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Enrollment is now open for the Data Engineering Professional Certificate! Data engineers are the architects of modern organizations, ensuring data is reliable, accessible, and ready for analytics and machine learning. This professional certificate is tailored to equip you with the critical skills, through frameworks and hands-on practice, to excel...

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DeepLearning.AI

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20,833 次观看 • 1 年前 •via X (Twitter)

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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 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!
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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 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

❌ Data Science ✅ Data Engineering is the sexiest job of the 21st century Top 7 skills you can master for Data Engineering /🧵/
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❌ Data Science ✅ Data Engineering is the sexiest job of the 21st century Top 7 skills you can master for Data Engineering /🧵/

Darshil | Data Engineer👨🏻‍🔧

69,092 次观看 • 3 年前

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

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)
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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.
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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 • 2 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
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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

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

20,368 次观看 • 1 年前

Did you know that Facebook made $134.9 billion in 2023 from selling your data? Our society has come to the point where we accepted an unfair model in which privacy is being exploited and data gifted to big tech that is selling it for billions. We intend to break the norm of how data is shared on the internet because it is outdated and unfair. You should be empowered with more control and monetization models for your data!
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Did you know that Facebook made $134.9 billion in 2023 from selling your data? Our society has come to the point where we accepted an unfair model in which privacy is being exploited and data gifted to big tech that is selling it for billions. We intend to break the norm of how data is shared on the internet because it is outdated and unfair. You should be empowered with more control and monetization models for your data!

Solana ID 🪷

30,451 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.
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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

The worst part of late-night experiments? The data analysis that comes after. OpenBio now automates your analysis - FTIR, spectroscopy data, and more. From data to publication-ready plots and insights in minutes. See it in action:
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The worst part of late-night experiments? The data analysis that comes after. OpenBio now automates your analysis - FTIR, spectroscopy data, and more. From data to publication-ready plots and insights in minutes. See it in action:

OpenBio

24,805 次观看 • 7 个月前

Today, Story becomes The DATA Foundation. $IP is now $DATA. The Story Network is now DATA Network, tracking 1B+ data records with a singular mission: to become the trust layer for all AI training data. Welcome to The DATA Foundation.
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Today, Story becomes The DATA Foundation. $IP is now $DATA. The Story Network is now DATA Network, tracking 1B+ data records with a singular mission: to become the trust layer for all AI training data. Welcome to The DATA Foundation.

The DATA Foundation

346,827 次观看 • 11 天前

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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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.

Faheem Ullah

26,452 次观看 • 5 个月前

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

205,745 次观看 • 3 个月前

The trust layer for robot and machine data is here peaqOS Stream enables machines to sign, verify, and monetize their data across Web3 and beyond → A machine signs its data at source → Any data protocol or platform can plug in and monetize it For the first time, machine data can be trusted by third parties at scale
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The trust layer for robot and machine data is here peaqOS Stream enables machines to sign, verify, and monetize their data across Web3 and beyond → A machine signs its data at source → Any data protocol or platform can plug in and monetize it For the first time, machine data can be trusted by third parties at scale

peaq

23,859 次观看 • 19 天前

The world around us is changing so fast. And to stay relevant, you need to constantly upskill yourself as well as accumulate capabilities. Data Analytics, Automation, and AI skill will continue to grow and evolve. On this channel, I will help you grow your knowledge of in these areas. From building your first machine learning model to deployment, building your first dashboard to becoming a pro, understanding business process automation using low code tools, data management, as well as learning from industry professionals through podcast. If you would like to learn about Microsoft Fabric, Power Platform, Data Management and Governance, Data Analytics, you are in the right place.
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The world around us is changing so fast. And to stay relevant, you need to constantly upskill yourself as well as accumulate capabilities. Data Analytics, Automation, and AI skill will continue to grow and evolve. On this channel, I will help you grow your knowledge of in these areas. From building your first machine learning model to deployment, building your first dashboard to becoming a pro, understanding business process automation using low code tools, data management, as well as learning from industry professionals through podcast. If you would like to learn about Microsoft Fabric, Power Platform, Data Management and Governance, Data Analytics, you are in the right place.

TheOyinbooke

15,539 次观看 • 2 年前

The amount of user data being collected by threads is insane. They now have access to your text messages, financial data, health and fitness data, “sensitive info”, and more. I know this is somewhat normal for social media apps, but this much data seems unnecessary and an overreach.
0:37

The amount of user data being collected by threads is insane. They now have access to your text messages, financial data, health and fitness data, “sensitive info”, and more. I know this is somewhat normal for social media apps, but this much data seems unnecessary and an overreach.

John Pompliano

181,979 次观看 • 3 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

Here’s how I would learn data engineering in 2025: 1. The basics: - learn SQL — SELECT, FROM, WHERE, GROUP BY, JOIN, HAVING, etc - learn Python — data structures: objects, arrays, tuples, namedtuples — algorithms: recursion, loops 2. Intermediate - learn distributed compute — pick up PySpark or Snowflake or BigQuery - learn data make architecture — pick up iceberg or delta lake - learn job orchestration — pick up Airflow or Mage - learn data quality — pick up Great expectations 3. Advanced - learn the data modeling techniques — one big table vs kimball vs Inmon vs data vault techniques - learn machine learning features and vector databases — pick up pinecone and how to fine tune LLMs with high quality data My newsletter has a deeper roadmap here:
1:19

Here’s how I would learn data engineering in 2025: 1. The basics: - learn SQL — SELECT, FROM, WHERE, GROUP BY, JOIN, HAVING, etc - learn Python — data structures: objects, arrays, tuples, namedtuples — algorithms: recursion, loops 2. Intermediate - learn distributed compute — pick up PySpark or Snowflake or BigQuery - learn data make architecture — pick up iceberg or delta lake - learn job orchestration — pick up Airflow or Mage - learn data quality — pick up Great expectations 3. Advanced - learn the data modeling techniques — one big table vs kimball vs Inmon vs data vault techniques - learn machine learning features and vector databases — pick up pinecone and how to fine tune LLMs with high quality data My newsletter has a deeper roadmap here:

Zach Wilson

29,205 次观看 • 1 年前

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 • 2 个月前

Bad data costs the US economy 3.1 Trillion dollars a year. What many fail to see is that bad data behind a secure firewall is still bad data. Incomplete or missing data affects industries into the trillions of dollars annually. Even worse, bad data can cost human life when faulty information is used in engineering, automotive, aerospace and countless other industries worldwide. Constellation Network $DAG has developed a distributed ledger technology to embrace the vision of a connected world, powered by big data, by creating the infrastructure to securely validate and protect one of the world’s most valuable resources.
0:57

Bad data costs the US economy 3.1 Trillion dollars a year. What many fail to see is that bad data behind a secure firewall is still bad data. Incomplete or missing data affects industries into the trillions of dollars annually. Even worse, bad data can cost human life when faulty information is used in engineering, automotive, aerospace and countless other industries worldwide. Constellation Network $DAG has developed a distributed ledger technology to embrace the vision of a connected world, powered by big data, by creating the infrastructure to securely validate and protect one of the world’s most valuable resources.

Constellation Network

13,502 次观看 • 1 年前