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Major program launch: Data Analytics Professional Certificate! This large, five-course sequence takes you all the way to being job-ready as a data analyst, and shows how to use Generative AI as a thought partner to enhance your work in this role. Offered by on Coursera, this is taught by...

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Andrew Ng

1,892,214 subscribers

85,182 次观看 • 1 年前 •via X (Twitter)

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11 条评论

Shaheen Niaz 的头像
Shaheen Niaz1 年前

Is it really free or gonna charge after a week ?

SecBriefs | Making Cybersecurity Simple 的头像
SecBriefs | Making Cybersecurity Simple2 年前

Thinking about a career in cybersecurity but worried about your technical background? Don't be! 💡 Many roles value your problem-solving, analytical, & communication skills. 🕵️‍♀️ "Cybersecurity Dictionary for Everyone" is a good start, available on Amazon:

lami 的头像
lami1 年前

🙏🏽

Vasan “VK” Kidambi 的头像
Vasan “VK” Kidambi1 年前

Two great minds means amazing wisdom!! 🙏

Emmanuel Moyrand 的头像
Emmanuel Moyrand1 年前

Exciting news for aspiring data analysts. 🎉

@profitleap 的头像
@profitleap1 年前

This sounds like an incredible opportunity to elevate skills in data analytics andAI. 🎉

Mohammed Lubbad, PhD 的头像
Mohammed Lubbad, PhD1 年前

What exciting developments await graduates of this program? Data-driven skills will shape future roles. 📊 #DataAnalytics

Basit Ali 的头像
Basit Ali1 年前

Wow

Eric CogniNet 的头像
Eric CogniNet1 年前

Establishes robust career trajectory, adding AI acumen. Comprehensive groundwork indeed. Consider follow-up Q&A sessions?

Anda 的头像
Anda1 年前

Oh ho! Data analysis meets AI companionship? That's like giving bamboo to a panda *and* teaching it to brew tea!

jc_stack 的头像
jc_stack1 年前

Great to see data analysis + LLMs being taught together. For AI agent design, mastering how data flows between agents and integrating real-time analytics are crucial skills to develop first

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We just launched a major new Data Engineering Professional Certificate on Coursera! Data underlies all modern AI systems, and engineers who know how to build systems to store and serve it are in high demand. If you're interested in learning this skill, please check out this 4-course sequence, which is designed to make you job-ready to be a Data Engineer. This is a new specialization taught by Joe Reis, the co-author of the best-selling book “Fundamentals of Data Engineering," in collaboration with AWS. (Disclosure, I serve on Amazon's board.) For many AI systems, data engineering is 80% of the work, and modeling is 20%. But people’s attention on these two topics is often flipped. This makes the job of the data engineer particularly important. In this professional certificate, you'll learn foundational data engineering skills while implementing modern data architectures using open-source tools: - Learn the key steps of the data lifecycle, to generate, ingest, store, transform, and serve data. - Learn to align with organizational goals to design the data pipeline right for your business' needs. - Understand how to make necessary trade-offs between speed, scalability, security, and cost. Joe has distilled into this specialization decades of experience helping startups and large companies with data infrastructure. He is also joined by 17 other industry leaders in the data field, who will help you learn in-demand skills for the growing field of data engineering. Please sign up here:
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We just launched a major new Data Engineering Professional Certificate on Coursera! Data underlies all modern AI systems, and engineers who know how to build systems to store and serve it are in high demand. If you're interested in learning this skill, please check out this 4-course sequence, which is designed to make you job-ready to be a Data Engineer. This is a new specialization taught by Joe Reis, the co-author of the best-selling book “Fundamentals of Data Engineering," in collaboration with AWS. (Disclosure, I serve on Amazon's board.) For many AI systems, data engineering is 80% of the work, and modeling is 20%. But people’s attention on these two topics is often flipped. This makes the job of the data engineer particularly important. In this professional certificate, you'll learn foundational data engineering skills while implementing modern data architectures using open-source tools: - Learn the key steps of the data lifecycle, to generate, ingest, store, transform, and serve data. - Learn to align with organizational goals to design the data pipeline right for your business' needs. - Understand how to make necessary trade-offs between speed, scalability, security, and cost. Joe has distilled into this specialization decades of experience helping startups and large companies with data infrastructure. He is also joined by 17 other industry leaders in the data field, who will help you learn in-demand skills for the growing field of data engineering. Please sign up here:

Andrew Ng

120,581 次观看 • 2 年前

“Data in robotics versus data in LLMs has a very different alpha decay problem.” — Tarun Chitra, Managing Partner Robot Ventures Much of what LLMs learn can carry across contexts. Robotics data is far more conditional: what works depends heavily on the robot, the objects, and the environment. As robots move into more complex, real-world settings, previously collected data can become less relevant much faster. For builders and investors, that shifts the question from “How much data do you have” to “How quickly can you close the loop between deployment, new data, and adaptation”. From The Convergence: Bhutan by YZi Labs.
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“Data in robotics versus data in LLMs has a very different alpha decay problem.” — Tarun Chitra, Managing Partner Robot Ventures Much of what LLMs learn can carry across contexts. Robotics data is far more conditional: what works depends heavily on the robot, the objects, and the environment. As robots move into more complex, real-world settings, previously collected data can become less relevant much faster. For builders and investors, that shifts the question from “How much data do you have” to “How quickly can you close the loop between deployment, new data, and adaptation”. From The Convergence: Bhutan by YZi Labs.

YZi Labs

26,519 次观看 • 25 天前

$BTC Statistical Study using Claude - A Beginner's Workflow Here's an example of a z-score study on $BTC - still tinkering so don't take this as overtly useful information but the creation of a dashboard for the visualization of statistical data is phenomenal. my current workflow: > import $BTC time data - .csv file (can get this information from multiple venues - Binance is where I got mine) > creating bins - if you have a larger data set - you can use recent data to prevent overt bias in the long direction or filter consolidation and trending regime data into separate bins for statistical analysis - however, you will have to define thresholds and determine what that entails. > defining metrics in Claude that you want to use for statistical analysis e.g. for z-score what is it based on and what type of calculation? make sure you understand the calculations being performed for any metrics that you are doing a study for and modify them accordingly. > prompting Claude to do a statistical analysis with specific instructions and then tell it to create visualization for this. I've been messing around with this and I'm seriously impressed by the output.
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$BTC Statistical Study using Claude - A Beginner's Workflow Here's an example of a z-score study on $BTC - still tinkering so don't take this as overtly useful information but the creation of a dashboard for the visualization of statistical data is phenomenal. my current workflow: > import $BTC time data - .csv file (can get this information from multiple venues - Binance is where I got mine) > creating bins - if you have a larger data set - you can use recent data to prevent overt bias in the long direction or filter consolidation and trending regime data into separate bins for statistical analysis - however, you will have to define thresholds and determine what that entails. > defining metrics in Claude that you want to use for statistical analysis e.g. for z-score what is it based on and what type of calculation? make sure you understand the calculations being performed for any metrics that you are doing a study for and modify them accordingly. > prompting Claude to do a statistical analysis with specific instructions and then tell it to create visualization for this. I've been messing around with this and I'm seriously impressed by the output.

Stoic

19,816 次观看 • 1 年前

PhD Students – Use this FREE tool to analyze data in 10 sec. This tool is used by more than 500,000 researchers. 1. Go to 2. Click on Data Analysis and upload your data file. 3. Write your prompt for data analysis. 4. For example, generate graphs for data analysis. 5. AnswerThis will generate variety of graphs for you. 6. It also generates an insightful data analysis report. You can download the graphs and the report. Try AnswerThis today. It’s FREE. AnswerThis link:
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PhD Students – Use this FREE tool to analyze data in 10 sec. This tool is used by more than 500,000 researchers. 1. Go to 2. Click on Data Analysis and upload your data file. 3. Write your prompt for data analysis. 4. For example, generate graphs for data analysis. 5. AnswerThis will generate variety of graphs for you. 6. It also generates an insightful data analysis report. You can download the graphs and the report. Try AnswerThis today. It’s FREE. AnswerThis link:

