10 Machine Learning Concepts You Must Know
✅ Supervised vs Unsupervised Learning – Understand the foundation of ML tasks
✅ Bias-Variance Tradeoff – Balance underfitting and overfitting
✅ Feature Engineering – The secret sauce to boost model performance
✅ Train-Test Split & Cross-Validation – Evaluate models the right way
✅ Confusion Matrix – Measure model accuracy, precision, recall, and F1
✅ Gradient Descent – The algorithm behind learning in most models
✅ Regularization (L1/L2) – Prevent overfitting by penalizing complexity
✅ Decision Trees & Random Forests – Interpretable and powerful models
✅ Support Vector Machines – Great for classification with clear boundaries
✅ Neural Networks – The foundation of deep learning
React ❤️ for detailed explanation
✅ Supervised vs Unsupervised Learning – Understand the foundation of ML tasks
✅ Bias-Variance Tradeoff – Balance underfitting and overfitting
✅ Feature Engineering – The secret sauce to boost model performance
✅ Train-Test Split & Cross-Validation – Evaluate models the right way
✅ Confusion Matrix – Measure model accuracy, precision, recall, and F1
✅ Gradient Descent – The algorithm behind learning in most models
✅ Regularization (L1/L2) – Prevent overfitting by penalizing complexity
✅ Decision Trees & Random Forests – Interpretable and powerful models
✅ Support Vector Machines – Great for classification with clear boundaries
✅ Neural Networks – The foundation of deep learning
React ❤️ for detailed explanation
❤7
Google hiring Data Scientist, Product, Google Home
Apply link: https://careers.google.com/jobs/results/122949546446594758-data-scientist/?src=Online/LinkedIn/linkedin_us&utm_source=linkedin&utm_medium=jobposting&utm_campaign=contract
👉WhatsApp Channel: https://whatsapp.com/channel/0029Vaxjq5a4dTnKNrdeiZ0J
👉Telegram Link: https://t.me/addlist/4q2PYC0pH_VjZDk5
All the best 👍👍
Apply link: https://careers.google.com/jobs/results/122949546446594758-data-scientist/?src=Online/LinkedIn/linkedin_us&utm_source=linkedin&utm_medium=jobposting&utm_campaign=contract
👉WhatsApp Channel: https://whatsapp.com/channel/0029Vaxjq5a4dTnKNrdeiZ0J
👉Telegram Link: https://t.me/addlist/4q2PYC0pH_VjZDk5
All the best 👍👍
👍2
Forwarded from AI Prompts | ChatGPT | Google Gemini | Claude
𝗠𝗶𝗰𝗿𝗼𝘀𝗼𝗳𝘁 𝗙𝗥𝗘𝗘 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗖𝗼𝘂𝗿𝘀𝗲𝘀😍
Whether you’re a student, fresher, or professional looking to upskill — Microsoft has dropped a series of completely free courses to get you started.
Learn SQL ,Power BI & More In 2025
𝗟𝗶𝗻𝗸:-👇
https://pdlink.in/42FxnyM
Enroll For FREE & Get Certified 🎓
Whether you’re a student, fresher, or professional looking to upskill — Microsoft has dropped a series of completely free courses to get you started.
Learn SQL ,Power BI & More In 2025
𝗟𝗶𝗻𝗸:-👇
https://pdlink.in/42FxnyM
Enroll For FREE & Get Certified 🎓
👍1
Data Visualization Tools & Best Practices
1. Power BI:
Purpose: Powerful business analytics tool to visualize and share insights from your data.
Best Practices:
Use simple visuals (avoid overloading with data).
Choose the right chart type (e.g., bar chart for comparisons, line chart for trends).
Use slicers and filters to allow users to explore data interactively.
Keep your color schemes consistent and avoid too many colors.
Use Tooltips for additional context without cluttering the visual.
2. Tableau:
Purpose: Data visualization tool used for creating interactive and shareable dashboards.
Best Practices:
Minimize clutter by reducing non-essential elements (e.g., gridlines, unnecessary labels).
