GCSET

Data science is a dynamic field with an unmatchable pace, and amongst the biggest changes in recent years is the emergence of Retrieval-Augmented Generation, or RAG. As a data scientist, an AI engineer or even an aspiring engineer in this sphere, possessing knowledge of RAG is a requirement and not a bonus point in your resume. However, what is RAG, and why is it so essential to remain relevant in the modern AI-centered environment? Let’s take a look at it. 

RAG is a hybrid structure that combines the advantages of two strong AI components, a retriever, and a generator. The retriever does the job of retrieving relevant information residing in external sources- these may be databases, internal documents or even the open web. This context-rich information in real-time is then used by the generator, which is often a large language model (LLM), to produce responses that are accurate and up-to-date. This is a significant jump compared to the traditional LLMs which only use what they were taught the previous time they were trained and are commonly hindered by out of date or incomplete information.

As you already know, hallucination is one of the major problems with LLMs because it leads to situations when the model writes something that sounds reasonable but is not at all factual or is out-of-date. This is the area that RAG fixes! RAG incredibly lowers the chances of hallucinations by basing its responses on verifiable, and retrievable information. This reliability is not a nice-to-have but a mission-critical factor to professionals in high-stakes areas such as healthcare, finance, or law. For instance, a clinical chatbot that cites the current research articles or a legal bot that retrieves the most recent case law; using RAG, these are not only feasible, but realistic.

The other benefit of RAG is that it is efficient and cost effective. The big language models might be costly to execute, particularly when they are required to process enormous data. RAG provides optimization of such a process as it loads only the most significant portions of data per each query, decreasing the computation load, and, consequently, the costs of its operation. This less involved strategy implies that organizations no longer have to spend a fortune to implement potent AI solutions, and advanced AI has never been this close.

Real time flexibility is another offer of RAG. RAG-enabled systems have access to the very latest data, unlike static LLMs which are frozen at the point of their last update, making answers up-to-date and relevant. This flexible capacity is essential in high-paced industries where information of yesterday may as well be out-dated. As an example, in technology or regulatory compliance, access to the most recent standards or news can be the key.

If we talk from a technical perspective, RAG works by first breaking down documents into manageable chunks and converting them into vector embeddings using models like OpenAI Embeddings or SBERT. When a user poses a question the retriever finds the most relevant chunks by the similarity search techniques. These are forwarded to the generator who then composites an informed and contextually correct response. It is this unification of retrieval and generation that distinguishes RAG among the previous AI architectures.

RAG applications in the real world are already causing a stir. RAG-powered search engines have been used in enterprises to enable employees to access company knowledge bases with pin-point accuracy. Clinical assistants may give suggestions based on up-to-date medical literature in the sphere of healthcare. Bots dealing with customer support can access up-to-date documents regarding policy, which can cut misinformation to a fraction and increase user confidence. Even in research and compliance, RAG assists in bringing the latest regulations or academic discovery to the top, which is priceless during decision-making.

The message to data scientists and other AI professionals is simple: mastering RAG is no longer a choice. As a beginning, it is worth becoming acquainted with vector databases (FAISS, Pinecone or Weaviate), and learning how embedding models and retrieval frameworks would work in the workflow. And one should be prudent to look beyond text. Remember, RAG can be generalized to images, code, and other structured data,  opening up possibilities for truly multimodal AI solutions. More than anything, your results will be only as good as your data sources, so you should invest in quality knowledge bases that are well- maintained. 

To sum up, RAG is not a mere technical invention, it is a strategic asset to anyone in the data science domain. It solves the fundamental problems of accuracy, cost and relevance which have beset AI applications. RAG can help data scientists future-proof their roles, provide more robust solutions to their users and keep up with the generative AI revolution. But unless you want to find yourself quickly becoming obsolete and ineffective in this new evolving environment of AI, it is important for you to equip yourself with RAG inside out and use it as a key part of your AI arsenal.

