50 million sq ft more data centre real estate and 40–45 TWh of additional power by 2030. This is crucial for India’s rapidly growing AI market, which is expected to hit $20–22 billion by 2027, especially given the current disparity where India holds 20% of the world’s data but only 3% of global data centre capacity.
Launched at a NITI Aayog workshop, a Deloitte report highlights India’s strengths (cost, renewables, location) for AI data centres but warns that becoming a global AI hub requires addressing gaps in infrastructure, talent, and policy.
According to the report, six key areas are crucial for establishing India’s strong AI foundation: real estate, power, connectivity, compute infrastructure, talent, and policy.
To boost data centre building, Deloitte suggests policy changes like treating them as essential services and giving them their own building rules.
The report urges policies to fast-track data centre approvals via dedicated units, clear data localisation, and specialised zones.
The report stated that, “Additionally, amending data access provisions under the Bharatiya Nagarik Suraksha Sanhita (BNSS), 2023, to reflect data centres’ operational realities and excluding data centres from surveillance scopes under the Telecommunication Act, 2023, will provide key policy support required for boosting India’s data centre capacity. “Moreover, implementing safe harbour rules for data hosting service providers and establishing data embassies will catalyse investment and spearhead India’s data centre revolution”.
Deloitte highlights that urgent investment in power sources and infrastructure, including renewables, is needed to stop India’s fast-growing AI data centres from overloading the power grid.
According to Bill McDermott, CEO of the global AI platform ServiceNow for business transformation, India is transitioning from a labour-focused approach to an AI (artificial intelligence) mindset.
During a media interaction at ServiceNow’s annual event, Knowledge 2025, in Las Vegas, which focused on AI-driven service transformation, McDermott said, “Corporations that don’t get on board with AI right now will fail”.
He said, “Autonomous agentic AI is changing everything. The 20th-century industrial complex of software stacks is now going by the wayside and being integrated into ServiceNow, so companies can run more efficiently at a lower cost and start to grow again”.
When asked about the price sensitivity of the Indian market, McDermott said that companies must be responsive to the specific needs of any market, and ServiceNow strives to be as responsive as possible.
He said, “I think India is really moving from a labour-centric mindset to an AI mindset, which means if you have brilliant labour and can apply the magic of AI, put AI to work for the people, the force multiplying effect is so big”.
McDermott said, “So the business cases are what decision-makers need to start looking at. How big of a business price is moving to a platform like ServiceNow? What do I get from it? Not what the platform costs. I think we’re moving away from how much the price is, to how much value can I extract”.
“We have come out with a very interesting programme called NowNext AI for companies that go all in with us”, he added.
“Reliance would be a great example of such a company. They have broad access rights, broad IP rights, and incredible access to all of our AI innovation, and we give it to them,” he said.
McDermott explained that their India strategy involves partnering with large firms to reduce costs and increase value for all businesses. He stressed that adopting their AI-powered platform drives productivity and is essential for companies’ survival and success in the AI era, also noting a global AI skill gap that ServiceNow aims to address through training.
Bengaluru-based Sarvam AI has released Bulbul v1, a new text-to-speech AI model that supports 11 Indian languages, including Hindi, Marathi, Punjabi, Oriya, Tamil, Bengali, Telugu, Kannada, Malayalam, and Gujarati. This model features voices with authentic and non-robotic accents.
Other than creating their own voices, users can select from six unique personalities designed for different work needs across different industries. This comes with the added benefits of faster response times and lower costs compared to alternatives such as Eleven Labs.
According to their recent blog post, Sarvam used a high-quality and diverse audio collection to train their AI model.
This dataset has audio from several speakers in many languages. It intentionally includes code-mixed language, names, short forms, and voices that sound both formal and casual for various uses.
In a significant move last week, the government selected Sarvam out of 67 applications to develop India’s first homegrown AI large language model.
Recognising the ambitious task of building a foundational AI model from scratch, the government is backing Sarvam AI with the significant computing power required.
India is witnessing significant and rapid progress in AI (artificial intelligence) across numerous industries. A recent KPMG study shows that 76% trust in this technology among Indians. The report also points to AI’s transformative force in the country, with 90% of people thinking it’s making things more accessible and effective.