Ensure readability with a clean and intuitive layout.
Use dual-axis charts when comparing two measures in a single visual.
Keep titles and labels concise; avoid redundant information.
Prioritize data integrity (avoid misleading visualizations).
3. Matplotlib & Seaborn (Python):
Purpose: Python libraries for static, animated, and interactive visualizations.
Best Practices:
Use subplots to visualize multiple charts together for comparison.
Keep axes readable with appropriate titles and labels.
Choose appropriate color palettes (e.g., Seaborn has good built-in color schemes).
Annotations can help clarify key points on the chart.
Use log scaling for large numerical ranges to make the data more interpretable.
4. Excel:
Purpose: Widely used tool for simple data analysis and visualization.
Best Practices:
Use pivot charts to summarize data interactively.
Stick to basic chart types (e.g., bar, line, pie) for easy-to-understand visuals.
Use conditional formatting to highlight key trends or outliers.
Label charts clearly (titles, axis names, and legends).
Limit the number of chart elements (don’t overcrowd your chart).
5. Google Data Studio:
Purpose: Free tool for creating dashboards and reports, often integrated with Google products.
Best Practices:
Link to live data sources for automatic updates (e.g., Google Sheets, Google Analytics).
Use dynamic filters to give users control over what data is shown.
Utilize templates for consistent reports and visuals.
Keep reports simple and focused on key metrics.
Design with mobile responsiveness in mind for accessibility.
6. Best Practices for Data Visualization:
Clarity over complexity: Simplify your visuals, removing unnecessary elements.
Choose the right chart: Select charts that best represent the data (e.g., bar for comparisons, line for trends).
Tell a story: Your visual should communicate a clear message or insight.
Consistency in design: Maintain a consistent style for fonts, colors, and layout across all visuals.
Be mindful of colorblindness: Use color schemes that are accessible to all viewers.
Provide context: Include clear titles, labels, and legends for better understanding.
I have curated best 80+ top-notch Data Analytics Resources 👇👇
https://whatsapp.com/channel/0029VaGgzAk72WTmQFERKh02
Like this post for more content like this 👍♥️
Share with credits: https://t.me/sqlspecialist
Hope it helps :)
1. Power BI:
Purpose: Powerful business analytics tool to visualize and share insights from your data.
Best Practices:
Use simple visuals (avoid overloading with data).
Choose the right chart type (e.g., bar chart for comparisons, line chart for trends).
Use slicers and filters to allow users to explore data interactively.
Keep your color schemes consistent and avoid too many colors.
Use Tooltips for additional context without cluttering the visual.
2. Tableau:
Purpose: Data visualization tool used for creating interactive and shareable dashboards.
Best Practices:
Minimize clutter by reducing non-essential elements (e.g., gridlines, unnecessary labels).
Ensure readability with a clean and intuitive layout.
Use dual-axis charts when comparing two measures in a single visual.
Keep titles and labels concise; avoid redundant information.
Prioritize data integrity (avoid misleading visualizations).
3. Matplotlib & Seaborn (Python):
Purpose: Python libraries for static, animated, and interactive visualizations.
Best Practices:
Use subplots to visualize multiple charts together for comparison.
Keep axes readable with appropriate titles and labels.
Choose appropriate color palettes (e.g., Seaborn has good built-in color schemes).
Annotations can help clarify key points on the chart.
Use log scaling for large numerical ranges to make the data more interpretable.
4. Excel:
Purpose: Widely used tool for simple data analysis and visualization.
Best Practices:
Use pivot charts to summarize data interactively.
Stick to basic chart types (e.g., bar, line, pie) for easy-to-understand visuals.
Use conditional formatting to highlight key trends or outliers.
Label charts clearly (titles, axis names, and legends).
Limit the number of chart elements (don’t overcrowd your chart).
5. Google Data Studio:
Purpose: Free tool for creating dashboards and reports, often integrated with Google products.
Best Practices:
Link to live data sources for automatic updates (e.g., Google Sheets, Google Analytics).
Use dynamic filters to give users control over what data is shown.
