data engineering

Vibe coding is revolutionizing the way we approach software development. At its core, vibe coding means expressing your intent in natural language and letting AI coding assistants translate that intent into working code. Instead of sweating the syntax, you describe the “vibe” of what you want—be it a data pipeline, a web app, or an analytics automation script—and frameworks like Replit, GitHub Copilot, Gemini Code Assist, and others do the heavy lifting.

This blog will guide you through what vibe coding is, why it matters, its benefits and limitations, and a deep dive into the frameworks making it possible. Whether you’re a data engineer, software developer, or just AI-curious, you’ll discover how prompt engineering, large language models, and rapid prototyping are reshaping the future of software development.

What Is Vibe Coding?

Vibe coding is a new paradigm in software development where you use natural language programming to instruct AI coding assistants to generate, modify, and even debug code. The term, popularized by AI thought leaders like Andrej Karpathy, captures the shift from manual coding to intent-driven development powered by large language models (LLMs) such as GPT-4, Gemini, and Claude.

How does vibe coding work?

• You describe your goal in plain English (e.g., “Build a REST API for customer management in Python”).
• The AI coding assistant interprets your prompt and generates the code.
• You review, refine, and iterate—often using further prompts to tweak or extend the solution.

This approach leverages advances in prompt engineering, code generation, and analytics automation, making software development more accessible and efficient than ever before.

Learn more about LLMs and their applications in this Data Science Dojo guide.

The Benefits of Vibe Coding

1. Accelerated Rapid Prototyping

Vibe coding enables you to move from idea to prototype in minutes. By using natural language programming, you can quickly test concepts, automate analytics, or build MVPs without getting bogged down in boilerplate code.

2. Lower Barrier to Entry

AI coding assistants democratize software development. Non-developers, data analysts, and business users can now participate in building solutions, thanks to intuitive prompt engineering and low-code interfaces.

3. Enhanced Productivity

Developers can focus on high-level architecture and problem-solving, letting AI handle repetitive or routine code generation. This shift boosts productivity and allows teams to iterate faster.

4. Consistency and Best Practices

Many frameworks embed best practices and patterns into their code generation, helping teams maintain consistency and reduce errors.

5. Seamless Integration with Data Engineering and Analytics Automation

Vibe coding is especially powerful for data engineering tasks—think ETL pipelines, data validation, and analytics automation—where describing workflows in natural language can save hours of manual coding.

For more on how AI is transforming workflows, see How AI is Transforming Data Science Workflows.

The Frameworks Powering Vibe Coding

Let’s explore the leading frameworks and tools that make vibe coding possible. Each brings unique strengths to the table, enabling everything from code generation to analytics automation and low-code development.

Replit

Replit is a cloud-based development environment that brings vibe coding to life. Its Ghostwriter AI coding assistant allows you to describe what you want in natural language, and it generates code, suggests improvements, and even helps debug. Replit supports dozens of languages and is ideal for rapid prototyping, collaborative coding, and educational use.

• Key Features: Real-time code generation, multi-language support, collaborative editing, and instant deployment.
• Use Case: “Create a Python script to scrape weather data and visualize it”—Ghostwriter handles the rest.

Learn more at Replit.

GitHub Copilot

GitHub Copilot, is an AI coding assistant that integrates directly into your IDE (like VS Code). It offers real-time code suggestions, autocompletes functions, and can even generate entire modules from a prompt. Copilot excels at code generation for software development, data engineering, and analytics automation.

• Key Features: Inline code suggestions, support for dozens of languages, context-aware completions, and integration with popular IDEs.
• Use Case: “Write a function to clean and merge two dataframes in pandas”—Copilot generates the code as you type.

Explore more at GitHub Copilot.

Gemini Code Assist

Gemini Code Assist is Google’s AI-powered coding partner, designed to help developers write, understand, and optimize code using natural language programming. It’s particularly strong in analytics automation and data engineering, offering smart code completions, explanations, and refactoring suggestions.

• Key Features: Context-aware code generation, integration with Google Cloud, and support for prompt-driven analytics workflows.
• Use Case: “Build a data pipeline that ingests CSV files from Google Cloud Storage and loads them into BigQuery.”

Learn more at Gemini Code Assist.

Cursor

Cursor is an AI-powered IDE built from the ground up for vibe coding. It enables developers to write prompts, generate code, and iterate—all within a seamless, collaborative environment. Cursor is ideal for rapid prototyping, low-code development, and team-based software projects.