Furthermore, 97% of Indians admitted to using AI at work, and 67% found it crucial for tasks; a significant 78% are concerned about its downsides. Also, 60% have already observed fewer human connections due to AI.
Akhilesh Tuteja, head of technology at KPMG in India, said, “India stands at the cusp of a remarkable AI-powered future. With 90 per cent of Indians expressing approval of AI and 94 per cent anticipating a wide range of benefits, the data reflects a strong sense of optimism about what AI can achieve for the country. This enthusiasm points to current transformation and a hopeful trajectory where AI enhances accessibility, productivity, and innovation across sectors”.
Globally, 58% of employees use AI, with 31% using it weekly or daily. In India, AI adoption is higher, with 93% report companies use it, and 97% intentionally use it at work. However, nearly half of global employees risk security by uploading sensitive data to public platforms. In India, 67% feel its significance at work, and 81% trust AI results without checking.
Globally, 66% trust AI output without checking, leading to errors in 56% of work. In India, misuse is higher: 70% of employees have used AI inappropriately, 73% have made mistakes due to it, and 72% have violated company AI policies.
A key challenge is that 57% globally and 83% in India hide their AI use, presenting it as their own work. Despite this, 83% of Indian companies have AI strategies, offer responsible AI training (86%), and have relevant policies (80%).
While 82% of Indian employees feel that AI boosts efficiency and work quality, leading to more revenue (77%), a significant portion (44%) report increased workload, stress, and pressure.
Generative AI (Gen AI) is no longer a buzzing word, it is impacting industries and redefining our tech experience. If you are curious about this revolutionary AI tool, this blog unveils the power, potential, and impact of GenAI today and in the future. Dive in to explore all the nitty gritty details of generative AI!
Generative AI–one of the most popular terms in today’s era. You get to see an unending buzz around Gen AI every now and then, showcasing its exceptional creations.
By the end of 2025, 1 in 4 companies using GenAI will likely use AI agents, and this figure is expected to double (from 25% to 50%) by 2027.
This powerful technology is increasingly becoming accessible and relevant to everyone. From creating captivating marketing copies to designing compelling visuals, Generative AI is becoming an integral tool across all domains.
But!
What actually Generative AI is? What is the potential of this groundbreaking technology? What future beholds?
Let’s break down all the complexities of this technology, explore characteristics of Generative AI, understand its practical applications, and uncover its future impact!
What is Generative AI?
“Generative models are a key enabler of machine creativity, allowing machines to go beyond what they’ve seen before and create something new.”—Ian Goodfellow
Ian Goodfellow, ex-Apple Machine Learning lead, highlighted generative AI’s core “machine creativity,” the power to produce original content, unlike other AI types.
Gen AI, or Generative AI, is a type of artificial intelligence tool. Creating original and new content based on the trained data is among the main characteristics of Generative AI. While other AI tools analyse and sort the data, Gen AI models understand the underlying patterns, create new and original things using them.
How Does Generative AI Work?
Generative AI works in a way similar to how a young child learns patterns in language. The way a child hears conversations, picks up grammar, vocabulary, and learns to put sentences together, Gen AI models absorb a large amount of “language data” including text, images, etc, and identify the statistical relationships and structures within it. Once it learns the patterns, it can create new data that’s similar to what it was trained on.
Generative AI capabilities are immense. The following are the features of generative AI:
Learning From Examples: Gen AI models look and analyse vast amounts of data to learn and analyse what things are.
Finding the Rules: Generative AI figure out how things are put together, including grammar, words for text, colours of pictures, shapes, tunes, etc.
Using Its Understanding: Once the Gen AI model understands the rules, you can ask this AI tool to create something.
Creating Something New: Generative AI uses its learning to create something new and original, not just a copy it learned from. This is among the key features of Generative AI.
Use Various Architectures: GenAI uses different architectures depending on what it needs to create.
LLMs (Large Language Models): Ideal for creating text that seems it have been written by a person.
GANs (Generative Adversarial Networks): It has two parts ‘maker’ AI and a ‘detector’ AI. One creates fake things, and the other tries to identify them. Both compete to build realistic fake images/things that look very real.