Utilize templates for consistent reports and visuals.
Keep reports simple and focused on key metrics.
Design with mobile responsiveness in mind for accessibility.
6. Best Practices for Data Visualization:
Clarity over complexity: Simplify your visuals, removing unnecessary elements.
Choose the right chart: Select charts that best represent the data (e.g., bar for comparisons, line for trends).
Tell a story: Your visual should communicate a clear message or insight.
Consistency in design: Maintain a consistent style for fonts, colors, and layout across all visuals.
Be mindful of colorblindness: Use color schemes that are accessible to all viewers.
Provide context: Include clear titles, labels, and legends for better understanding.
I have curated best 80+ top-notch Data Analytics Resources 👇👇
https://whatsapp.com/channel/0029VaGgzAk72WTmQFERKh02
Like this post for more content like this 👍♥️
Share with credits: https://t.me/sqlspecialist
Hope it helps :)
👍2❤1
NetApp hiring Machine Learning Engineer
https://careers.netapp.com/job/-/-/27600/79640994048?jobPipeline=limtedlistings
https://careers.netapp.com/job/-/-/27600/79640994048?jobPipeline=limtedlistings
Forwarded from Generative AI
𝟲 𝗙𝗿𝗲𝗲 𝗔𝗜 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗖𝗼𝘂𝗿𝘀𝗲𝘀 𝗧𝗼 𝗨𝗽𝘀𝗸𝗶𝗹𝗹 𝗜𝗻 𝟮𝟬𝟮𝟱😍
Whether you’re a student, aspiring data analyst, software enthusiast, or just curious about AI, now’s the perfect time to dive in.
These 6 beginner-friendly and completely free AI courses from top institutions like Google, IBM, Harvard, and more
𝗟𝗶𝗻𝗸:-👇
https://pdlink.in/4d0SrTG
Enroll for FREE & Get Certified 🎓
Whether you’re a student, aspiring data analyst, software enthusiast, or just curious about AI, now’s the perfect time to dive in.
These 6 beginner-friendly and completely free AI courses from top institutions like Google, IBM, Harvard, and more
𝗟𝗶𝗻𝗸:-👇
https://pdlink.in/4d0SrTG
Enroll for FREE & Get Certified 🎓
👍2
3 ways to keep your data science skills up-to-date
1. Get Hands-On: Dive into real-world projects to grasp the challenges of building solutions. This is what will open up a world of opportunity for you to innovate.
2. Embrace the Big Picture: While deep diving into specific topics is essential, don't forget to understand the breadth of data science problem you are solving. Seeing the bigger picture helps you connect the dots and build solutions that not only are cutting edge but have a great ROI.
3. Network and Learn: Connect with fellow data scientists to exchange ideas, insights, and best practices. Learning from others in the field is invaluable for staying updated and continuously improving your skills.
1. Get Hands-On: Dive into real-world projects to grasp the challenges of building solutions. This is what will open up a world of opportunity for you to innovate.
2. Embrace the Big Picture: While deep diving into specific topics is essential, don't forget to understand the breadth of data science problem you are solving. Seeing the bigger picture helps you connect the dots and build solutions that not only are cutting edge but have a great ROI.
3. Network and Learn: Connect with fellow data scientists to exchange ideas, insights, and best practices. Learning from others in the field is invaluable for staying updated and continuously improving your skills.
👍1
Forwarded from Python for Data Analysts
𝗙𝗥𝗘𝗘 𝗦𝗼𝗳𝘁𝘀𝗸𝗶𝗹𝗹𝘀 𝗖𝗼𝘂𝗿𝘀𝗲 𝗪𝗶𝘁𝗵 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗲 😍
This FREE soft skills course is your gateway to mastering communication, teamwork, leadership, and more.
Plus, you’ll earn a certificate to add a professional edge to your resume📄📌
𝐋𝐢𝐧𝐤👇:-
https://pdlink.in/4j2CT4O
Invest in yourself, and stand out in your career journey!✅️
This FREE soft skills course is your gateway to mastering communication, teamwork, leadership, and more.
