Data Engineers

𝗙𝗿𝗲𝗲 𝗣𝘆𝘁𝗵𝗼𝗻 𝗖𝗼𝘂𝗿𝘀𝗲: 𝗧𝗵𝗲 𝗕𝗲𝘀𝘁 𝗦𝘁𝗮𝗿𝘁𝗶𝗻𝗴 𝗣𝗼𝗶𝗻𝘁 𝗳𝗼𝗿 𝗧𝗲𝗰𝗵 & 𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀 𝗕𝗲𝗴𝗶𝗻𝗻𝗲𝗿𝘀😍 🚀 Want to break into tech or data analytics but don’t know how to start?📌✨️ Python is the #1 most in-demand programming language, and Scaler’s free Python for Beginners course is a game-changer for absolute beginners📊✔️ 𝐋𝐢𝐧𝐤👇:- https://pdlink.in/45TroYX No coding background needed!✅️

Planning for Data Science or Data Engineering Interview. Focus on SQL & Python first. Here are some important questions which you should know. 𝐈𝐦𝐩𝐨𝐫𝐭𝐚𝐧𝐭 𝐒𝐐𝐋 𝐪𝐮𝐞𝐬𝐭𝐢𝐨𝐧𝐬 1- Find out nth Order/Salary from the tables. 2- Find the no of output records in each join from given Table 1 & Table 2 3- YOY,MOM Growth related questions. 4- Find out Employee ,Manager Hierarchy (Self join related question) or Employees who are earning more than managers. 5- RANK,DENSERANK related questions 6- Some row level scanning medium to complex questions using CTE or recursive CTE, like (Missing no /Missing Item from the list etc.) 7- No of matches played by every team or Source to Destination flight combination using CROSS JOIN. 8-Use window functions to perform advanced analytical tasks, such as calculating moving averages or detecting outliers. 9- Implement logic to handle hierarchical data, such as finding all descendants of a given node in a tree structure. 10-Identify and remove duplicate records from a table. 𝐈𝐦𝐩𝐨𝐫𝐭𝐚𝐧𝐭 𝐏𝐲𝐭𝐡𝐨𝐧 𝐪𝐮𝐞𝐬𝐭𝐢𝐨𝐧𝐬 1- Reversing a String using an Extended Slicing techniques. 2- Count Vowels from Given words . 3- Find the highest occurrences of each word from string and sort them in order. 4- Remove Duplicates from List. 5-Sort a List without using Sort keyword. 6-Find the pair of numbers in this list whose sum is n no. 7-Find the max and min no in the list without using inbuilt functions. 8-Calculate the Intersection of Two Lists without using Built-in Functions 9-Write Python code to make API requests to a public API (e.g., weather API) and process the JSON response. 10-Implement a function to fetch data from a database table, perform data manipulation, and update the database. Join for more: https://whatsapp.com/channel/0029Va8v3eo1NCrQfGMseL2D ENJOY LEARNING 👍👍

𝟭𝟬𝟬𝟬+ 𝗙𝗿𝗲𝗲 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗲𝗱 𝗖𝗼𝘂𝗿𝘀𝗲𝘀 𝗯𝘆 𝗜𝗻𝗳𝗼𝘀𝘆𝘀 – 𝗟𝗲𝗮𝗿𝗻, 𝗚𝗿𝗼𝘄, 𝗦𝘂𝗰𝗰𝗲𝗲𝗱!😍 🚀 Looking to upgrade your skills without spending a rupee?💰 Here’s your golden opportunity to unlock 1,000+ certified online courses across technology, business, communication, leadership, soft skills, and much more — all absolutely FREE on Infosys Springboard!🔥 𝐋𝐢𝐧𝐤👇:- https://pdlink.in/43UcmQ7 Save this blog, sign up, and start your upskilling journey today!✅️

𝟰 𝗛𝗶𝗴𝗵-𝗜𝗺𝗽𝗮𝗰𝘁 𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻𝘀 𝘁𝗼 𝗟𝗮𝘂𝗻𝗰𝗵 𝗬𝗼𝘂𝗿 𝗖𝗮𝗿𝗲𝗲𝗿 𝗶𝗻 𝟮𝟬𝟮𝟱😍 These globally recognized certifications from platforms like Google, IBM, Microsoft, and DataCamp are beginner-friendly, industry-aligned, and designed to make you job-ready in just a few weeks 𝐋𝐢𝐧𝐤👇:- https://pdlink.in/4kC18XE These courses help you gain hands-on experience — exactly what top MNCs look for!✅️