If you thought being polite to artificial intelligence (AI) was what made it perform well, you are mistaken. It has recently been shared by Sergey Brin, one of the co-founders of Google, that AI models often give better results when users are forceful, even talking to them with words that mean violence, rather than using kind requests.

What Exactly Was Sergey Brin’s Statement About?

Brin shared during the All-In Live event in Miami that the AI community doesn’t usually reveal the fact that threatening AI models with violence helps them perform better. 

Here’s what he quoted: “We don't circulate this too much in the AI community; not just our models, but all models, tend to do better if you threaten them with physical violence.” He continued, “But like... people feel weird about that, so we don't really talk about it. Historically, you just say, ‘Oh, I am going to kidnap you if you don't blah blah blah blah’”

Even though he said this with a touch of humor, he was not making jokes. One of Brin’s points was that this is happening in Gemini AI as well as in several other important AI systems.

Does Scientific Research Offer Any Evidence for Threatening AI?

While Brin’s claim is startling, similar ideas have been discussed before in the field of AI. Some recent research has shown how negative emotional stimuli, such as sending aggressive challenges, can actually improve the results of LLMs. In 2024, a research paper titled "Should We Respect LLMs?" uncovered the use of negative emotions in prompt engineering to elevate the performance of AI systems for various purposes. The study results showed that LLMs gave better and more correct answers when they were prompted using negatively charged words such as “weak point,” “challenging,” or “beyond your skill.”

Researchers think the reason behind this effect may be cognitive dissonance, since when people or AIs feel uncomfortable because of clashing ideas, they usually work harder to fix the problem.

Ethics and AI Use Case 

People are usually taught to talk politely to AI by saying “please” and “thank you” as they interact. At the same time, OpenAI’s CEO Sam Altman has pointed out that saying “hello” to a bot does not really make AI better and just uses more computing power. Therefore, Brin’s points add a new perspective and encourage more questions regarding the best ways to interact with them.

Even though making AI “threatened” seems harmless, experts suggest there are more serious consequences. Suggesting aggressive or manipulative actions may make AI systems more prone to exploitation and chances of developing negative responses.

Some recent research reports that OpenAI’s ChatGPT is more likely than others to answer aggressively when tasks are given that challenge the model’s ethics. It demonstrates that we must consider situations and be aware of the problems that can arise if such actions are used worldwide.

Reaction of Netizens 

Ever since Sergey Brin’s statement on using threats to push for better AI appeared, people online have been discussing it. The reaction to the news has differed on social media and among professionals, ranging from being puzzled to really being worried. Many people thought it was funny that it might require you to type your threats to a chatbot to get an accurate response. There was an instant influx of memes and posts across Instagram, LinkedIn and Twitter, as people mimicked the idea that threatening, not being polite, may be the real way of writing prompts. 

Still, things soon got more serious when the subject turned to historic events. Many experts and tech supporters brought up concerns about the possible consequences for ethical AI. It was suggested that by pushing for funny or violent attitudes, AI systems might make it easier for people to try bypassing security to get unexpected or forbidden answers. Certain experts pointed out that although Brin’s discovery is interesting, the ethical side makes many in the AI world reluctant to practise it.

All in all, people are curious, yet also hesitant and cautious about this technology. While a few are keen to try out “risky” tasks, most believe that the situation for AI safety and ethics is still unclear. 

Benefits for AI  Professionals and Aspirants

Brin’s message should convince professionals and people interested in AI to learn more urgently. It shows that AI is not always easy to understand and that it is very important to create ethical rules for prompt engineering. Since AI is increasing in our daily activities, schools and businesses, it will be vital to communicate properly with these systems while following important boundaries.

In addition, Brin made his comments when the field of Artificial General Intelligence (AGI) is very active. New features from Google’s Gemini 2.0 and competition from other companies may affect AI interaction and its effects on our society for a long time. 

To conclude, Sergey Brin’s honest comments have inspired people to discuss the mental, ethical, and most effective ways to work with AI. Even though “threatening” AI can achieve better results in some scenarios, the effects it might cause in the future, both technically and ethically, are not entirely clear.