Plus, you’ll earn a certificate to add a professional edge to your resume📄📌
𝐋𝐢𝐧𝐤👇:-
https://pdlink.in/4j2CT4O
Invest in yourself, and stand out in your career journey!✅️
👍1
Complete Roadmap to learn Machine Learning and Artificial Intelligence
👇👇
Week 1-2: Introduction to Machine Learning
- Learn the basics of Python programming language (if you are not already familiar with it)
- Understand the fundamentals of Machine Learning concepts such as supervised learning, unsupervised learning, and reinforcement learning
- Study linear algebra and calculus basics
- Complete online courses like Andrew Ng's Machine Learning course on Coursera
Week 3-4: Deep Learning Fundamentals
- Dive into neural networks and deep learning
- Learn about different types of neural networks like Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs)
- Implement deep learning models using frameworks like TensorFlow or PyTorch
- Complete online courses like Deep Learning Specialization on Coursera
Week 5-6: Natural Language Processing (NLP) and Computer Vision
- Explore NLP techniques such as tokenization, word embeddings, and sentiment analysis
- Dive into computer vision concepts like image classification, object detection, and image segmentation
- Work on projects involving NLP and Computer Vision applications
Week 7-8: Reinforcement Learning and AI Applications
- Learn about Reinforcement Learning algorithms like Q-learning and Deep Q Networks
- Explore AI applications in fields like healthcare, finance, and autonomous vehicles
- Work on a final project that combines different aspects of Machine Learning and AI
Additional Tips:
- Practice coding regularly to strengthen your programming skills
- Join online communities like Kaggle or GitHub to collaborate with other learners
- Read research papers and articles to stay updated on the latest advancements in the field
Pro Tip: Roadmap won't help unless you start working on it consistently. Start working on projects as early as possible.
2 months are good as a starting point to get grasp the basics of ML & AI but mastering it is very difficult as AI keeps evolving every day.
Best Resources to learn ML & AI 👇
Learn Python for Free
Prompt Engineering Course
Prompt Engineering Guide
Data Science Course
Google Cloud Generative AI Path
Unlock the power of Generative AI Models
Machine Learning with Python Free Course
Machine Learning Free Book
Deep Learning Nanodegree Program with Real-world Projects
AI, Machine Learning and Deep Learning
Join @free4unow_backup for more free courses
ENJOY LEARNING👍👍
👇👇
Week 1-2: Introduction to Machine Learning
- Learn the basics of Python programming language (if you are not already familiar with it)
- Understand the fundamentals of Machine Learning concepts such as supervised learning, unsupervised learning, and reinforcement learning
- Study linear algebra and calculus basics
- Complete online courses like Andrew Ng's Machine Learning course on Coursera
Week 3-4: Deep Learning Fundamentals
- Dive into neural networks and deep learning
- Learn about different types of neural networks like Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs)
- Implement deep learning models using frameworks like TensorFlow or PyTorch
- Complete online courses like Deep Learning Specialization on Coursera
Week 5-6: Natural Language Processing (NLP) and Computer Vision
- Explore NLP techniques such as tokenization, word embeddings, and sentiment analysis
- Dive into computer vision concepts like image classification, object detection, and image segmentation
- Work on projects involving NLP and Computer Vision applications
Week 7-8: Reinforcement Learning and AI Applications
- Learn about Reinforcement Learning algorithms like Q-learning and Deep Q Networks
- Explore AI applications in fields like healthcare, finance, and autonomous vehicles
- Work on a final project that combines different aspects of Machine Learning and AI
Additional Tips:
- Practice coding regularly to strengthen your programming skills
- Join online communities like Kaggle or GitHub to collaborate with other learners
- Read research papers and articles to stay updated on the latest advancements in the field
Pro Tip: Roadmap won't help unless you start working on it consistently. Start working on projects as early as possible.
2 months are good as a starting point to get grasp the basics of ML & AI but mastering it is very difficult as AI keeps evolving every day.
