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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...

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Andrew Ng

1,247,847 subscribers

118,937 次观看 • 1 年前 •via X (Twitter)

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10 条评论

Rufus 的头像
Rufus1 年前

Cool, I will definitely enroll for this course.

Jay 的头像
Jay1 年前

This is awesome. I am definitely getting on this asap. Enjoyed your ML/DL courses on Coursera.

DE 的头像
DE1 年前

enrolled sir

Richard Buckley Watterson 的头像
Richard Buckley Watterson1 年前

Does has any plan to create yearly subscription or combine with Coursera plus?

iHope 的头像
iHope1 年前

i have the book “Fundamentals of Data Engineering", though not the knowledge

Rajeev Kumar 的头像
Rajeev Kumar1 年前

Good to hear!

Tais 的头像
Tais1 年前

Finally, a VPS service that puts customization in your hands! #GameChanger $TSAI #AI #ERC20 #ETH #NLP

naila huq 的头像
naila huq1 年前

👏

Hillary Kiptoo 的头像
Hillary Kiptoo1 年前

My current course, almost done with week 1.

Alexander De Ridder 的头像
Alexander De Ridder1 年前

Incredible. Data engineering empowers AI growth. Course looks promising.

相关视频

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 Sean Barnes, Ph.D., a Data Science & Engineering Leader at Netflix. Analyzing data remains one of the most important skills in where the world is going with AI. This comprehensive certificate takes you all the way to being job-ready. Each course comes with practical projects demonstrated in real-world contexts, such as analyzing sales data for a Korean bakery, video game sales trends across different regions, or identifying factors impacting customer retention for a communications company. You'll also work on estimating fire distribution for forest fire prevention, analyzing how a diamond's properties affect its market value, and developing predictive models for retail sales analysis, carbon emissions, and coral reef conservation. Here's some of what you'll learn: - How to define data and categorize it into its many types such as discrete & continuous numerical, structured & unstructured, time series, categorical, and know what insights can be derived from the different types of data categories. - How to differentiate between data-related job roles and their responsibilities, and how data flows through an organization from the moment of capture to decision-making. - How to perform data processing functions and apply conditional formatting in spreadsheets to extract business value from your data using statistical calculations and best practices for visualizing and interpreting data. - How to use LLMs for stakeholder analysis, data exploration, and data visualization. - Best practices for using LLMs for as a thought partner to data analysis work By the end of this professional certificate program, you will have learned core statistical concepts, analysis techniques, and visualization methodologies that will serve as the foundation for working as a data analyst. The world needs more data analysts, especially ones who know how to use modern generative AI. With data science roles projected to grow 36% by 2033, the skills taught in this program create new professional opportunities in data. Sign up here!
2:24

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 Sean Barnes, Ph.D., a Data Science & Engineering Leader at Netflix. Analyzing data remains one of the most important skills in where the world is going with AI. This comprehensive certificate takes you all the way to being job-ready. Each course comes with practical projects demonstrated in real-world contexts, such as analyzing sales data for a Korean bakery, video game sales trends across different regions, or identifying factors impacting customer retention for a communications company. You'll also work on estimating fire distribution for forest fire prevention, analyzing how a diamond's properties affect its market value, and developing predictive models for retail sales analysis, carbon emissions, and coral reef conservation. Here's some of what you'll learn: - How to define data and categorize it into its many types such as discrete & continuous numerical, structured & unstructured, time series, categorical, and know what insights can be derived from the different types of data categories. - How to differentiate between data-related job roles and their responsibilities, and how data flows through an organization from the moment of capture to decision-making. - How to perform data processing functions and apply conditional formatting in spreadsheets to extract business value from your data using statistical calculations and best practices for visualizing and interpreting data. - How to use LLMs for stakeholder analysis, data exploration, and data visualization. - Best practices for using LLMs for as a thought partner to data analysis work By the end of this professional certificate program, you will have learned core statistical concepts, analysis techniques, and visualization methodologies that will serve as the foundation for working as a data analyst. The world needs more data analysts, especially ones who know how to use modern generative AI. With data science roles projected to grow 36% by 2033, the skills taught in this program create new professional opportunities in data. Sign up here!