VAEs (Variational Autoencoders): Learns a simpler way to describe a set of data and utilise it to create new things.
Diffusion Models: Learns to make a clear image and more from random data.
Transformer Models: Best for understanding sequences like words in sentences, vital for text and other data types.
Popular Generative AI Tools and Platforms: Exploring Their Applications!
Generative AI gave rise to a range of fascinating tools and platforms. From brainstorming ideas, automating writing, generating creative content and more, you can find a specific Gen AI tool to assist you. The capabilities of Generative AI extend beyond simply creating content; various GenAI tools support a range of tasks.
Here is an overview of popular Gen AI tools designed for specific outcomes to understand their applications.
Text Generation
ChatGPT: A conversational AI platform by OpenAI has 400 million weekly active users worldwide. It is capable of generating human-like text for writing, learning, brainstorming, and coding.
Microsoft Copilot: An AI-powered tool by Microsoft attracted over 1 million customers in 2024. It drafts documents, summarises emails, suggests formulas, generates code, and more.
Google Gemini: A multimodal AI model by Google has 350 million monthly active users. It is capable of understanding and generating text, code, images and more.
Claude: A next-gen AI assistant by Anthropic, has 18.9 million monthly active users worldwide. It is used for content creation, question answering, and summarisation.
Image Generation
DALL·E: An AI model by ChatGPT DALL·E attracted over 1.5 million users actively creating more than 2 million images in 2024. It generates photorealistic images, paintings, and numerous artistic styles.
Midjourney: An AI image generator, Midjourney’s Discord user base grew to 19.26 million, up from 17.5 million in 2023. Based on the user prompts, Midjourney bot produces a set of stunning images.
Stable Diffusion: A text-to-image AI model, Stable Diffusion has more than 10 million users daily. It generates detailed and high-quality photorealistic visuals.
Code Generation
GitHub Copilot: An AI coding assistant by GitHub and OpenAI, GitHub Copilot has reached 15 million users. It provides real-time code suggestions and autocompletion. This AI tool can even generate entire code blocks based on comments and context.
Amazon CodeWhisperer: A machine learning powered code generator, Amazon CodeWhisperer has over 1 million active monthly users. It offers real-time code suggestions and security recommendations within the IDE (integrated development environment).
How to Use Generative AI for Learning IT Skills?
Generative AI is not only applied in industries but also boosts IT skills. Whether you are a student just starting out or a seasoned professional who wants to enhance your expertise, integrating Gen AI into your learning can help you unlock a new level of understanding in various fields like data science, web development, cybersecurity, and more.
The following are some practical methods learners can leverage Generative AI to expand their knowledge in various IT fields.
Code Generation and Explanation
AI tools like ChatGPT, Gemini, or Copilot can generate code examples based on your needs. You can also ask explanation for existing code if you encounter any unfamiliar code. Additionally, you can also ask AI for alternative ways to solve any specific programming problem. This helps you with various best practices and techniques to refine your approach.
Learning New Frameworks and Libraries
If you are starting with a new framework like Angular, Django, or React, you can ask AI to create a basic project setup with essential files and configurations.
You can inquire about common use cases of a particular function, module, or class within a library or framework. If you want to learn using specific features or components, you can also request code examples.
Data Science Exploration and Understanding
To practice machine learning and data analysis techniques, you can leverage AI tools to create dummy data with specific characteristics. You can ask AI to provide clear and concise explanations, analogies or step-by-step instructions to learn concepts like regression, classification, hypothesis testing, and more.
Also, you can leverage AI to manage missing data, encode variables in Python using libraries like Pandas and Scikit-learn.
Cybersecurity Concepts and Practice
You can ask artificial intelligence to explain complex concepts like SQL injection, XSS (cross-site scripting), or buffer overflows in simplified language with easy-to-understand examples.
Additionally, you can request Python scripts for basic penetration testing or network scanning. AI tools also provide recommendations on secure coding practices, network security configurations, password management, and more.
Web Development Fundamentals
Using AI tools, you can generate HTML, CSS, and JavaScript templates. You can get explanations for concepts like responsive design, DOM (Document Object Model), and RESTful APIs.