Best Resources to learn ML & AI 👇
Learn Python for Free
Prompt Engineering Course
Prompt Engineering Guide
Data Science Course
Google Cloud Generative AI Path
Unlock the power of Generative AI Models
Machine Learning with Python Free Course
Machine Learning Free Book
Deep Learning Nanodegree Program with Real-world Projects
AI, Machine Learning and Deep Learning
Join @free4unow_backup for more free courses
ENJOY LEARNING👍👍
👍2
Forwarded from Python for Data Analysts
𝗗𝗲𝗹𝗼𝗶𝘁𝘁𝗲 𝗩𝗶𝗿𝘁𝘂𝗮𝗹 𝗙𝗥𝗘𝗘 𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻 😍
If you’re eager to build real skills in data analytics before landing your first role, Deloitte is giving you a golden opportunity—completely free!
💡 No prior experience required
📚 Ideal for students, freshers, and aspiring data analysts
⏰ Self-paced — complete at your convenience
🔗 𝗔𝗽𝗽𝗹𝘆 𝗛𝗲𝗿𝗲 (𝗙𝗿𝗲𝗲)👇:-
https://pdlink.in/4iKcgA4
Enroll for FREE & Get Certified 🎓
If you’re eager to build real skills in data analytics before landing your first role, Deloitte is giving you a golden opportunity—completely free!
💡 No prior experience required
📚 Ideal for students, freshers, and aspiring data analysts
⏰ Self-paced — complete at your convenience
🔗 𝗔𝗽𝗽𝗹𝘆 𝗛𝗲𝗿𝗲 (𝗙𝗿𝗲𝗲)👇:-
https://pdlink.in/4iKcgA4
Enroll for FREE & Get Certified 🎓
❤1
DATA SCIENCE JOBS ARE EXPLODING! 🤯💸
• Data Scientist: $118,399
• Data Analyst: $85,000
• Machine Learning Engineer: $123,117
• Business Intelligence Analyst: $97,000
• AI Researcher: $99,518
Top Ways Land a High-Paying Data Science Job:
1. Master Python & SQL
• Learn Pandas, NumPy, and Matplotlib.
• SQL is essential for handling databases.
2. Take Online Data Science Courses
• Platforms like Coursera, Udacity, and edX offer top courses.
• Certifications from Google or IBM add value.
3. Build a Strong Portfolio
• Work on real-world projects (Kaggle competitions, dashboards).
• Share projects on GitHub and LinkedIn.
4. Gain Experience with Internships & Freelance Work
• Apply for analyst roles or freelance on Upwork.
• Contribute to open-source projects.
5. Network & Stay Ahead
• Join data science meetups & LinkedIn groups.
• Follow industry leaders like Andrew Ng & Hadley Wickham.
Extra Tip: By Specializing in deep learning or NLP, you will stand out!
Data Science Jobs: 👇
https://whatsapp.com/channel/0029VaxTMmQADTOA746w7U2P
• Data Scientist: $118,399
• Data Analyst: $85,000
• Machine Learning Engineer: $123,117
• Business Intelligence Analyst: $97,000
• AI Researcher: $99,518
Top Ways Land a High-Paying Data Science Job:
1. Master Python & SQL
• Learn Pandas, NumPy, and Matplotlib.
• SQL is essential for handling databases.
2. Take Online Data Science Courses
• Platforms like Coursera, Udacity, and edX offer top courses.
• Certifications from Google or IBM add value.
3. Build a Strong Portfolio
• Work on real-world projects (Kaggle competitions, dashboards).
• Share projects on GitHub and LinkedIn.
4. Gain Experience with Internships & Freelance Work
• Apply for analyst roles or freelance on Upwork.
• Contribute to open-source projects.
5. Network & Stay Ahead
• Join data science meetups & LinkedIn groups.
• Follow industry leaders like Andrew Ng & Hadley Wickham.
Extra Tip: By Specializing in deep learning or NLP, you will stand out!