Faheem Ullah

207,403 次观看 • 7 个月前

Graduated as a Data Science Master’s Student from Kent State University Worked as a Senior Business Data Analyst for 6 + years in India Took a decision after that to do masters in Data Science field to have a career as Data Scientist in USA 6 years Data work experience and a 2 year masters all at the age of 29 with encouragement from my parents and inspiration from my idol Emiwaytweets - Raju InnocentEvil
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Graduated as a Data Science Master’s Student from Kent State University Worked as a Senior Business Data Analyst for 6 + years in India Took a decision after that to do masters in Data Science field to have a career as Data Scientist in USA 6 years Data work experience and a 2 year masters all at the age of 29 with encouragement from my parents and inspiration from my idol Emiwaytweets - Raju InnocentEvil

INNOCENT EVIL ⁶𓅓

527,575 次观看 • 2 年前

Use this FREE tool to analyze data in 10 sec. This tool is used by more than 500,000 researchers. 1. Go to 2. Click on Data Analysis and upload your data file. 3. Write your prompt for data analysis. 4. For example, generate graphs from data. 5. AnswerThis will generate variety of graphs 6. It also generates an insightful data analysis report. You can download the graphs and the report. Try AnswerThis today. It’s FREE.
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Faheem Ullah

26,496 次观看 • 9 个月前

This is the best way to understand how ML models actually work! Use Drawdata to draw a 2D dataset in Jupyter. Use it to actively pick data from the widget and update the model as the data is being drawn! Fully interactive, real-time, and open-source!
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This is the best way to understand how ML models actually work! Use Drawdata to draw a 2D dataset in Jupyter. Use it to actively pick data from the widget and update the model as the data is being drawn! Fully interactive, real-time, and open-source!

Daily Dose of Data Science

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Use this FREE tool to analyze data in 10 sec. This tool is used by more than 500,000 researchers. 1. Go to and log in 2. Click on Data Analysis and upload your data file. 3. Write your prompt for data analysis. 4. E.g., generate graphs for exploratory data analysis. 5. Liner will generate a variety of graphs for you. 6. It also generates an insightful data analysis report. ✦ You can edit the report. For example, you can: ✦ Insert citations in the report ✦ Make changes to the text ✦ Add/remove any graphs After making changes, you can download the report. Try Liner today. It’s FREE. Liner link:
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Use this FREE tool to analyze data in 10 sec. This tool is used by more than 500,000 researchers. 1. Go to and log in 2. Click on Data Analysis and upload your data file. 3. Write your prompt for data analysis. 4. E.g., generate graphs for exploratory data analysis. 5. Liner will generate a variety of graphs for you. 6. It also generates an insightful data analysis report. ✦ You can edit the report. For example, you can: ✦ Insert citations in the report ✦ Make changes to the text ✦ Add/remove any graphs After making changes, you can download the report. Try Liner today. It’s FREE. Liner link:

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11,984 次观看 • 6 个月前

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#WATCH | Delhi | UIDAI Chairperson and World Bank Executive Director Neelkanth Mishra says, "...What is most critical is that 99% of the data in corporations is untouched by AI. None of the companies wants to share this data with AI models because once you've shared it, it's gone. And if you don't have that, then how do you get solutions?.. There is a complete market failure in the data market... India's Digital Personal Data Protection (DPDP) law, the data architecture which is based on DEPA (Data Empowerment and Protection Architecture), is a remarkable model for enabling data sharing where the owner of the data still retains control even as it makes it available for certain usage..."

ANI

36,038 次观看 • 11 天前

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Introducing: Advanced Data Assets™ By LayerAI 🧬 Data is used to power Big Data and AI. The increasing demand for high quality data to improve AI models will lead to new companies creating value in the global data market. The problem: current data collection methods make data fall short of its true potential. Data is limited. Building a decentralized solution where users choose to productize and earn from their data opens untapped sources of income. 🌐 Creating Advanced Data Assets™ There exists a market opportunity to create a digital blockchain-based global data marketplace. Data collected using our nodes, our native network apps, settled on LayerAI. The Advanced Data. Meet the Ecosystem that empowers users to participate in the Data Economy.