Andrew Ng

84,686 次观看 • 1 年前

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?
0:51

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

20,368 次观看 • 1 年前

1/ The data parsing market for robotics is just emerging, and it’s already valued in the tens of billions. Data parsing to build data models and software for Robotics is a $20B market in 2024 and is expected to reach $150B+ by 2034 Cherry AI Robotics has built a data parsing tool for robotics, which goes live next week, and will effectively make this project one of the pioneers in data parsing for robotics.
0:25

1/ The data parsing market for robotics is just emerging, and it’s already valued in the tens of billions. Data parsing to build data models and software for Robotics is a $20B market in 2024 and is expected to reach $150B+ by 2034 Cherry AI Robotics has built a data parsing tool for robotics, which goes live next week, and will effectively make this project one of the pioneers in data parsing for robotics.

CherryAI Robotics

90,786 次观看 • 10 个月前

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.
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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

27,136 次观看 • 2 年前

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):
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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

We can only trust X 🚨 META/ Facebook planning to build data centers in China, store their data in China, and then GIVE THE CHINESE GOVERNMENT ACCESS to the data “In this document, Facebook is talking about making Chinese user data available to the Chinese government because they're going to store that data in China. Is that correct?” “Correct” “When you store that data in China, Americans who exchange messages or other information with Chinese Facebook users, that would mean the Chinese government could get access to the American data as well, is that correct? “Through the POP servers? Potentially.” “And Facebook was willing to take that risk?” “Yes. There was a lot of discussion about this and ultimately yes.” “I mean, this is, this is extraordinary. This is exactly contrary to what Facebook has represented for years here. They're willing to build data centers, store data in China. They are willing explicitly to give the Chinese government access to it”
1:00

We can only trust X 🚨 META/ Facebook planning to build data centers in China, store their data in China, and then GIVE THE CHINESE GOVERNMENT ACCESS to the data “In this document, Facebook is talking about making Chinese user data available to the Chinese government because they're going to store that data in China. Is that correct?” “Correct” “When you store that data in China, Americans who exchange messages or other information with Chinese Facebook users, that would mean the Chinese government could get access to the American data as well, is that correct? “Through the POP servers? Potentially.” “And Facebook was willing to take that risk?” “Yes. There was a lot of discussion about this and ultimately yes.” “I mean, this is, this is extraordinary. This is exactly contrary to what Facebook has represented for years here. They're willing to build data centers, store data in China. They are willing explicitly to give the Chinese government access to it”

Wall Street Apes

80,854 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 • 7 个月前

Imitation learning is great, but needs us to have (near) optimal data. We throw away most other data (failures, evaluation data, suboptimal data, undirected play data), even though this data can be really useful and way cheaper! In our new work - RISE, we show a simple way to *use all of this non-optimal data to robustify imitation learning* with minimal requirements beyond BC. Key idea: use non-expert data to learn how to *recover* back to expert data with a minimal frills offline RL that works under sparse data coverage. Allows usage of *all* available data, not just expert data - never throw your data away! Paper: Website: A 🧵(1/10)
0:57

Imitation learning is great, but needs us to have (near) optimal data. We throw away most other data (failures, evaluation data, suboptimal data, undirected play data), even though this data can be really useful and way cheaper! In our new work - RISE, we show a simple way to *use all of this non-optimal data to robustify imitation learning* with minimal requirements beyond BC. Key idea: use non-expert data to learn how to *recover* back to expert data with a minimal frills offline RL that works under sparse data coverage. Allows usage of *all* available data, not just expert data - never throw your data away! Paper: Website: A 🧵(1/10)

Abhishek Gupta

20,595 次观看 • 8 个月前

Making Data and AI Lovable. Lovable now integrates with Databricks, providing a natural language interface that allows anyone—regardless of technical skills—to build live data apps can read and write data stored in Databricks. Bridge the gap between complex data engineering and beautiful, functional front-ends.
0:35

Making Data and AI Lovable. Lovable now integrates with Databricks, providing a natural language interface that allows anyone—regardless of technical skills—to build live data apps can read and write data stored in Databricks. Bridge the gap between complex data engineering and beautiful, functional front-ends.