Data Science Jobs: 👇
https://whatsapp.com/channel/0029VaxTMmQADTOA746w7U2P
👍3
Google hiring Staff Data Scientist Product Manager, Data Science, Analytics
Apply link: https://careers.google.com/jobs/results/101440522295354054-staff-data-scientist-product-manager/
👉WhatsApp Channel: https://whatsapp.com/channel/0029VaxngnVInlqV6xJhDs3m
👉Telegram Link: https://t.me/addlist/4q2PYC0pH_VjZDk5
All the best 👍👍
Apply link: https://careers.google.com/jobs/results/101440522295354054-staff-data-scientist-product-manager/
👉WhatsApp Channel: https://whatsapp.com/channel/0029VaxngnVInlqV6xJhDs3m
👉Telegram Link: https://t.me/addlist/4q2PYC0pH_VjZDk5
All the best 👍👍
👍1
We’re Hiring: Machine Learning Developer 🚀
At YASH Technologies, we’re building cutting-edge AI solutions across industries like commerce, finance, and agriculture. We’re looking for a Machine Learning Developer with expertise in LLMs, NLP, and Deep Learning to join our team!
What You’ll Do:
✅ Develop & optimize ML, NLP, and Generative AI models
✅ Fine-tune LLMs & work on prompt engineering
✅ Scale AI models from prototype to production
✅ Collaborate with global teams
What We’re Looking For:
🔹 2-6 years in ML, Deep Learning, NLP, and GenAI
🔹 Experience with TensorFlow, PyTorch, Hugging Face
🔹 Strong Python, R, or Scala skills
🔹 Knowledge of cloud-based ML platforms (AWS, Azure, GCP)
Interested? Let’s talk! Drop a comment or DM me at Poorva.bhatt@yash.com 📩
At YASH Technologies, we’re building cutting-edge AI solutions across industries like commerce, finance, and agriculture. We’re looking for a Machine Learning Developer with expertise in LLMs, NLP, and Deep Learning to join our team!
What You’ll Do:
✅ Develop & optimize ML, NLP, and Generative AI models
✅ Fine-tune LLMs & work on prompt engineering
✅ Scale AI models from prototype to production
✅ Collaborate with global teams
What We’re Looking For:
🔹 2-6 years in ML, Deep Learning, NLP, and GenAI
🔹 Experience with TensorFlow, PyTorch, Hugging Face
🔹 Strong Python, R, or Scala skills
🔹 Knowledge of cloud-based ML platforms (AWS, Azure, GCP)
Interested? Let’s talk! Drop a comment or DM me at Poorva.bhatt@yash.com 📩
👍2
Forwarded from Python for Data Analysts
𝟲 𝗙𝗿𝗲𝗲 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗖𝗼𝘂𝗿𝘀𝗲𝘀 𝘁𝗼 𝗠𝗮𝗸𝗲 𝗬𝗼𝘂𝗿 𝗥𝗲𝘀𝘂𝗺𝗲 𝗦𝘁𝗮𝗻𝗱 𝗢𝘂𝘁 𝗶𝗻 𝟮𝟬𝟮𝟱😍
As competition heats up across every industry, standing out to recruiters is more important than ever📄📌
The best part? You don’t need to spend a rupee to do it!💰
𝐋𝐢𝐧𝐤👇:-
https://pdlink.in/4m0nNOD
👉 Start learning. Start standing out✅️
As competition heats up across every industry, standing out to recruiters is more important than ever📄📌
The best part? You don’t need to spend a rupee to do it!💰
𝐋𝐢𝐧𝐤👇:-
https://pdlink.in/4m0nNOD
👉 Start learning. Start standing out✅️
👍3
Looking for Data Scientist professionals
Skills required - Proficient in R & Python with statistical modelling and ML techniques along with deep learning and NLP concepts.
Experience - 5-7 Years
Location - Bengaluru
Interested candidates can mail their cv at nidhig@symphonihr.com
Skills required - Proficient in R & Python with statistical modelling and ML techniques along with deep learning and NLP concepts.