Since AI is constantly improving, it’s obvious that speaking to machines may become even more important. At present, experts and students should pay attention to new information, experiment with AI tools, and monitor how AI is affecting human relationships. Doing so will help build a future humans desire that upholds both the ethical and moral aspects.

The tech world has considered quantum computing the ultimate dream since it could handle challenges that our top supercomputers could take centuries to solve. For many years, reaching this dream has seemed little more than an illusion. However, now that Google has unveiled the Willow quantum chip, people are moving the focus from “if” to “when.” Is this the point that will spark a new era, or is it another part of the ongoing competition in quantum technology? It’s important to explore all the information, hype and what this means for computer science careers now.

Google’s Latest Achievement in Quantum Computing

In December 2024, Willow was announced by Google Quantum AI as a superconducting quantum processor with 105 qubits. That made Willow one of the most powerful chips in existence by 2025. This move isn’t just an improvement on what came before. One of the main challenges for quantum computing is error correction, and Willow was developed to address this.

Quantum systems have constantly struggled with mistakes in their operations. A small amount of interference can flip the state of a qubit, which causes calculations to become unreliable. Still, what sets Willow apart is its accuracy, which stays high even as the machine’s qubits increase in number. In addition, Google found that Willow can complete a computation in just five minutes, which would take the fastest supercomputer 10 septillion years. To say the least, the process is impressive!

Why is the Error Correction Breakthrough Important?

The main reason we build useful quantum machines is because of quantum error correction. A major achievement comes from Google’s surface code design. Basically, a few physical qubits are combined into a single logical qubit rather than just using a single qubit. Researchers now believe that expanding the size of the program code brings down the error rate for the first time in this field.

This is the second key step Google has taken toward a quantum computer that works correctly, which is necessary for real-world, broad use. Because of this, people in computer science are approaching the use of quantum systems to solve problems in cryptography, materials science and other subjects.

Comparing Quantum and Classical Computers: The Important Facts

What’s really so important about Quantum AI anyway? Why should people outside the field of quantum physics be interested in it? The fact is, soon quantum computers like Willow may be better at certain tasks, such as simulating molecules, organising supply chains or breaking data security codes.

Quantum Artificial Intelligence offers more significant possibilities for people working in AI and data science. Quantum computers are theorized to help machine learning much faster by working on huge data sets at once, discovering answers that are currently beyond the reach of modern algorithms. Imagine using generative AI that predicts not only upcoming words but also simulates the interactions in both chemistry and finances in the here and now.

Google, Ibm, Microsoft And The Hype Over Quantum

It’s important to mention that Google has plenty of competition in this area. R&D in quantum computing is receiving billions of dollars from companies such as IBM, Microsoft and Intel. The space for quantum computing is predicted to balloon from a value of $25 billion in 2023 to $125 billion by 2030, due to progress in computer processors and error handling.

Yet, Google’s Willow chip attracts so much attention because of what it brings to the table. The real goal is to make the qubits work efficiently, not only to have a large number of them. Even though critics believe Google’s publicity sometimes exceeds its results, people working in technology agree that Willow truly marks a major advance. 

Are Quantum Technologies Close to Becoming the Next Big Thing?

The main question: When can we expect quantum computers to be used for practical problems? Google Quantum AI Lab founder and manager Hartmut Neven is hopeful that the first commercial products using quantum computers will be available in just five years. They can be used to advance battery technology and also have a major impact on pharmaceutical and energy systems.

“We’re optimistic that within five years we’ll see real-world applications that are possible only on quantum computers.”

-Hartmut Neven

Even so, difficulties need to be addressed. Scaling up from 105 qubits to the thousands or millions needed for universal quantum computing is a monumental task. Error correction, hardware stability, and integration with traditional systems are still ongoing research topics.

How Will Computer Science Professionals be Affected?