Additionally, you can paste code errors into the AI and understand the potential causes and recommendations to solve the issue.
Personalised Learning and Study Plans
AI tools are great in assisting you with study schedules. You can generate a learning plan for a particular IT skill, based on your career goals, current knowledge, and desired timeline.
If you find any topic complicated, you can ask for concise and easy-to-understand summaries. AI tools also give recommendations on online courses, documentation, and resources for skills that you’re seeking to master.
Benefits of Generative AI in Education and Training
Generative AI is transforming IT education and training. Here is a list of key advantages:
Personalised Learning
AI is intelligent. It can analyse students’ preferred style, knowledge gaps, and pace their learning smartly. AI can create personalised learning plans, study material, and assessments, which ensure they learn at their own pace and focus on areas that require improvement.
Generating Content
AI helps educators in generating engaging and diverse learning material, including lesson plans, exercises, quizzes, and more. This assistance significantly saves their time and helps them focus on key areas, which enhances the overall quality of educational content.
Interactive Learning Experience
As Gen AI create interactive training models, virtual labs, and game-based learning, it makes complex topics more engaging and easier to learn. Not only does this make these complex topics digestible, but it also improves students’ understanding and retention.
Streamlined Administrative Tasks
AI tools automate various educational tasks, including attendance tracking, grading, and creating reports. This significantly saves the time of educators, making them focus on more critical areas.
Real-time Feedback & Assessment
AI-powered grading systems can help educators assess students’ assignments quickly and accurately. They can provide real-time feedback to students on their work, which helps them know their mistakes and correct them promptly.
24/7 Automated Assistance
AI-powered chatbots and virtual tutors can assist students around the clock. These AI tools can answer students’ queries, explain complex topics, and provide instant feedback on their work. This rapidity helps students in making quick improvements and accelerates the overall learning process.
Challenges and Limitations of Generative AI
While Generative AI is a mighty AI tool, it comes with its own limitations and significant challenges that need to be considered. This includes ethical concerns, the spread of misinformation, and inherent technical constraints. Let’s understand these challenges in detail.
Ethical Concerns
Generative AI raises several ethical questions regarding bias in data training, which leads to unequal or unfair results, the potential misuse for creating deepfakes, and more.
Simply put, it can make fake videos, create unfair or wrong things, or even pretend to be someone, which can lead to trust issues. Another big question is, who is accountable for the harm or the problem that AI-created things cause?
Misinformation
As Gen AI has the ability to create realistic images, text, and even audio, it poses a significant risk of spreading misinformation. Furthermore, it will be really difficult to differentiate between real and fake. This technology can create fake news stories or videos that look so real, making it tough to know what to believe and what not. This can cause a lot of confusion or even harm.
Technical Limitations
While Generative AI is going through a rapid advancement, it still faces technical barriers. Gen AI models can fall short in true understanding and common sense, which leads to factually incorrect, contextually inappropriate, or nonsensical results. Additionally, it learns from what it’s already seen, leading to a lack of new ideas.
The Future of Generative AI: What’s Next?
Here is a list of the top 5 trends to watch in the future of Generative AI and its impact on the tech industry.
1. Enhanced Multimodality and Integration
Future Generative AI models will process seamlessly and create various types of data, including text, image, code, video, and more. This will lead to a more intuitive and richer user experience and deeper integration into everyday workflows and applications.
2. More Independent AI Systems
You can expect AI systems to be more sophisticated that can independently plan, collaborate, and execute tasks. It will automate the most complex processes and unlock a new level of efficiency and effectiveness across various industries.
3. AI That Reasons and Explains
Future Gen AI will have more enhanced reasoning capabilities. It will provide better transparency and improve overall decision-making processes.
These reasoning and explanation capabilities will increase trust in these tools, which will enable wider adoption in domains like finance and healthcare.
4. Highly Tailored Experience
Generative AI will offer hyper-personalised experiences to users. This cutting-edge AI tool will allow users to create deeply tailored content, suggestions, and interfaces that adapt to the unique needs and preferences in various areas like learning, online shopping, and more.