• Key Features: Prompt-driven code generation, collaborative editing, and integration with popular version control systems.
• Use Case: “Generate a REST API in Node.js with endpoints for user authentication and data retrieval.”

Discover Cursor at Cursor.

OpenAI Codex

OpenAI Codex is the engine behind many AI coding assistants, including GitHub Copilot and ChatGPT. It’s a large language model trained specifically for code generation, supporting dozens of programming languages and frameworks.

• Key Features: Deep code understanding, multi-language support, and integration with various development tools.
• Use Case: “Translate this JavaScript function into Python and optimize for performance.”

Read more about Codex at OpenAI Codex.

IBM watsonx Code Assistant

IBM watsonx Code Assistant is an enterprise-grade AI coding assistant designed for analytics automation, data engineering, and software development. It offers advanced prompt engineering capabilities, supports regulatory compliance, and integrates with IBM’s cloud ecosystem.

• Key Features: Enterprise security, compliance features, support for analytics workflows, and integration with IBM Cloud.
• Use Case: “Automate ETL processes for financial data and generate audit-ready logs.”

Explore IBM watsonx Code Assistant at IBM.

How Vibe Coding Empowers Data Engineering and Analytics Automation

Vibe coding isn’t just for web apps or simple scripts—it’s a game-changer for data engineering and analytics automation. Here’s how:

• ETL Pipelines: Describe your data flow in natural language, and let AI generate the code to extract, transform, and load data.
• Analytics Automation: Automate reporting, dashboard creation, and data validation with prompt-driven workflows.
• Rapid Prototyping: Test new data models, algorithms, or analytics strategies in minutes, not days.

See how Context Engineering shapes reliable, context-aware LLM outputs.

The Limitations of Vibe Coding

While vibe coding is a game-changer, it’s not without challenges:

• Code Quality and Reliability: AI-generated code may contain subtle bugs or inefficiencies. Always review and test before deploying.
• Debugging Complexity: If you don’t understand the generated code, troubleshooting can be tough.
• Security Risks: AI may inadvertently introduce vulnerabilities. Human oversight is essential.
• Scalability: Vibe coding excels at rapid prototyping and automation, but complex, large-scale systems still require traditional software engineering expertise.
• Over-Reliance on AI: Relying solely on AI coding assistants can erode foundational coding skills over time.

For a deep dive into prompt engineering and its importance, check out Master Prompt Engineering: Proven Strategies and Hands-On Examples.

Best Practices for Effective Vibe Coding

1. Be Specific with Prompts: Clear, detailed instructions yield better results.
2. Iterate and Refine: Use feedback loops to improve code quality.
3. Review and Test: Always validate AI-generated code for correctness and security.
4. Document Your Work: Maintain clear documentation for future maintenance.
5. Stay Involved: Use AI as a copilot, not a replacement for human expertise.

For hands-on strategies, check out Strategies to master prompt engineering by hands-on examples.

The Future of Vibe Coding

As large language models and AI coding assistants continue to evolve, vibe coding will become the default for:

• Internal tool creation
• Business logic scripting
• Data engineering automation
• Low-code/no-code backend assembly

Emerging trends include multimodal programming (voice, text, and visual), agentic AI for workflow orchestration, and seamless integration with cloud platforms.

Stay updated with the latest trends in Agentic AI.

Frequently Asked Questions (FAQs)

Q1: Is vibe coding replacing traditional programming?

No—it augments it. Developers still need to review, refine, and understand the code.

Q2: Can vibe coding be used for production systems?

Yes, with proper validation, testing, and reviews. AI can scaffold, but humans should own the last mile.

Q3: What languages and frameworks does vibe coding support?

Virtually all popular languages (Python, JavaScript, SQL) and frameworks (Django, React, dbt, etc.).

Q4: How can I start vibe coding today?

Try tools like Replit, GitHub Copilot, Gemini Code Assist, or ChatGPT. Start with small prompts and iterate.

Q5: What are the limitations of vibe coding?

Best for prototyping and automation; complex systems still require traditional expertise.

Conclusion & Next Steps

Vibe coding is more than a trend—it’s a fundamental shift in how we build software. By leveraging AI coding assistants, prompt engineering, and frameworks like Replit, GitHub Copilot, Gemini Code Assist, Cursor, ChatGPT, Claude, OpenAI Codex, and IBM watsonx Code Assistant, you can unlock new levels of productivity, creativity, and accessibility in software development.