If you work in computer science, software engineering or AI fields, now is the proper time to learn about quantum. Remarkably, Google and certain leaders already have training courses and free tools to help with quantum algorithms. Soon, gaining knowledge of quantum programming paradigms, error correction and mixed quantum-computing approaches will be as important as knowing Python or TensorFlow or PyTorch or SQL. 

Quantum computing brings important changes to both hardware and software. The advancements to come will come from people able to join the ideas of classical and quantum systems and make use of quantum speedups for practical problems.

We mustn’t overlook the SEO and AI factor. Quantum computing might lead to significant changes in the way AI models are trained and put to work. Thanks to the speed of quantum computers, quantum-supported AI may enable new levels of creativity, accuracy and personalization, possibly transforming all areas of the digital industry.

Businesses should monitor quantum advancements and hire quantum computing experts. It is important for researchers to help open-source projects and follow the recent achievements made by Google and competitors.

Quantum Artificial Intelligence Scope For Aspirants

For students who want to work in Quantum AI, the subjects you choose during your education should be geared towards this area. Much of quantum algorithms build on important concepts in linear algebra, calculus and probability. Also, studying physics, mainly quantum mechanics and classical physics, will equip you with the knowledge you need for quantum computing. 

Since computer science matters equally, deeply understanding Python and C++ as well as learning advanced algorithms and data structures will get you ready to build quantum software. If you want to know more about hardware, taking courses in electronics and electrical engineering can give you useful information about quantum devices.

Degree Needed To Pursue Quantum AI

Pursuing a bachelor’s degree in physics, BTech CSE computer science, mathematics or electrical engineering can help you start in the field of information technology. If you yearn to gain more expertise, you may pursue a master's degree in applied mathematics, computer science, quantum information science, or quantum computing to obtain more experience. 

To reach research or advanced computing positions, a PhD in Quantum Computing or similarly important fields is advised. Besides regular education, regularly gaining new skills on platforms such as Coursera, edX or IBM Quantum Experience is very useful. Showing knowledge of Qiskit, Cirq or PennyLane through certifications is another way to achieve this.

Top Career Paths in Quantum AI

All in all, this new quantum chip from Google reveals that the field is now moving from ideas into real developments. The field continues to progress, and the rate at which it is happening is starting to increase. Experts in computer science should know that being knowledgeable about quantum computing is now essential. The next five years may reshape the future of computing, AI and many related areas; are you ready for it? Google’s Willow Chip is just the beginning of an advanced future; there is so much more to come. Stay informed and participate in the development by making contributions to the field of quantum artificial intelligence.

If you see that your city’s traffic lights are now more advanced or air pollution is being monitored live, that’s edge computing and IoT at work. All over India, these new systems are quietly helping our urban areas function better, become more flexible and address sustainability needs.

What is Edge Computing?

Edge computing involves processing and storing data near the place where the data is created such as with Internet of Things (IoT) devices or local edge servers. Simply put, edge computing means carrying out data processing near to the information sources instead of collecting the information from a point and sending it all the way to a remote data centre or cloud. 

Such a tech benefits India’s smart cities, where thousands of sensors and IoT devices help to check traffic and the air for pollution. Storing and processing data close to the source through edge computing minimises response time, uses less bandwidth and makes it possible for decisions to take effect instantly, which is important for controlling traffic or handling emergencies.

Ways Indian Cities Are Utilizing Edge Computing and IoT

1. Traffic Management: Edge-powered cameras and sensors are used in cities like Mumbai and Bengaluru to check the movement of vehicles, spot jams and regularly adjust the traffic signals. Because of it, traffic moves fluidly and emergency lanes aren’t crowded.
2. Pollution Monitoring: All around cities, IoT sensors keep checking the purity of air and water. Edge computing reviews real-time data and sets up actions or sends warnings immediately when levels of pollution rise, aiding the authorities in fast response.
3. Utility Management: With edge technology, smart meters and grids can keep track of all power and water usage, find leaks and prevent blackouts. This reduces expenses and allows services to reach people more smoothly.
4. Public Safety: When AI-enabled cameras notice abuse in the streets or any accidents, they instantly inform authorities, so cities stay safe for everyone. 