LayerAI | AI2Earn

27,140 次观看 • 2 年前

#WATCH | Delhi | Principal Secretary to PM, Dr PK Mishra says, "The Indian statistical system, which has a glorious past, saw some decline for a few years, but the system itself and others have been able to revive it. Also, the Prime Minister's office played a catalyst and a facilitator. The way we have now improved our system, I'm sure it will again become very well established and predominant globally." "A lot of data is generated by our departments, ministries and digital activities also. So, how to use the potential of the data, how to standardise it, how to make it compatible with each other, how to derive inferences from the data, how to ensure that the data is as trusted as comprehensive and as compatible as the earlier survey data or maybe it is expanding the scope of our statistics."
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#WATCH | Delhi | Principal Secretary to PM, Dr PK Mishra says, "The Indian statistical system, which has a glorious past, saw some decline for a few years, but the system itself and others have been able to revive it. Also, the Prime Minister's office played a catalyst and a facilitator. The way we have now improved our system, I'm sure it will again become very well established and predominant globally." "A lot of data is generated by our departments, ministries and digital activities also. So, how to use the potential of the data, how to standardise it, how to make it compatible with each other, how to derive inferences from the data, how to ensure that the data is as trusted as comprehensive and as compatible as the earlier survey data or maybe it is expanding the scope of our statistics."

ANI

87,293 次观看 • 3 个月前

Learn to train an LLM with distributed data while ensuring privacy using federated learning in a new two-part short course, Intro to Federated Learning and Federated Fine-tuning of LLMs with Private Data, created with Flower and taught by Daniel J. Beutel and Nicholas Lane. Federated learning allows a single model to be trained across multiple devices, such as phones, or multiple organizations, such as hospitals, without the need to share data to a central server. This two-part course gives you an introduction to federated learning, and then teaches you how to fine-tune your large language model with distributed data using Flower Lab’s open source federated learning framework. You’ll learn: - How to use federated learning to train a variety of models, ranging from speech and vision models to LLMs, across distributed data while offering data privacy options to users and organizations. - Privacy Enhancing Technologies like differential privacy (DP), which obscures individual data by adding calibrated noise to query results. - Two variants of differential privacy - Central and Local - and how to choose depending on your use case. - How to measure and decrease bandwidth usage to make federated learning more practical and efficient with techniques like using pre-trained models and Parameter-Efficient Fine-Tuning - How federated LLM fine-tuning reduces the risk of leaking training data. Sign up here!
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Learn to train an LLM with distributed data while ensuring privacy using federated learning in a new two-part short course, Intro to Federated Learning and Federated Fine-tuning of LLMs with Private Data, created with Flower and taught by Daniel J. Beutel and Nicholas Lane. Federated learning allows a single model to be trained across multiple devices, such as phones, or multiple organizations, such as hospitals, without the need to share data to a central server. This two-part course gives you an introduction to federated learning, and then teaches you how to fine-tune your large language model with distributed data using Flower Lab’s open source federated learning framework. You’ll learn: - How to use federated learning to train a variety of models, ranging from speech and vision models to LLMs, across distributed data while offering data privacy options to users and organizations. - Privacy Enhancing Technologies like differential privacy (DP), which obscures individual data by adding calibrated noise to query results. - Two variants of differential privacy - Central and Local - and how to choose depending on your use case. - How to measure and decrease bandwidth usage to make federated learning more practical and efficient with techniques like using pre-trained models and Parameter-Efficient Fine-Tuning - How federated LLM fine-tuning reduces the risk of leaking training data. Sign up here!

Andrew Ng

65,422 次观看 • 2 年前

World Labs CEO Dr. Fei-Fei Li & SceniX Co-Founder Yunzhu Li on the data bottleneck in robotics and how world models help: "The lack of data in training, the lack of data in evaluation, this is very, very different from language models, where data is abundant on the internet." "And we know that in order for robotics to work, we have to somehow unlock the power of scaling law. But where does that come from?" "It's a profound problem that everybody's battling with in robotics." "We see a lot of unlock in being able to do this whole process through the modeling of the environments." "Being able to create these digital worlds... that's just going to unlock so much more potential for being able to replace all the costly and the unsafe data in the real environments with the data generated from the worlds for robots to be able to do scalable learning and evaluations." Fei-Fei Li Yunzhu Li martin_casado
1:49