Experience - 5-7 Years
Location - Bengaluru
Interested candidates can mail their cv at nidhig@symphonihr.com
👍2
𝗠𝗶𝗰𝗿𝗼𝘀𝗼𝗳𝘁’𝘀 𝗙𝗥𝗘𝗘 𝗣𝗼𝘄𝗲𝗿𝗕𝗜 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗖𝗼𝘂𝗿𝘀𝗲😍
🚀 Want to Break into Data Analytics? Start with This Free Power BI Course by Microsoft🎯
If you’re trying to enter the field of data analytics but don’t know where to start, Microsoft has your back!💻📍
𝐋𝐢𝐧𝐤👇:-
https://pdlink.in/4jJvuaq
Best part? It’s completely free and created by one of the most trusted names in tech✅️
🚀 Want to Break into Data Analytics? Start with This Free Power BI Course by Microsoft🎯
If you’re trying to enter the field of data analytics but don’t know where to start, Microsoft has your back!💻📍
𝐋𝐢𝐧𝐤👇:-
https://pdlink.in/4jJvuaq
Best part? It’s completely free and created by one of the most trusted names in tech✅️
👍2
Sony Hiring Data Science Intern
Internship Duration: 6 months
Graduation Year: 2025 / 2026
Location: Bengaluru / Mumbai / Remote
Apply Link:
https://www.linkedin.com/jobs/view/4216336436/
Internship Duration: 6 months
Graduation Year: 2025 / 2026
Location: Bengaluru / Mumbai / Remote
Apply Link:
https://www.linkedin.com/jobs/view/4216336436/
Linkedin
2,000+ Intelligence Specialist jobs in United States (64 new)
Today’s top 2,000+ Intelligence Specialist jobs in United States. Leverage your professional network, and get hired. New Intelligence Specialist jobs added daily.
Forwarded from Python for Data Analysts
𝟯 𝗙𝗿𝗲𝗲 𝗧𝗖𝗦 𝗖𝗼𝘂𝗿𝘀𝗲𝘀 𝗘𝘃𝗲𝗿𝘆 𝗙𝗿𝗲𝘀𝗵𝗲𝗿 𝗠𝘂𝘀𝘁 𝗧𝗮𝗸𝗲 𝘁𝗼 𝗚𝗲𝘁 𝗝𝗼𝗯-𝗥𝗲𝗮𝗱𝘆😍
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🎯 If You’re a Fresher, These TCS Courses Are a Must-Do📄✔️
Stepping into the job market can be overwhelming—but what if you had certified, expert-backed training that actually prepares you?👨🎓✨️
𝐋𝐢𝐧𝐤👇:-
https://pdlink.in/42Nd9Do
Don’t wait. Get certified, get confident, and get closer to landing your first job✅️
Data Scientist Hiring in Noida, India
https://jobs.ericsson.com/careers/job/563121763669097?domain=ericsson.com&jobPipeline=LinkedIn
https://jobs.ericsson.com/careers/job/563121763669097?domain=ericsson.com&jobPipeline=LinkedIn
Ericsson
Data Scientist | Ericsson
Develop and deploy machine learning models for various applications including chat-bot, XGBoost, random forest, NLP, computer vision, and generative AI. Utilize Python for data manipulation, analysis, and modeling tasks. Proficient in SQL for querying and…
Airbus hiring Data Scientist - Generative AI
Apply link: https://ag.wd3.myworkdayjobs.com/en-US/Airbus/job/Bangalore-Area/Data-Scientist---Generative-AI_JR10315684-1
👉WhatsApp Channel: https://whatsapp.com/channel/0029Vaxjq5a4dTnKNrdeiZ0J
👉Telegram Link: https://t.me/addlist/4q2PYC0pH_VjZDk5
All the best 👍👍
Apply link: https://ag.wd3.myworkdayjobs.com/en-US/Airbus/job/Bangalore-Area/Data-Scientist---Generative-AI_JR10315684-1
👉WhatsApp Channel: https://whatsapp.com/channel/0029Vaxjq5a4dTnKNrdeiZ0J
👉Telegram Link: https://t.me/addlist/4q2PYC0pH_VjZDk5
All the best 👍👍
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