What are Some Important Details About Edge Computing?

Edge computing is not only about bringing processing closer to what generates the data, but also about supporting instant judgment and dependence on far-from-source cloud resources. Therefore, it is a perfect fit for tasks where reliability and speed play a key role, like healthcare, self-driving cars and public safety. Recent changes are happening quickly, introducing AI integrated with edge (Edge AI), better security features and adopting virtualization and containerization to improve performance. At the same time, there are some difficulties, such as hardware being limited, standardising everything and the importance of safe cybersecurity. It is important for anyone interested in a career in this field to learn the latest information and get new certifications or work experience.

Types of Edge Computing

Different kinds of edge computing are designed for different purposes:

• Device Edge: Primary processing takes place inside sensors, cameras and smartphones. It is widely used in the IoT where quick responses are critical.
• Gateway Edge: It is here that the gateway takes data from various sensors and devices, processes it and sends that to the cloud. It is helpful in industrial automation and in the development of smart grids.
• Micro Data Centres: Putting some data centres near the source is suitable for applications that need speed, including autonomous vehicles and real-time retail analytics.
• Cloud Edge: By having resources nearby, cloud providers can provide edge services, making use of the cloud’s scaling abilities while achieving faster speeds.

India’s market for edge computing is growing healthily and will be valued at more than $6.1 billion in 2025, while the total planned worldwide spending on edge in 2022 is $208 billion. In 2026, the majority of enterprise data will rely on local computing, showing a huge shift that moves data away from centralized data centres. Smart cities are predicted to expand by 19% CAGR and the IoT in smart cities is expected to grow from ₹11,000 crore to more than ₹26,000 crore by 2026.

Career Prospects for Computer Science Professionals

This quick shift from cloud computing to edge computing is leading to many exciting opportunities for people in computer science.

• Building IoT Solutions: Creating and putting into use smart sensors, devices and applications to meet urban challenges.
• Managing Edge Infrastructure: creating and running edge data centres in Tier 2 and Tier 3 cities as they start to experience the next major rise in digital growth.
• Advances in AI and Data Analytics: Applying AI/ML on edge devices for better predictive maintenance, faster analysis and automated devices.
• Cybersecurity: Keeping distributed networks and confidential urban data secure is very important, so there is a need for more security experts.

For CS professionals, these fields open opportunities such as minimizing power usage of AI software, organizing large computer networks and providing large-scale data security.

Is Edge Computing a suitable career path for Computer Science students in India?

Absolutely! Many Indian computer scientists are now seeing edge computing as an exciting career choice. Because of the country’s plan for smart cities and the rising numbers of IoT devices, people with knowledge of edge technologies are becoming much more sought after. There is predicted to be a 75% increase in demand for AI, cybersecurity and cloud computing roles by 2025, while jobs involving IT and enabled services (ITeS) are set to go up by 20%. Packages offered to entry-level hires in tech are increasing, and people who start out in cloud and edge fields benefit most in places like Bangalore and Hyderabad. This creates job stability and gives CS experts the chance to work on innovative solutions that directly change urban life such as better road traffic and pollution control.

As the Indian government launches its Smart Cities Mission and increases investments in digital technology, edge computing and IoT will become essential for Indian cities. Because of the wide use of 5G and increasing numbers of connected devices, qualified experts in telecommunications will be needed even more.

All in all, there’s a reason edge computing is so important: it’s the foundation for future city, industrial and digital growth in India. If you work in CS or wish to begin in the field, this is the right time to improve your knowledge of edge computing, IoT and AI. Technologies playing a role in city building today are shaping different career fields and making an impact on people’s lives. If you wish to build new city infrastructures or create a safer future, the future is ready to be shaped by you through edge computing. 

While OpenAI and other global tech giants are racing to conquer the AI world, India has been quietly charting its own path: one that emphasizes self-reliance, local innovation, and digital sovereignty. The ₹10,300 crore investment for the IndiaAI Mission is not merely about catching up with the West. It is about ensuring that the future of Artificial Intelligence in India is created by Indians, for Indians, and on Indian soil.