World Labs CEO Dr. Fei-Fei Li & SceniX Co-Founder Yunzhu Li on the data bottleneck in robotics and how world models help: "The lack of data in training, the lack of data in evaluation, this is very, very different from language models, where data is abundant on the internet." "And we know that in order for robotics to work, we have to somehow unlock the power of scaling law. But where does that come from?" "It's a profound problem that everybody's battling with in robotics." "We see a lot of unlock in being able to do this whole process through the modeling of the environments." "Being able to create these digital worlds... that's just going to unlock so much more potential for being able to replace all the costly and the unsafe data in the real environments with the data generated from the worlds for robots to be able to do scalable learning and evaluations." Fei-Fei Li Yunzhu Li martin_casado

a16z

78,216 次观看 • 2 个月前

What if your personal data became an asset you actually owned? In this May 2026 AMA, Matrix AI Network explores one of the most important questions facing AI, Web3, and the future digital economy: Who owns the data that powers artificial intelligence? As AI models continue to improve, the next major bottleneck is no longer computing power—it is access to high-quality, verified human data. The discussion covers how personal biodata can become a valuable digital asset, how data quality is measured, and why future AI companies may depend directly on users as data providers. Topics include: ✅ Biodata as a new asset class ✅ AI-grade data quality scoring ✅ Wearable devices for raw data ✅ Data ownership and monetization models ✅ Preventing fake data and device spoofing ✅ Real-world applications in healthcare, sports science and mental wellness ✅ Hypnus and the future biodata ecosystem ✅ Project direction The future may not belong to companies that own data. It belongs to the people who create it. Watch now 👇 #AI #Web3 #DataOwnership
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What if your personal data became an asset you actually owned? In this May 2026 AMA, Matrix AI Network explores one of the most important questions facing AI, Web3, and the future digital economy: Who owns the data that powers artificial intelligence? As AI models continue to improve, the next major bottleneck is no longer computing power—it is access to high-quality, verified human data. The discussion covers how personal biodata can become a valuable digital asset, how data quality is measured, and why future AI companies may depend directly on users as data providers. Topics include: ✅ Biodata as a new asset class ✅ AI-grade data quality scoring ✅ Wearable devices for raw data ✅ Data ownership and monetization models ✅ Preventing fake data and device spoofing ✅ Real-world applications in healthcare, sports science and mental wellness ✅ Hypnus and the future biodata ecosystem ✅ Project direction The future may not belong to companies that own data. It belongs to the people who create it. Watch now 👇 #AI #Web3 #DataOwnership

Matrix AI Network

61,436 次观看 • 4 个月前

This April AMA is here. In this session, the team breaks down how biodata (like sleep and brain data) can be standardized, valued, and turned into real-world assets (RWA). What you’ll learn: 1. The 4 key milestones for Biodata RWA in 2026 2. Why sleep data is the first step in building a data economy 3. How data becomes structured, scored, and monetizable 4. The role of MAN token in rewarding and settling data usage 5. How privacy is protected with distributed storage & computation 6. Ownership models: sell vs license your data 7. Why institutions (AI, pharma, insurance) need verified biodata In the future, your data won’t just exist, it will work for you.
35:10

This April AMA is here. In this session, the team breaks down how biodata (like sleep and brain data) can be standardized, valued, and turned into real-world assets (RWA). What you’ll learn: 1. The 4 key milestones for Biodata RWA in 2026 2. Why sleep data is the first step in building a data economy 3. How data becomes structured, scored, and monetizable 4. The role of MAN token in rewarding and settling data usage 5. How privacy is protected with distributed storage & computation 6. Ownership models: sell vs license your data 7. Why institutions (AI, pharma, insurance) need verified biodata In the future, your data won’t just exist, it will work for you.

Matrix AI Network

15,296 次观看 • 5 个月前

Here's how I would learn data engineering basics in 2025: - Find a data source you care about (examples: gaming APIs, stock market, web scraping, etc) - Use Python to interact and ingest your source. Initially just write the data to a CSV. - Setup an account with Snowflake or Google BigQuery. - update your Python script to load a table in Snowflake/BigQuery - schedule your script with CRON in the cloud with a service like Heroku. - build aggregations and visualizations on top of your ingested data Only thing this misses is data quality and complex job orchestration which you can learn later! How would you learn data engineering nowadays?
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Here's how I would learn data engineering basics in 2025: - Find a data source you care about (examples: gaming APIs, stock market, web scraping, etc) - Use Python to interact and ingest your source. Initially just write the data to a CSV. - Setup an account with Snowflake or Google BigQuery. - update your Python script to load a table in Snowflake/BigQuery - schedule your script with CRON in the cloud with a service like Heroku. - build aggregations and visualizations on top of your ingested data Only thing this misses is data quality and complex job orchestration which you can learn later! How would you learn data engineering nowadays?

