Rockborne’s Post

🤦♂️ I spent 38 months learning in demand data science skills, like: * data mining * data viz * dashboard design/ dev * advanced analytics + more 🤖 And within only a few months, developments in GenAI have produced tools like Tableau Pulse and ChatGPT-4o that have democratized data analysis. 🤖 📉 Anyone can upload their own spreadsheets and get insights, with less technical skills and a lower barrier to entry. 📉 💪 And I love how that empowers people! Every one can now make data-informed decisions in their own lives. 💪 🎯 But professionally, it levels the playing field, and places data analysis and business intelligence work firmly in the crosshairs of disruption. 🎯 📚 To be better positioned for the future, I will be committing to studying Data Engineering and sharing my learning journey here for the next 180+ days. 📚 Let’s build 🛠️ #dataengineering #dataengineer #genai #data #tech

It comes with great joy that I could build such a masterpiece using Tableau! All thanks to Baraa Khatib Salkini for his mentorship. It was a very tedious project, but you held me by the hand all through. I appreciate! With such feat, I have gotten the needed confidence to tackle any project. I will continue to leverage on this knowledge to make the best of it in my Data Science journey. Key concepts: 1. Building container structure 2. Generating datasets using ChatGPT and Faker library 3. Concise interpretation of user requirements

Unlock the Power of Data with #SQL - Your Essential Cheat Sheet! 👉 𝐓𝐨𝐩𝐢𝐜𝐬 𝐂𝐨𝐯𝐞𝐫𝐞𝐝: 𝐀𝐠𝐠𝐫𝐞𝐠𝐚𝐭𝐢𝐨𝐧𝐬 𝐆𝐫𝐨𝐮𝐩𝐢𝐧𝐠 𝐃𝐚𝐭𝐚: Learn to group your data efficiently to identify patterns and insights. 𝐆𝐫𝐨𝐮𝐩𝐢𝐧𝐠 𝐒𝐞𝐭𝐬: Understand how to use grouping sets for advanced data aggregation. 𝐀𝐠𝐠𝐫𝐞𝐠𝐚𝐭𝐢𝐨𝐧 𝐅𝐮𝐧𝐜𝐭𝐢𝐨𝐧𝐬: Utilize functions like SUM, AVG, and COUNT to perform calculations on your data. 𝐅𝐢𝐥𝐭𝐞𝐫𝐢𝐧𝐠: Filter your data to focus on specific subsets and refine your analysis. 𝐈𝐦𝐩𝐨𝐫𝐭𝐚𝐧𝐜𝐞 𝐨𝐟 𝐋𝐞𝐚𝐫𝐧𝐢𝐧𝐠: Mastering SQL is crucial for efficiently retrieving and analyzing data from databases. It allows you to group and filter data, perform aggregations, and extract valuable insights, making it an essential skill in data-driven fields. 📌 Follow 1stepGrow Academy for more insights on Data Science & Ai.  📍 Ready to transform your career in data science, machine learning, and AI at 👉 https://lnkd.in/dZte5GdH! #1stepGrow #DataAnalysis #dataAnalyst #DataScience #DatabaseManagement #TechSkills #CareerDevelopment

Unlock the Power of Data with #SQL - Your Essential Cheat Sheet! 👉 𝐓𝐨𝐩𝐢𝐜𝐬 𝐂𝐨𝐯𝐞𝐫𝐞𝐝: 𝐀𝐠𝐠𝐫𝐞𝐠𝐚𝐭𝐢𝐨𝐧𝐬 𝐆𝐫𝐨𝐮𝐩𝐢𝐧𝐠 𝐃𝐚𝐭𝐚: Learn to group your data efficiently to identify patterns and insights. 𝐆𝐫𝐨𝐮𝐩𝐢𝐧𝐠 𝐒𝐞𝐭𝐬: Understand how to use grouping sets for advanced data aggregation. 𝐀𝐠𝐠𝐫𝐞𝐠𝐚𝐭𝐢𝐨𝐧 𝐅𝐮𝐧𝐜𝐭𝐢𝐨𝐧𝐬: Utilize functions like SUM, AVG, and COUNT to perform calculations on your data. 𝐅𝐢𝐥𝐭𝐞𝐫𝐢𝐧𝐠: Filter your data to focus on specific subsets and refine your analysis. 𝐈𝐦𝐩𝐨𝐫𝐭𝐚𝐧𝐜𝐞 𝐨𝐟 𝐋𝐞𝐚𝐫𝐧𝐢𝐧𝐠: Mastering SQL is crucial for efficiently retrieving and analyzing data from databases. It allows you to group and filter data, perform aggregations, and extract valuable insights, making it an essential skill in data-driven fields. 📌 Follow 1stepGrow Academy for more insights on Data Science & Ai.  📍 Ready to transform your career in data science, machine learning, and AI at 👉 https://lnkd.in/dZte5GdH! #1stepGrow #DataAnalysis #dataAnalyst #DataScience #DatabaseManagement #TechSkills #CareerDevelopment

Day 0️⃣ 2️⃣ of #100daysofdataengineering Challenge! 🚀 Today's mission is to develop a solid understanding of Structured Query Language (SQL). I found the following resources helpful. 🎓 https://lnkd.in/dApG9wGW (Free course with projects and quizzes) 📺 https://lnkd.in/dPkvr7Mm (Tutorial with practical approach) Stay tuned for updates, insights, and learnings as I dive into the world of data engineering. If you're interested, join me on this exciting journey! #DataEngineering #100DaysOfDataEngineering #LearningJourney #ContinuousImprovement #Day02 #SQL