5. Wider Access to AI Tools
In future, AI tools and platforms, including Gen AI, will be easier to use. This wider access and ease of usage will allow more people, businesses, startups, etc., to leverage the full potential of Generative AI.
Concluding Thoughts
Generative AI is rapidly reforming numerous industries. From creating appealing content for marketing campaigns to accelerating drug discovery in healthcare, the capabilities of Generative AI can surpass our imagination. While ethical considerations and challenges exist and require careful consideration, the potential of Generative AI can’t be ignored.
So, should you start learning Generative AI now?
Absolutely!
Understanding its fundamental principle, applications, and staying abreast with GenAI’s transformative impact is necessary not only for tech enthusiasts but also for professionals coming from diverse backgrounds.
Generative AI is here to stay for way longer than we can imagine. Embrace this opportunity to learn this technology and build an impactful career through data science with GenAI and ML program.
Let’s discuss how we can help you tap into this revolutionary technology! Contact us today.
FAQs
How can Generative AI help in learning IT skills?
Generative AI helps with code examples, suggests learning paths, identifies relevant resources, explains complex concepts in simpler language, and even creates a learning schedule. This AI tool helps in various domains, including data science, coding, cybersecurity, web development, etc.
Is Generative AI useful for non-programmers too?
Yes, Gen AI offers numerous benefits to non-programmers, too. It helps in creating content, generating images, automating tasks, data analysis insights, and making technology more accessible and easy to understand.
Will Generative AI replace human jobs?
While Generative AI has capabilities to automate various tasks and roles, rather than replacing humans, this tool will enhance their abilities and is expected to create more jobs.
What is the salary of a data scientist skilled in AI tools?
The salary of a data scientist depends on experience, location, and skills. However, in India, data scientists skilled in AI tools can command salaries from ₹6 to ₹36 lakh per annum.
How does generative AI help students?
Generative AI can act as a personal tutor that explains complex topics in easy terms. This AI tool greatly helps students with brainstorming ideas for projects and assignments.
How long does it take to learn Generative AI?
While the time to learn Generative AI depends on your career goals and educational background, it ranges from a few months to years.
Do I need to learn Python for Generative AI?
Learning Python for Generative AI is not mandatory. However, learning this programming language can be very beneficial in the field of Gen AI and machine learning. You can check out the top reasons to learn Python to learn more.
What is the salary for Generative AI freshers?
In India, freshers in Generative AI typically earn between ₹6 and ₹12 lakhs per annum. This figure varies based on a fresher’s qualification, company size, and specific role.
How to use artificial intelligence in data science?
Artificial intelligence enhances data science by finding patterns and making better predictions from sets of data. Data scientists use AI to clean data, pick the right analysis methods, and create the best predictive models.
As AI (artificial intelligence) grows exponentially, discussions are taking place regarding the doubtful future of human employment, as this technology has the potential to both enhance productivity and displace workers.
Last month, the UNCTAD (United Nations agency for trade and development) warned that AI could impact up to 40% of jobs worldwide.
A McKinsey & Company report projects that AI automation could lead to the displacement of 400 to 800 million jobs globally within the next five years, depending on its adoption speed. This change would lead about 14% of the global workforce (375 million workers) to find completely new jobs.
An SEO.AI report shows that 14% of the workforce has been affected by AI, but the impact was not as severe as expected.
According to a National Bureau of Economic Research report, automation has been a huge factor in why blue-collar wages in the US have not increased or have even decreased since 1980. It’s responsible for 50% to 70% of the changes in wages.
According to the UN and Atomberg’s Arindam Paul, many underestimate the threat of AI, which could eliminate 40-50% of white-collar jobs in India’s IT and BPO industries – a different impact than previous tech had on blue-collar roles.
Top AI figures like Bill Gates, Sam Altman, and Sundar Pichai have warned about AI risks in an open letter. Comparing AI threats to pandemics and nuclear warfare, these experts have urged urgent global action.
While AI job loss is a concern, many workers see its benefits. SnapLogic found that 81% feel AI enhances their work. In India, despite 74% fearing AI, 94% believe that AI will boost their career, and 83% want to offload tasks to it. Experts urge policies for worker transition and new job growth.
info@karmickinstitute.com
+919836423755 
+919836423755
Login