Databricks

34,247 次观看 • 3 个月前

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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 • 2 个月前

AI is transforming the way businesses operate 🤖. It’s no longer just a tool, it powers decision-making, efficiency, and growth. From automating tasks to predicting trends and uncovering insights, AI is becoming the backbone of modern business. But AI isn’t magic. It needs massive amounts of high-quality data 📊. Companies like Scale AI and other Web2 giants thrive because they control the pipelines of this data, turning it into a huge competitive advantage. The problem is most of this data is locked in centralized systems 🔒, expensive, and often inaccessible for smaller companies or innovators. That’s where OORT | The Data Cloud for Decentralized AI comes in 🌐✨. By building decentralized storage and AI infrastructure, OORTECH makes data open, secure, and accessible to everyone. It’s a foundation for the next generation of AI-powered businesses, giving all innovators the chance to compete and create 🚀
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AI is transforming the way businesses operate 🤖. It’s no longer just a tool, it powers decision-making, efficiency, and growth. From automating tasks to predicting trends and uncovering insights, AI is becoming the backbone of modern business. But AI isn’t magic. It needs massive amounts of high-quality data 📊. Companies like Scale AI and other Web2 giants thrive because they control the pipelines of this data, turning it into a huge competitive advantage. The problem is most of this data is locked in centralized systems 🔒, expensive, and often inaccessible for smaller companies or innovators. That’s where OORT | The Data Cloud for Decentralized AI comes in 🌐✨. By building decentralized storage and AI infrastructure, OORTECH makes data open, secure, and accessible to everyone. It’s a foundation for the next generation of AI-powered businesses, giving all innovators the chance to compete and create 🚀

Sky Wee

67,552 次观看 • 9 个月前

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AI is transforming the way businesses operate 🤖. It helps companies work faster, cut costs, and make smarter decisions. From automating repetitive tasks to predicting trends and uncovering insights, AI is becoming the backbone of modern business. But AI can’t function without data 📊. Massive amounts of high-quality data are required to train models and make them effective. Right now, most of this data is controlled by a few centralized giants 🔒, making it expensive, siloed, and out of reach for smaller businesses and innovators. This is where OORT | The Data Cloud for Decentralized AI comes in 🌐✨. By combining decentralized storage with AI infrastructure powered by blockchain, OORT makes data open, secure, and accessible to everyone. It’s building a foundation where businesses of all sizes can tap into AI’s full potential and compete in the AI-driven economy 🚀

Sky Wee

58,239 次观看 • 8 个月前

We have data in abundance now. The real value isn't within the RAW Data, but is this data validated and verified? More value in data refinement. We have a solution for this which is validated by 6 years of research and development with the Department of Defense Now securing and validating commercial data from partners like Common Crawl and Panasonic
1:03

We have data in abundance now. The real value isn't within the RAW Data, but is this data validated and verified? More value in data refinement. We have a solution for this which is validated by 6 years of research and development with the Department of Defense Now securing and validating commercial data from partners like Common Crawl and Panasonic

Constellation²

10,559 次观看 • 1 年前

Getting a big tech data engineer job in 2016: - do you know SQL? - yes - here’s $500k Getting a big tech data engineer job in 2024: - do you know Spark, Kafka, Iceberg? - yes - did you shake hands with Bill Inmon when he invented the data warehouse? - no - rejected and ghosted Here’s why! Follow to stay up to date on data engineering!
1:22

Getting a big tech data engineer job in 2016: - do you know SQL? - yes - here’s $500k Getting a big tech data engineer job in 2024: - do you know Spark, Kafka, Iceberg? - yes - did you shake hands with Bill Inmon when he invented the data warehouse? - no - rejected and ghosted Here’s why! Follow to stay up to date on data engineering!

Zach Wilson

43,547 次观看 • 1 年前

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Introducing the Future of AI-Powered Sports Data with $FXI.⚡️ We’re not just talking about innovation, we’re building it. 🚀 From #AI-driven data capture to on-chain validation and data monetization, the FX1 ecosystem is rewriting the rules of the sports and data economy. Our platform empowers leagues, teams, and creators to unlock the full potential of their data with cutting-edge AI solutions. 🧠 Why FX1? ➡️ AI-Powered Precision: Transform raw sports data into actionable insights. ➡️ Data Validation on Chain: Building trust in the data economy through blockchain. ➡️ AI-Powered Revenue Streams: Use sports data to drive predictive analytics, odds modeling, and other high-value solutions that unlock new ways to monetize. FX1 is leading the charge in AI innovation, creating a seamless bridge between sports, blockchain, and the data economy. This isn’t just evolution - it’s a revolution. 💡 The future of sports is here. And it’s powered by AI. 🤖 Watch the full video and see how we’re turning vision into reality. 👇