What is a READ ME File?? ➕ A read-me file is a text or mark down file that introduces and explains our work in a more crystal clear manner by guiding and navigating users through the project of work. ➕ It should contain information that is necessary for the users who are referring and going through our piece of work and have a clear understanding of it with detailed description. ➕ In data analysis it is a good practice to create "READ ME" Files, when and where required, as we share our work of analyzing data to management of organization and also other stake holders of the business. ➕ These "READ ME" Files can make a detailed explanation to people, whenever they seek clarity regarding the steps followed & practices adopted in commencement of work. If they do not understand and stuck at a point these files helps to solve the problem and clear the doubts. ➕ The scope and contents of a "README" file can vary basing on the type of work, contents of data and development team involved in it. ➕ It is always preferred to write our "README" document as a plain text file and avoiding proprietary formats such as "MS WORD". A paragraph with a full information of explanation is better than other big lengthy formats. ➕ A good "README" file typically consists of Title& description, table of contents usage, contribution, Licenses, Badges and additional sections referencing the analytical work that we have performed in organization. ➕ The "README" file should be updated always whenever the content of the work is updated the changes made are should be appealing by using headers, bullet points, and images when ever required. Use clear language by avoiding jargon and complex words. ➕ By inculcating "README" files in our work for every task given by the authorities of organization, we can be effectively communicating our observations to the listeners of data. ➕ Practices like these can be a game-changer in our day-to-day activities and helps to stand out in the crowd. Thanks, Alekhya Jonnalagadda. #data #dataanalysis #datascientist #businessanalyst #dataengineer #AI #ML #GenAI

10 Commandments of Data Science 1. Solve problems, not just work with tools. Remember, 80% of your time goes to data gathering and cleaning. 2. Excel and SQL are gold mines. Don't overlook their power in data analysis. 3. Stick to simple models like Linear Regression. Not every problem needs complex neural networks. 4. Textbook solutions won't cut it always. Be ready to innovate and explore new approaches. 5. Tools like ChatGPT and Stack Overflow are your best friends. Nobody can remember everything! 6. Master Data Visualization. Simplify key insights for non-technical stakeholders. 7. Storytelling matters. Learn PowerPoint to showcase your work effectively. 8. Keep learning. Data Science evolves fast. Stay updated to stay relevant. 9. Remember, the focus should always be on problem-solving. Tools and models come second. #datascience #MLdeveloper

🚀 Revolutionize Your Data Exploration with AI2sql! 🚀 Are you tired of complex SQL queries slowing down your data analysis? Say hello to AI2sql.io - your new secret weapon for effortless data exploration! 🔍 What is AI2sql? AI2sql is an innovative platform that simplifies data queries using cutting-edge AI technology. Our intuitive dashboard empowers both SQL novices and experts to elevate their data game. ✨ Key Features: • Text to SQL: Generate queries from natural language • SQL Optimization: Enhance query performance • Query Explanation: Understand complex SQL effortlessly • Data Insights: Uncover hidden patterns in your data • SQL Formatting & Validation: Ensure clean, error-free code 💼 Perfect for: • Data Analysts • Business Intelligence Professionals • Software Developers • Anyone working with databases! Don't let data complexity hold you back. Discover how AI2sql can transform your workflow and unlock the full potential of your data. Curious to learn more? Check out our website ai2sql.io or drop a comment below! #DataScience #AI #SQL #BusinessIntelligence #Database

Many are in the data industry. 🤐 Many will enter now or then; many are preparing for transition. Among all these million of faces,a great data scientist is the one who work on mind training. Working amidst the Pressure of driving great insights, finding best patters, giving best accuracy, making data speak in best way. Keeping pace with growing technologies. Motivating and guiding the new commers. Doing their best even when they are going through a heartbreak. Data Science is not about coding or tool. Because tools and coding can be learnt. Data Science also involves mental game. No one sees it. Only you. It’s the victory, the control of the mind, the joy of deciding what to focus on that makes you a great data scientist. Remember, inner world drives the external world always. #DataScience #MindTraining #DataMastery #MachineLearning #ArtificialIntelligence #DataAnalytics #BigData #DataDriven #DataMining #PredictiveAnalytics #AI #DataScientists #DeepLearning #Statistics #BusinessIntelligence #DataVisualization #DataEngineering #PythonProgramming #RProgramming #DataInsights #Analytics #QuantitativeAnalysis

Become a Data Analyst in 2 months😯 Learn AI in 3 months🫨 Advance Excel in 1 month😲 Data Science in 3 months.. 🌟 Don't fall for quick-fix promises! 🌟 Ever seen ads promising to turn you into a Data God in just a few months? 🤔 Imagine if it were that easy, we'd all be ruling the data universe by now! Instead of chasing shortcuts, I believe in building a strong foundation. Learning takes time and effort, but it's worth every moment. Just like buildings constructed with care and precision, solid knowledge stands the test of time. Let's embrace the journey of learning, step by step. It's not about how fast we go, but how well we grasp the fundamentals. That's what sets us up for lasting success in the dynamic world of data. #DataScience #LearningJourney #BuildingFoundations #PatiencePaysOff