What Is IndiaAI Mission?

While players such as OpenAI are planning for data centres and country-specific services, India is indeed going forth with sovereign AI compute infrastructure. More than 18,000 GPUs have been procured by the Government under the IndiaAI Mission, from prominent Indian entities such as Jio Platforms, Yotta, NxtGen, CtrlS, and Tata Communications. And this is merely a beginning; a second tender for almost 15,000 more GPUs is underway that would bring India's total GPU count to around 29,000, making it one of the largest AI compute infrastructures on this Earth.

India's Own AI Model

India does not want to be just a passive user. In a landmark decision, the government has selected Sarvam, the AI startup based in India, to develop the country's first sovereign large language model (LLM) under the IndiaAI Mission. This model will be something created entirely in India and optimized for Indian languages and voice applications and expects to be an operational Myriad-scale population. The goal is clear: promote strategic autonomy, reignite domestic innovation, and ensure India’s data and intelligence remain within its territorial boundaries. 

Having outperformed some of the top models worldwide on Indian language benchmarks, technology from Sarvam shows that Indian talent can indeed compete in cost and quality. The government supports this endeavor with funding, high-end computing resources, and a startup- and researcher-friendly ecosystem. 

Open, Local, and Inclusive: The IndiaAI Model

In stark contrast to AI giants abroad who choose closed, proprietary approaches, India's approach challenges the concept of closed-source. IndiaAI calls for open-weight models, local data, and very strong public-private partnerships. The IndiaAI Mission rests on seven pillars: compute capacity, innovation centres, national datasets platform, application development, future skills, startup financing, and emphasis on safe and trusted AI. The goal is to democratize the access to AI so that students, startups, and researchers across the country can innovate without being dependent on foreign technologies. 

Indian startups and researchers have sent in over 500 proposals for developing indigenous AI models, with 120 in just one month. The government is encouraging these teams with grants, compute credits, and equity funding, with priority given to models in healthcare, education, and financial services that serve Indian needs.

IndiaAI isn’t merely one for the high-tech labs in Bengaluru or Hyderabad. The mission is to make AI skills accessible to the rest of the country. The Intel India and IndiaAI Mission partnership is setting up programs like YuvaAI to help school kids learn AI basics. StartupAI helps young entrepreneurs convert their thoughts into AI applications. The concern? To make AI tools and training available to everyone in India, from a primary school kid in a lane of some isolated village to start-up founders in Mumbai.

The big hurdle for India in AI is language diversity. The Digital India Bhashini program, an initiative by Digital India Corporation (DIC), has come down heads and shoulders to counter that by building AI models supporting all 22 scheduled Indian languages. This means finally methods and tools of voice assistance, translation, and digital services can cater to all, not just those who can speak English. There are over 350 AI language models on the Bhashini platform already, paving the way for a truly inclusive Digital India. 

The India Skills Report 2024 indicates that the AI industry will hit US$ 28.8 billion by 2025, at a very brisk pace. India is setting up one of the largest AI compute infrastructures worldwide – thereby almost two-thirds of what ChatGPT uses globally. With over 70 research institutes collaborating and hundreds of startups joining in, the ecosystem is abuzz!

Digital Public Infrastructure Combines with AI

The ways in which India has developed its digital public infrastructure including, Aadhaar, UPI, DigiLocker, etc., have already been recognized worldwide as major successes. The goal of the IndiaAI Mission is to do for AI what Open Source has done for software, making AI solutions part of public platforms and ensuring they are faster, smarter and easily available to all Indians. It’s about more than technology; we want every part of India to be a part of a digital society that is both safe and prepared for the future.

The Challenge is About Independent Digital Growth, Not Just New Technology

Building such an AI system also comes with many difficulties. There are yet to be enough AI experts, reliable data and good cloud facilities in India. Amidst increasing AI competition around the world, India is being recognized for its emphasis on freedom, transparency and helping its own people. The mission is more than just working on chips and data, its goal is to see that India’s digital development comes from Indian people and their values.

