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BI#dataanalytics #datascience #ai #datasciencetraining #dataanalyticstraining
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DECode With Harry Ultimate Data Science Paid Course Free
@devxrev01
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DAHe suggested that it is not necessary for everyone to become proficient programmers because the advancements in artificial intelligence (AI) will automate programming itself.
🎥 via @worldgovsummit
#ai #aitools #Programming #tech #technology #artificialintelligence #Coding #nvidia
@datascienceinfo
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URAb details dekhoge?‼️
📩 Comment “INTERNSHIP” to get the apply link & details in dm
If you’ve been wanting to start your Data Science journey but didn’t know where to begin — this is your sign. 💻
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@urvishares
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BEComment LINKS and I’ll send all 3 to your DMs.
You’re going to spend tonight watching something anyway.
Make it count.
3 videos. Claude course, Dario Amodei on AGI, NotebookLM masterclass.
A few hours and you’ll know more about AI than most people working in tech right now.
#biz #tech #AI #netflix
@beinginvested
28.1K
WO📍Learning to code and becoming a data scientist without a background in computer science or mathematics is absolutely possible, but it will require dedication, time, and a structured approach. ✨👌🏻
🖐🏻Here’s a step-by-step guide to help you get started:
1. Start with the Basics:
- Begin by learning the fundamentals of programming. Choose a beginner-friendly programming language like Python, which is widely used in data science.
- Online platforms like Codecademy, Coursera, and Khan Academy offer interactive courses for beginners.
2. Learn Mathematics and Statistics:
- While you don’t need to be a mathematician, a solid understanding of key concepts like algebra, calculus, and statistics is crucial for data science.
- Platforms like Khan Academy and MIT OpenCourseWare provide free resources for learning math.
3. Online Courses and Tutorials:
- Enroll in online data science courses on platforms like Coursera, edX, Udacity, and DataCamp. Look for beginner-level courses that cover data analysis, visualization, and machine learning.
4. Structured Learning Paths:
- Follow structured learning paths offered by online platforms. These paths guide you through various topics in a logical sequence.
5. Practice with Real Data:
- Work on hands-on projects using real-world data. Websites like Kaggle offer datasets and competitions for practicing data analysis and machine learning.
6. Coding Exercises:
- Practice coding regularly to build your skills. Sites like LeetCode and HackerRank offer coding challenges that can help improve your programming proficiency.
7. Learn Data Manipulation and Analysis Libraries:
- Familiarize yourself with Python libraries like NumPy, pandas, and Matplotlib for data manipulation, analysis, and visualization.
For more look at the comment ⤵️
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#datascience #computerscience #datascientist #dataanalytics #dataanalyticstraining #python #softwaredeveloper #dataanalysis #bigdata #generativeai #codingbootcamp #businesswoman #veribilimi #codemotivation
@woman.engineer
884
DARegister now! Limited spots available. #datanomicstech #datascience #vibecoding #techskills #ai
@datanomicstech
95.7K
NOCOMMENT " DA " FOR REGISTRATION LINK ❤️🔥❤️🔥
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@novitechresearchanddevelopment
159
A_⚡🚀 COMING SOON 🚀⚡
Google's quantum chip solved a problem that would take a classical supercomputer 13,000 years.
The era isn't coming. It's here.
𝗤𝘂𝗮𝗻𝘁𝘂𝗺 𝗖𝗼𝗺𝗽𝘂𝘁𝗶𝗻𝗴: 𝗣𝗮𝗿𝗮𝗱𝗼𝘅 𝘁𝗼 𝗣𝗼𝘄𝗲𝗿𝗵𝗼𝘂𝘀𝗲
Follow the page and hit the bell🔔
#QuantumComputing #STEM #Podcast #ASI #ComingSoon #EruditeExploration
@a_stellar_insight
9.8K
DAFor the first time, I felt confident that I could actually work like a Data Scientist, not just sound like one.
🎓 I studied Data Science in college… but it was all theory.
We had lectures, notes, and assignments — but not once did we actually build something real. 😞
I knew the formulas, but not how to use them. I could explain algorithms, but not apply them.
That’s when I found DataTeach.ai Hyderabad — and everything changed. 💡
Here, learning means doing. 💻
Every concept is backed by hands-on practice, real-time projects, and mentor guidance.
From SQL to Python, Machine Learning to AI, we dive deep into real data — building dashboards, cleaning datasets, analyzing patterns, and solving industry-level problems.
💬 If you’re stuck with only theory, it’s time to upgrade your skills.
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@datateach.ai
49.9K
THAI is not a tool, it's work.
NVIDIA's CEO Jensen Huang argues that past software like Excel were tools you use. AI is different. It's a "worker" that can use tools on your behalf.
This is why AI will finally make the $100 trillion global economy productive, not just the $1 trillion tools industry.
If you want to try some of the powerful AI agent tools available now, comment "tools" to get my full toolkit.
Credit: NVIDIA GTC 2025
@theneoniche
469
BIMost people think the AI boom started with ChatGPT.
But the real foundation of the artificial intelligence revolution was built years earlier.
In the early 2000s, NVIDIA was mainly a gaming GPU company.
Then Jensen Huang made a bold bet by creating CUDA, a platform that allowed developers to use GPUs for parallel computing, machine learning, and deep learning.
At that time:
• AI startups weren’t profitable
• Deep learning was mostly academic
• Investors ignored AI infrastructure
For years, CUDA looked like a niche experiment.
Then machine learning, large language models, and generative AI exploded and suddenly the world needed massive AI computing power.
That power came from NVIDIA GPUs.
By 2023, NVIDIA crossed a $1 trillion valuation, becoming one of the most valuable AI technology companies.
The lesson:
The AI revolution didn’t start with hype it started with AI infrastructure and long-term vision.
Follow for more stories about AI founders, tech companies, and the future of artificial intelligence.
#jensenhuang
#nvidia
#aitechnology
#machinelearning
#techfounders
@binarybopper
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