Prepare for GATE: The Right Time is NOW!
GeeksforGeeks brings you everything you need to crack GATE 2026 – 900+ live hours, 300+ recorded sessions, and expert mentorship to keep you on track.
What’s inside?
✔ Live & recorded classes with India’s top educators
✔ 200+ mock tests to track your progress
✔ Study materials - PYQs, workbooks, formula book & more
✔ 1:1 mentorship & AI doubt resolution for instant support
✔ Interview prep for IITs & PSUs to help you land opportunities
Learn from Experts Like:
Satish Kumar Yadav – Trained 20K+ students
Dr. Khaleel – Ph.D. in CS, 29+ years of experience
Chandan Jha – Ex-ISRO, AIR 23 in GATE
Vijay Kumar Agarwal – M.Tech (NIT), 13+ years of experience
Sakshi Singhal – IIT Roorkee, AIR 56 CSIR-NET
Shailendra Singh – GATE 99.24 percentile
Devasane Mallesham – IIT Bombay, 13+ years of experience
Use code UPSKILL30 to get an extra 30% OFF (Limited time only)
📌 Enroll for a free counseling session now: https://gfgcdn.com/tu/UI2/
GeeksforGeeks brings you everything you need to crack GATE 2026 – 900+ live hours, 300+ recorded sessions, and expert mentorship to keep you on track.
What’s inside?
✔ Live & recorded classes with India’s top educators
✔ 200+ mock tests to track your progress
✔ Study materials - PYQs, workbooks, formula book & more
✔ 1:1 mentorship & AI doubt resolution for instant support
✔ Interview prep for IITs & PSUs to help you land opportunities
Learn from Experts Like:
Satish Kumar Yadav – Trained 20K+ students
Dr. Khaleel – Ph.D. in CS, 29+ years of experience
Chandan Jha – Ex-ISRO, AIR 23 in GATE
Vijay Kumar Agarwal – M.Tech (NIT), 13+ years of experience
Sakshi Singhal – IIT Roorkee, AIR 56 CSIR-NET
Shailendra Singh – GATE 99.24 percentile
Devasane Mallesham – IIT Bombay, 13+ years of experience
Use code UPSKILL30 to get an extra 30% OFF (Limited time only)
📌 Enroll for a free counseling session now: https://gfgcdn.com/tu/UI2/
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Learn Data Science in 2025
𝟭. 𝗔𝗽𝗽𝗹𝘆 𝗣𝗮𝗿𝗲𝘁𝗼'𝘀 𝗟𝗮𝘄 𝘁𝗼 𝗟𝗲𝗮𝗿𝗻 𝗝𝘂𝘀𝘁 𝗘𝗻𝗼𝘂𝗴𝗵 📚
Pareto's Law states that "that 80% of consequences come from 20% of the causes".
This law should serve as a guiding framework for the volume of content you need to know to be proficient in data science.
Often rookies make the mistake of overspending their time learning algorithms that are rarely applied in production. Learning about advanced algorithms such as XLNet, Bayesian SVD++, and BiLSTMs, are cool to learn.
But, in reality, you will rarely apply such algorithms in production (unless your job demands research and application of state-of-the-art algos).
For most ML applications in production - especially in the MVP phase, simple algos like logistic regression, K-Means, random forest, and XGBoost provide the biggest bang for the buck because of their simplicity in training, interpretation and productionization.
So, invest more time learning topics that provide immediate value now, not a year later.
𝟮. 𝗙𝗶𝗻𝗱 𝗮 𝗠𝗲𝗻𝘁𝗼𝗿 ⚡
There’s a Japanese proverb that says “Better than a thousand days of diligent study is one day with a great teacher.” This proverb directly applies to learning data science quickly.
Mentors can teach you about how to build a model in production and how to manage stakeholders - stuff that you don’t often read about in courses and books.
So, find a mentor who can teach you practical knowledge in data science.
𝟯. 𝗗𝗲𝗹𝗶𝗯𝗲𝗿𝗮𝘁𝗲 𝗣𝗿𝗮𝗰𝘁𝗶𝗰𝗲 ✍️
If you are serious about growing your excelling in data science, you have to put in the time to nurture your knowledge. This means that you need to spend less time watching mindless videos on TikTok and spend more time reading books and watching video lectures.
Join for more: https://whatsapp.com/channel/0029Va8v3eo1NCrQfGMseL2D
ENJOY LEARNING 👍👍
𝟭. 𝗔𝗽𝗽𝗹𝘆 𝗣𝗮𝗿𝗲𝘁𝗼'𝘀 𝗟𝗮𝘄 𝘁𝗼 𝗟𝗲𝗮𝗿𝗻 𝗝𝘂𝘀𝘁 𝗘𝗻𝗼𝘂𝗴𝗵 📚
Pareto's Law states that "that 80% of consequences come from 20% of the causes".
This law should serve as a guiding framework for the volume of content you need to know to be proficient in data science.
Often rookies make the mistake of overspending their time learning algorithms that are rarely applied in production. Learning about advanced algorithms such as XLNet, Bayesian SVD++, and BiLSTMs, are cool to learn.
But, in reality, you will rarely apply such algorithms in production (unless your job demands research and application of state-of-the-art algos).
For most ML applications in production - especially in the MVP phase, simple algos like logistic regression, K-Means, random forest, and XGBoost provide the biggest bang for the buck because of their simplicity in training, interpretation and productionization.
So, invest more time learning topics that provide immediate value now, not a year later.
𝟮. 𝗙𝗶𝗻𝗱 𝗮 𝗠𝗲𝗻𝘁𝗼𝗿 ⚡
There’s a Japanese proverb that says “Better than a thousand days of diligent study is one day with a great teacher.” This proverb directly applies to learning data science quickly.
Mentors can teach you about how to build a model in production and how to manage stakeholders - stuff that you don’t often read about in courses and books.
So, find a mentor who can teach you practical knowledge in data science.
𝟯. 𝗗𝗲𝗹𝗶𝗯𝗲𝗿𝗮𝘁𝗲 𝗣𝗿𝗮𝗰𝘁𝗶𝗰𝗲 ✍️
If you are serious about growing your excelling in data science, you have to put in the time to nurture your knowledge. This means that you need to spend less time watching mindless videos on TikTok and spend more time reading books and watching video lectures.
Join for more: https://whatsapp.com/channel/0029Va8v3eo1NCrQfGMseL2D
ENJOY LEARNING 👍👍
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The New Data Scientist of 2025:
- Business background
- Self-educated
- Knows enough SQL
- Knows enough Python
- Knows enough machine learning
- Uses Microsoft Excel
- Uses AI to be productive
- Title isn't "Data Scientist"
- Business background
- Self-educated
- Knows enough SQL
- Knows enough Python
- Knows enough machine learning
- Uses Microsoft Excel
- Uses AI to be productive
- Title isn't "Data Scientist"
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