Ready to try vibe coding?

• Explore the frameworks above and experiment with prompt-driven development.
• Dive deeper into prompt engineering and AI-powered workflows on Data Science Dojo’s blog.

Data engineering tools are specialized software applications or frameworks designed to simplify and optimize the process of managing, processing, and transforming large volumes of data. These tools provide data engineers with the necessary capabilities to efficiently extract, transform, and load (ETL) data, build scalable data pipelines, and prepare data for further analysis and consumption by other applications.

By offering a wide range of features, such as data integration, transformation, and quality management, data engineering tools help ensure that data is structured, reliable, and ready for decision-making.

Data engineering tools also enable workflow orchestration, automate tasks, and provide data visualization capabilities, making it easier for teams to manage complex data processes. In today’s data-driven world, these tools are essential for building efficient, effective data pipelines that support business intelligence, analytics, and overall data strategy.

 

 

Top 10 data engineering tools

In summary, data engineering tools are vital for managing, processing, and transforming data efficiently. They streamline workflows, handle big data challenges, and ensure the availability of high-quality data for analysis. These tools enhance scalability, optimize performance, and support seamless integration, making data accessible and reliable for decision-making.

Ultimately, data engineering tools enable organizations to build effective data pipelines and maintain data security, unlocking valuable insights across teams.

 

Data Science Dojo is offering DBT for FREE on Azure Marketplace packaged with support for various data warehouses and data lakes to be configured from CLI. 

 

What does DBT stands for? 

Traditionally, data engineers had to process extensive data available at multiple data clouds in the same available cloud environments. The next task was to migrate the data and then transform it as per the requirements, but Data migration was a task not easy to do so. DBT short for Data Build Tool, allows the analysts and engineers to manipulate massive amounts of data from various significant cloud warehouses to be processed reliably at a single workstation using modular SQL. 

It is basically the “T” in ELT for data transformation in diverse data warehouses. 

 

ELT vs ETL – Insights of both terms

Now what do these two terms mean? Have a look at the table below: 

	ELT	ETL
1.	Stands for Extraction Load Transform	Stands for Extraction Transform Load
2.	Supports structured, unstructured, semi structured and raw type of data	Requires relational and structured dataset
3.	New technology, so it’s difficult to find experts or to create data pipelines	Old process, used for over 20 years now
4.	Dataset is extracted from sources and warehoused in the destination and then transformed	After extraction, data is brought into the staging area where’s its transformed and then loaded into target system
5.	Quick data loading time because data is integrated at target system once and then transformed	Takes more time as it’s a multistage process involving a staging area for transformation and twice loading operations

 

Use cases for ELT 

Since dbt relates closely to ELT process, let’s discuss its use cases: 

• Associations with huge volumes of information: Meteorological frameworks like weather forecasters gather, examine and utilize a lot of information consistently. Organizations with enormous exchange volumes additionally fall into this classification. The ELT process considers faster exchange of data 

• Associations needing quick accessibility: Stock trades produce and utilize a lot of data continuously, where postponements can be destructive. 

 

Challenges for Data Build Tool (DBT)

Data distributed across multiple data centers and the ability to transform those volumes at a single place was a big challenge. 

Then testing and documenting the workflow was another problem. 

Therefore, an engine that could cater to the multiple disjointed data warehouses for data transformation would be suitable for the data engineers. Additionally, testing the complex data pipeline with the same agent would do wonders. 

Working of DBT

Data Build Tool is a partially open-source platform for transforming and modeling data obtained from your data warehouses all in one place. It allows the usage of simple SQL to manipulate data acquired from different sources. Users can document their files and can generate DAG diagrams thereby identifying the lineage of workflow using dbt docs. Automated tests can be run to detect flaws and missing entries in the data models as well. Ultimately, you can deploy the transformed data model to any other warehouse. DBT serves pleasantly in the cutting-edge information stack and is considered cloud agnostic meaning it operates with several significant cloud environments. 