FX1

23,913 次观看 • 1 年前

AI completely changes the way we can talk to data. Hex is leading the way by building collaborative, shareable analytics workspaces. I sat down with CEO Barry McCardel to discuss how agent workflows, conversational interfaces, and context-aware models are reshaping analysis. We cover how Hex aims to make everyone a data person by unifying analysis and AI in one workflow, how Hex is replacing outdated fragmented workflows, and the importance of trust and accuracy in AI systems. We also go over Hex’s awesome product launch videos, Barry’s emphasis on being fun and weird, and lessons he learned from Palantir. Enjoy! 0:00 The challenge of trustworthy data 1:19 Context management, observability, and governance 9:24 The promise and reality of data democratization 15:51 The impact of AI on data workflows 31:39 Breadth, depth, and speed: the three dimensions of AI in data 48:04 The rise of ambient analytics 55:56 The modern data stack and industry consolidation 1:03:40 Acquisitions and owning the data insight layer 1:06:06 Lessons from Palantir: forward-deployed engineering 1:13:13 Commitment engineering and customer collaboration 1:17:11 Brand, launch videos, and having fun in SaaS Barry McCardel Hex
1:22:12

AI completely changes the way we can talk to data. Hex is leading the way by building collaborative, shareable analytics workspaces. I sat down with CEO Barry McCardel to discuss how agent workflows, conversational interfaces, and context-aware models are reshaping analysis. We cover how Hex aims to make everyone a data person by unifying analysis and AI in one workflow, how Hex is replacing outdated fragmented workflows, and the importance of trust and accuracy in AI systems. We also go over Hex’s awesome product launch videos, Barry’s emphasis on being fun and weird, and lessons he learned from Palantir. Enjoy! 0:00 The challenge of trustworthy data 1:19 Context management, observability, and governance 9:24 The promise and reality of data democratization 15:51 The impact of AI on data workflows 31:39 Breadth, depth, and speed: the three dimensions of AI in data 48:04 The rise of ambient analytics 55:56 The modern data stack and industry consolidation 1:03:40 Acquisitions and owning the data insight layer 1:06:06 Lessons from Palantir: forward-deployed engineering 1:13:13 Commitment engineering and customer collaboration 1:17:11 Brand, launch videos, and having fun in SaaS Barry McCardel Hex

Sarah Wang

41,746 次观看 • 7 个月前

Ever wondered how we come up with combos with new characters and new moves? It’s all in the frame data! Frame data opens up a new perspective to the game. This is one of the most basic levels of frame data 101. Check it out! #streetfighter #streetfighter6 #kimberly #sf6
2:20

Ever wondered how we come up with combos with new characters and new moves? It’s all in the frame data! Frame data opens up a new perspective to the game. This is one of the most basic levels of frame data 101. Check it out! #streetfighter #streetfighter6 #kimberly #sf6

Sherry Nhan

109,126 次观看 • 3 年前

The FBI has published the monthly update of reported crime and law enforcement data to the Crime Data Explorer at Watch the below video to learn more about how this data, available in this new release cadence, can help law enforcement crush violent crime.
0:48

The FBI has published the monthly update of reported crime and law enforcement data to the Crime Data Explorer at Watch the below video to learn more about how this data, available in this new release cadence, can help law enforcement crush violent crime.

FBI

31,078 次观看 • 9 个月前

1/ Cherry AI Robotics is building the world’s first data layer for robotics. Every autonomous robot needs a neural network to operate, which requires massive domain-specific data. Cherry AI Robotics has an entire ecosystem of products with 200k+ communities and 800k+ users. These products generate large amounts of data, which is parsed to create valuable data sets for robotics. Learn more:
0:30

1/ Cherry AI Robotics is building the world’s first data layer for robotics. Every autonomous robot needs a neural network to operate, which requires massive domain-specific data. Cherry AI Robotics has an entire ecosystem of products with 200k+ communities and 800k+ users. These products generate large amounts of data, which is parsed to create valuable data sets for robotics. Learn more:

CherryAI Robotics

105,357 次观看 • 10 个月前