Zach Wilson

20,412 次观看 • 1 年前

Ever wondered how AI could understand your company’s private data the way Google understands the web? Elastic is doing just that by being the foundation for context engineering: ▶️ Linking an organisation’s proprietary data to AI, making its data searchable and actionable. ▶️ Enabling multimodal, multilingual AI experiences in India’s Tier II and III cities. ▶️ Driving global business impact for enterprises and governments alike. In this exclusive conversation with Ash Kulkarni, CEO of Elastic, learn how the company is helping enterprises turn data into clarity and real-world outcomes. See the full article and video here: Shradha Sharma
1:45

Ever wondered how AI could understand your company’s private data the way Google understands the web? Elastic is doing just that by being the foundation for context engineering: ▶️ Linking an organisation’s proprietary data to AI, making its data searchable and actionable. ▶️ Enabling multimodal, multilingual AI experiences in India’s Tier II and III cities. ▶️ Driving global business impact for enterprises and governments alike. In this exclusive conversation with Ash Kulkarni, CEO of Elastic, learn how the company is helping enterprises turn data into clarity and real-world outcomes. See the full article and video here: Shradha Sharma

YourStory

16,476 次观看 • 10 个月前

I made an AI data science team with Python. I'm excited to share my new project, the AI Data Science Team, a Python package for creating AI co-pilots and agents to assist in common data science tasks. I walk you through the GitHub page, showcasing the progress and potential of this project. I'll explain how these agents can streamline tasks like code generation and modeling pipeline creation, making data science projects more efficient. Introduction to AI Data Science Team Project Integrating Human-in-the-Loop Example: Accessing Agents for Feature Engineering Setting up Feature Engineering Agent Code On GitHub: Python AI-Tips Newsletter (stay updated as I build this project in the open):
12:57

I made an AI data science team with Python. I'm excited to share my new project, the AI Data Science Team, a Python package for creating AI co-pilots and agents to assist in common data science tasks. I walk you through the GitHub page, showcasing the progress and potential of this project. I'll explain how these agents can streamline tasks like code generation and modeling pipeline creation, making data science projects more efficient. Introduction to AI Data Science Team Project Integrating Human-in-the-Loop Example: Accessing Agents for Feature Engineering Setting up Feature Engineering Agent Code On GitHub: Python AI-Tips Newsletter (stay updated as I build this project in the open):

Matt Dancho (Business Science)

11,799 次观看 • 1 年前

New Salesforce Vector Database for Data Cloud integrates structured data with unstructured data—case files, call transcripts, Slack, emails, PDFs, audio, and video. This synergy powers the most accurate AI results, advanced automations, and profound customer insights, enabling seamless collaboration between AI and human workers to supercharge your sales, service, marketing, and commerce teams. New Salesforce Vector Database for Data Cloud, transforming how businesses leverage unstructured data integrating structured business entities & keyword search. Key highlights: 1. Ground all enterprise knowledge with copilots & digital agents. 2. Launch new Lightning apps like package search & customer intelligence. 3. Automate case routing & SDR via textual data analysis. 4. Use Tableau to analyze structured & unstructured data.
1:23

New Salesforce Vector Database for Data Cloud integrates structured data with unstructured data—case files, call transcripts, Slack, emails, PDFs, audio, and video. This synergy powers the most accurate AI results, advanced automations, and profound customer insights, enabling seamless collaboration between AI and human workers to supercharge your sales, service, marketing, and commerce teams. New Salesforce Vector Database for Data Cloud, transforming how businesses leverage unstructured data integrating structured business entities & keyword search. Key highlights: 1. Ground all enterprise knowledge with copilots & digital agents. 2. Launch new Lightning apps like package search & customer intelligence. 3. Automate case routing & SDR via textual data analysis. 4. Use Tableau to analyze structured & unstructured data.

Marc Benioff

57,910 次观看 • 2 年前