 

(Picture Courtesy: https://www.getdbt.com/ ) 

 

 Important aspects of DBT

• DBT enables data analysts with the feasibility to take over the task of data engineers. With modular SQL at hand, analysts can take ownership of data transformation and eventually create visualizations upon it 
• It’s cloud agnostic which means that DBT can handle multiple significant cloud environments with their warehouses such as BigQuery, Redshift, and Snowflake to process mission-critical data 
• Users can maintain a profile specifying connections to different data sources along with schema and threads 
• Users can document their work and can generate DAG diagrams to visualize their workflow 

• Through the snapshot feature, you can take a copy of your data at any point in time for a variety of reasons such as tracing changes, time intervals, etc. 

 

What Data Science Dojo has for you 

DBT instance packaged by Data Science Dojo comes with pre-installed plugins which are ready to use from CLI without the burden of installation. It provides the flexibility to connect with different warehouses, load the data, transform it using analysts’ favorite language – SQL and finally deploy it to the data warehouse again or export it to data analysis tools. 

• Ubuntu VM having dbt Core installed to be used from Command Line Interface (CLI) 
• Database: PostgreSQL 
• Support for BigQuery 
• Support for Redshift 
• Support for Snowflake 
• Robust integrations 
• A web interface at port 8080 is spun up by dbt docs to visualize the documentation and DAG workflow 
• Several data models as samples are provided after initiating a new project 

This dbt offer is compatible with the following cloud providers: 

• GCP 
• Snowflake 
• AWS 

 

Disclaimer: The service in consideration is the free open-source version which operates from CLI. The paid features as stated officially by DBT are not endorsed in this offer. 

Conclusion 

Incoherent sources, data consistency problems, and conflicting definitions for measurements and enterprise details lead to disarray, excess endeavors, and unfortunate data being dispersed for decision-making. DBT resolves all these issues. It was built with version control in mind. It has enabled data analysts to take on the role of data engineers. Any developer with good SQL skills is able to operate on the data – this is in fact the beauty of this tool. 

At Data Science Dojo, we deliver data science education, consulting, and technical services to increase the power of data. Therefore, to enhance your data engineering and analysis skills and make the most out of this tool, use the Data Science Bootcamp by Data Science Dojo, your ideal companion in your journey to learn data science! 

Click on the button below to head over to the Azure Marketplace and deploy DBT for FREE by clicking on “Get it now”. 

 

Note: You’ll have to sign up to Azure, for free, if you do not have an existing account. 

50 self-explanatory data science quotes by thought leaders you need to read if you’re a Data Scientist, – covering the four core components of data science landscape. 

Data science for anyone can seem scary. This made me think of developing a simpler approach to it. To reinforce a complicated idea, quotes can do wonders. Also, they are a sneak peek into the window of the author’s experience. With precise phrasing with chosen words, it reinstates a concept in your mind and offers a second thought to your beliefs and understandings.  

In this article, we jot down 51 best quotes on data science that were once shared by experts. So, before you let the fear of data science get to you, browse through the wise words of industry experts divided into four major components to get inspired. 

 

 

Data strategy 

If you successfully devise a data strategy with the information available, then it will help you to debug a business problem. It builds a connection to the data you gather and the goals you aim to achieve with it. Here are five inspiring and famous data strategy quotes by Bernard Marr from his book, “Data Strategy: How to Profit from a World of Big Data, Analytics and the Internet of Things” 

1. “Those companies that view data as a strategic asset are the ones that will survive and thrive.” 
2. “Doesn’t matter how much data you have, it’s whether you use it successfully that counts.” 
3. “If every business, regardless of size, is now a data business, every business, therefore, needs a robust data strategy.” 
4. “They need to develop a smart strategy that focuses on the data they really need to achieve their goals.” 
5. “Data has become one of the most important business assets, and a company without a data strategy is unlikely to get the most out of their data resources.” 

Other Best Quotes on Data Science

Some other influential data strategy quotes are as follows: 

6. “Big data is at the foundation of all of the megatrends that are happening today, from social to mobile to the cloud to gaming.” – Chris Lynch, Former CEO, Vertica  

7. “You can’t run a business today without data. But you also can’t let the numbers drive the car. No matter how big your company is or how far along you are, there’s an art to company-building that won’t fit in any spreadsheet.” Chris Savage, CEO, Wistia 

8. “Data science is a combination of three things: quantitative analysis (for the rigor required to understand your data), programming (to process your data and act on your insights), and narrative (to help people comprehend what the data means).” — Darshan Somashekar, Co-founder, at Unwind media 