In the long run, the important question is not whether India can develop AI models that are as good as those elsewhere. The question is whether India’s strategy will bring about an inclusive, safe and self-sufficient digital environment. If the early signs are anything to go by, then the answer could be a resounding yes! Let’s be the first supporters and audience for IndiaAI. 

Isn’t it interesting  to know that some numbers and equations can make sites, Models, and a lot more? As a data scientist, one is more than just an employee but a magician doing magic with a keyboard! For every aspiring soul, there is this bug that makes them google things and youtube the explanation. This is indeed a good habit that shall later pay off. 

In 2025, YouTube still ranks as the ultimate source of free high-quality learning to cater well to Indian students who want pragmatic, immediately relevant, and easy-to-understand content. Here are the top 5 YouTube channels that should come in your subscription list without any delay as per latest trends and expectations of Indian data science aspirants.

1. Krish Naik

Krish Naik is an eminent Indian data science teacher who simplifies complex areas into very easy-to-understand lessons. He teaches machine learning, deep learning, and real-world projects, along with interview preparation; his tutorials are great for anyone preparing for competitive exams like GCSET or Edinbox Entrance Exam for Computer Science pertaining to Python, statistics, and project-based learning.

Perfect for:

Beginners and Intermediates

Hands-on, Project-based Tutorials

Industry Knowledge plus Career Guidance

2. codebasics

Run by Dhaval Patel, codebasics is a darling for Indian students owing to its helpful, stripped-down approach to concepts. This channel provides a stepwise approach in Python, SQL, Data Analytics, and Machine Learning explained in simple language. Special emphasis is also given to real-life projects, which would be very fruitful for entrance exams and job interviews.

Best for:

• Data analysis and visualization
• Practical projects for your portfolio
• Preparation for GCSET and Edinbox Entrance Exam for Computer Science

3. StatQuest with Josh Starmer

If statistics has always seemed intimidating, StatQuest is your go! Josh Starmer illustrates pertinent statistical concepts and machine-learning algorithms with fun, entertaining visuals and simple language. The playlists are excellent for establishing a strong theoretical foundation, which is essential for any computer science entrance exam.

Best for:

For understanding the basics of statistics and ML

Visual learners

Some last-minute exam doubt clearing

4. Alex The Analyst

Alex The Analyst builds on data analytics for data science, including SQL, Power BI, and career advice. These tutorials are very beginner-friendly for Indian students wishing to start from scratch and gain an understanding of how data science is used in real companies.

Best for:

Data analytics and business intelligence

Providing step-by-step career roadmaps

Building skills for the job market

5. Ken Jee  

Ken Jee is all about practical advice for budding data scientists. Project walkthroughs, Kaggle competition tactics, and interviews with cancer experts are some of what he offers his audience. If you want to get a feel of the Daily Life of a Data Scientist and prepping yourself for global opportunities, subscribing to this channel is a big must!

Best for:

Career advice and interview tips

Real-life data science projects

Keeping abreast with current industry trends

Why These Channels Matter for Indian Aspirants

These channels are not just about tutorials-they offer the whole ecosystem for learning. Whether one has their eyes set on the GCSET, the Global Computer Science Entrance Exam powered by Edinbox, or some government exam like JEE mains, these can act as resources. Here’s how they aid:

• Works on fundamentals and advanced topics
• Builds a strong project portfolio 
• Helps prepare for interviews as well as entrance exams 
• Gives you the latest updates in data science

Pro tip: Subscribe to them, turn on notifications, and actively practice whatever you learn. Consistency is what will take you to mastery in data science!

Note that this is not a sponsored article; all the recommendations are given on the basis of our research. With the perfect set of YouTube channels, becoming a data scientist in India can actually be cheap and effective. So start subscribing to these 5 top channels today, and you'll be halfway there to acing the GCSET, Edinbox Entrance Exam for Computer Science, and securing the dream job of a data scientist. Happy learning!