9. “In the next two to three years, consumer data will be the most important differentiator. Whoever is able to unlock the reams of data and strategically use it will win.” — Eric McGee, VP Data and Analytics 

10. “Data science isn’t about the quantity of data but rather the quality.” — Joo Ann Lee, Data Scientist, Witmer Group 

11. “If someone reports close to a 100% accuracy, they are either lying to you, made a mistake, forecasting the future with the future, predicting something with the same thing, or rigged the problem.” — Matthew Schneider, Former United States Attorney 

12. “Executive management is more likely to invest in data initiatives when they understand the ‘why.’” — Della Shea, Vice President of Privacy and Data Governance, Symcor

13. “If you want people to make the right decisions with data, you have to get in their head in a way they understand.” — Miro Kazakoff, Senior Lecturer, MIT Sloan 

14. “Everyone has the right to use company data to grow the business. Everyone has the responsibility to safeguard the data and protect the business.” — Travis James Fell, CSPO, CDMP, – Product Manager 

15. “For predictive analytics, we need an infrastructure that’s much more responsive to human-scale interactivity. The more real-time and granular we can get, the more responsive, and more competitive, we can be.”  Peter Levine, VC and General Partner ,Andreessen Horowitz 

Data engineering 

Without a sophisticated system or technology to access, organize, and use the data, data science is no less than a bird without wings. Data engineering builds data pipelines and endpoints to utilize the flow of data. Check out these top quotes on data engineering by thought leaders: 

16. “Defining success with metrics that were further downstream was more effective.” John Egan, Head of Growth Engineer, Pinterest 

17. ” Wrangling data is like interrogating a prisoner. Just because you wrangled a confession doesn’t mean you wrangled the answer.” — Brad Schneider – Politician 

18. “If you have your engineering team agree to measure the output of features quarter over quarter, you will get more features built. It’s just a fact.” Jason Lemkin, Founder, SaaStr Fund 

19. “Data isn’t useful without the product context. Conversely, having only product context is not very useful without objective metrics…” Jonathan Hsu, CFO, and COO,  AppNexus & Head of Data Science, at Social Capital 

20.  “I think you can have a ridiculously enormous and complex data set, but if you have the right tools and methodology, then it’s not a problem.” Aaron Koblin, Entrepreneur in Data and Digital Technologies 

21. “Many people think of data science as a job, but it’s more accurate to think of it as a way of thinking, a means of extracting insights through the scientific method.” — Thilo Huellmann, Co-fFounder, at Levity 

22. “You want everyone to be able to look at the data and make sense out of it. It should be a value everyone has at your company, especially people interacting directly with customers. There shouldn’t be any silos where engineers translate the data before handing it over to sales or customer service. That wastes precious time.” Ben Porterfield, Founder and VP of Engineering, at Looker 

23. “Of course, hard numbers tell an important story; user stats and sales numbers will always be key metrics. But every day, your users are sharing a huge amount of qualitative data, too — and a lot of companies either don’t know how or forget to act on it.” Stewart Butterfield, CEO,   Slack

 

 

Data analysis and models 

Every business is bombarded with a plethora of data every day. When you get tons of data, analyze it and make impactful decisions. Data analysis uses statistical and logical techniques to model the use of data:.  

24. “In most cases, you can’t build high-quality predictive models with just internal data.” — Asif Syed, Vice President of Data Strategy, Hartford Steam Boiler 

25. “Since most of the world’s data is unstructured, an ability to analyze and act on it presents a big opportunity.” — Michael Shulman, Head of Machine Learning, Kensho 

26. “It’s easy to lie with statistics. It’s hard to tell the truth without statistics.” — Andrejs Dunkels, Mathematician, and Writer 

27. “Information is the oil of the 21st century, and analytics is the combustion engine.” Peter Sondergaard, Senior Vice President, Gartner Research 

28. “Use analytics to make decisions. I always thought you needed a clear answer before you made a decision and the thing that he taught me was [that] you’ve got to use analytics directionally…and never worry whether they are 100% sure. Just try to get them to point you in the right direction.” Mitch Lowe, Co-founder of Netflix 

29. “Your metrics influence each other. You need to monitor how. Don’t just measure which clicks generate orders. Back it up and break it down. Follow users from their very first point of contact with you to their behavior on your site and the actual transaction. You have to make the linkage all the way through.” Lloyd Tabb, Founder, Looker 

30. “Don’t let shallow analysis of data that happens to be cheap/easy/fast to collect nudge you off-course in your entrepreneurial pursuits.” Andrew Chen, Partner at Andreessen Horowitz,  

31. “Our real job with data is to better understand these very human stories, so we can better serve these people. Every goal your business has is directly tied to your success in understanding and serving people.” — Daniel Burstein, Senior Director, Content & Marketing, Marketing Sherpa 

32. “A data scientist combines hacking, statistics, and machine learning to collect, scrub, examine, model, and understand data. Data scientists are not only skilled at working with data, but they also value data as a premium product.” — Erwin Caniba, Founder and Owner,Digitacular Marketing Solutions 

33. “It has therefore become a strategic priority for visionary business leaders to unlock data and integrate it with cloud-based BI and analytic tools.” — Gil Peleg, Founder , Model 9 – Crunchbase 

34.  “The role of data analytics in an organization is to provide a greater level of specificity to discussion.” — Jeff Zeanah, Analytics Consultant  

35. “Data is the nutrition of artificial intelligence. When an AI eats junk food, it’s not going to perform very well.” — Matthew Emerick, Data Quality Analyst 

36. “Analytics software is uniquely leveraged. Most software can optimize existing processes, but analytics (done right) should generate insights that bring to life whole new initiatives. It should change what you do, not just how you do it.”  Matin Movassate, Founder, Heap Analytics 

37. “No major multinational organization can ever expect to clean up all of its data – it’s a never-ending journey. Instead, knowing which data sources feed your BI apps, and the accuracy of data coming from each source, is critical.” — Mike Dragan, COO, – Oveit 

38. “All analytics models do well at what they are biased to look for.” — Matthew Schneider, Strategic Adviser 

39. “Without big data analytics, companies are blind and deaf, wandering out onto the web like deer on a freeway.” Geoffrey Moore, Author and Consultant 

Data visualization and operationalization 

When you plan to take action with your data, you visualize it on a very large canvas. For an actionable insight, you must squeeze the meaning out of all the analysis performed on that data, this is data visualization. Some  data visualization quotes that might interest you are: 

40. “Companies have tons and tons of data, but [success] isn’t about data collection, it’s about data management and insight.” — Prashanth Southekal, Business Analytics Author 

41. “Without clean data, or clean enough data, your data science is worthless.” — Michael Stonebraker, Adjunct Professor, MIT 

42. “The skill of data storytelling is removing the noise and focusing people’s attention on the key insights.” — Brent Dykes, Author, “Effective Data Storytelling” 

43. “In a world of more data, the companies with more data-literate people are the ones that are going to win.” — Miro Kazakoff, Senior Lecturer, MIT Sloan 

44. The goal is to turn data into information and information into insight. Carly Fiorina, Former CEO, Hewlett Packard 

45. “Data reveals impact, and with data, you can bring more science to your decisions.” Matt Trifiro, CMO, at Vapor IO 

46. “The skill of data storytelling is removing the noise and focusing people’s attention on the key insights.” — Brent Dykes, data strategy consultant and author, “Effective Data Storytelling” 

47. “In a world of more data, the companies with more data-literate people are the ones that are going to win.” — Miro Kazakoff, Senior Lecturer, MIT Sloan 

48. “One cannot create a mosaic without the hard small marble bits known as ‘facts’ or ‘data’; what matters, however, is not so much the individual bits as the sequential patterns into which you organize them, then break them up and reorganize them'” — Timothy Robinson, Physician Scientist 

49. “Data are just summaries of thousands of stories–tell a few of those stories to help make the data meaningful.” Chip and Dan Heath, Authors of Made to Stick and Switch

Parting thoughts on amazing data science quotes

Each quote by industry experts or experienced professionals provides us with insights to better understand the subject. Here are the final quotes for both aspiring and existing data scientists: 

50. “The self-taught, un-credentialed, data-passionate people—will come to play a significant role in many organizations’ data science initiatives.” – Neil Raden, Founder, and Principal Analyst, Hired Brains Research. 

51. “Data scientists are involved with gathering data, massaging it into a tractable form, making it tell its story, and presenting that story to others.” – Mike Loukides, Editor, O’Reilly Media. 

Have we missed any of your favorite quotes on data? Or do you have any thoughts on the data quotes shared above? Let us know in the comments.