Machine Learning Algorithms every data scientist should know: 📌 Supervised Learning: 🔹 Regression ∟ Linear Regression ∟ Ridge & Lasso Regression ∟ Polynomial Regression 🔹 Classification ∟ Logistic Regression ∟ K-Nearest Neighbors (KNN) ∟ Decision Tree ∟ Random Forest ∟ Support Vector Machine (SVM) ∟ Naive Bayes ∟ Gradient Boosting (XGBoost, LightGBM, CatBoost) 📌 Unsupervised Learning: 🔹 Clustering ∟ K-Means ∟ Hierarchical Clustering ∟ DBSCAN 🔹 Dimensionality Reduction ∟ PCA (Principal Component Analysis) ∟ t-SNE ∟ LDA (Linear Discriminant Analysis) 📌 Reinforcement Learning (Basics): ∟ Q-Learning ∟ Deep Q Network (DQN) 📌 Ensemble Techniques: ∟ Bagging (Random Forest) ∟ Boosting (XGBoost, AdaBoost, Gradient Boosting) ∟ Stacking Don’t forget to learn model evaluation metrics: accuracy, precision, recall, F1-score, AUC-ROC, confusion matrix, etc. Free Machine Learning Resources: https://whatsapp.com/channel/0029Va8v3eo1NCrQfGMseL2D React ❤️ for more free resources

𝐈𝐁𝐌 𝐅𝐑𝐄𝐄 𝐂𝐞𝐫𝐭𝐢𝐟𝐢𝐜𝐚𝐭𝐢𝐨𝐧 𝐂𝐨𝐮𝐫𝐬𝐞𝐬😍 🚀 Dive into the world of Data Analytics with these 6 free courses by IBM! Gain practical knowledge and stand out in your career with tools designed for real-world applications. All courses come with expert guidance and are free to access!🎉 𝐋𝐢𝐧𝐤 👇:-    https://bit.ly/4iXOmmb   Enroll For FREE & Get Certified 🎓

Amazon Interview Process for Data Scientist position 📍Round 1- Phone Screen round This was a preliminary round to check my capability, projects to coding, Stats, ML, etc. After clearing this round the technical Interview rounds started. There were 5-6 rounds (Multiple rounds in one day). 📍 𝗥𝗼𝘂𝗻𝗱 𝟮- 𝗗𝗮𝘁𝗮 𝗦𝗰𝗶𝗲𝗻𝗰𝗲 𝗕𝗿𝗲𝗮𝗱𝘁𝗵: In this round the interviewer tested my knowledge on different kinds of topics. 📍𝗥𝗼𝘂𝗻𝗱 𝟯- 𝗗𝗲𝗽𝘁𝗵 𝗥𝗼𝘂𝗻𝗱: In this round the interviewers grilled deeper into 1-2 topics. I was asked questions around: Standard ML tech, Linear Equation, Techniques, etc. 📍𝗥𝗼𝘂𝗻𝗱 𝟰- 𝗖𝗼𝗱𝗶𝗻𝗴 𝗥𝗼𝘂𝗻𝗱- This was a Python coding round, which I cleared successfully. 📍𝗥𝗼𝘂𝗻𝗱 𝟱- This was 𝗛𝗶𝗿𝗶𝗻𝗴 𝗠𝗮𝗻𝗮𝗴𝗲𝗿 where my fitment for the team got assessed. 📍𝗟𝗮𝘀𝘁 𝗥𝗼𝘂𝗻𝗱- 𝗕𝗮𝗿 𝗥𝗮𝗶𝘀𝗲𝗿- Very important round, I was asked heavily around Leadership principles & Employee dignity questions. So, here are my Tips if you’re targeting any Data Science role: -> Never make up stuff & don’t lie in your Resume. -> Projects thoroughly study. -> Practice SQL, DSA, Coding problem on Leetcode/Hackerank. -> Download data from Kaggle & build EDA (Data manipulation questions are asked) Best Data Science & Machine Learning Resources: https://topmate.io/coding/914624 ENJOY LEARNING 👍👍

𝗟𝗲𝗮𝗿𝗻 𝗗𝗮𝘁𝗮 𝗦𝗰𝗶𝗲𝗻𝗰𝗲 𝗳𝗼𝗿 𝗙𝗥𝗘𝗘 𝘄𝗶𝘁𝗵 𝗛𝗮𝗿𝘃𝗮𝗿𝗱 𝗨𝗻𝗶𝘃𝗲𝗿𝘀𝗶𝘁𝘆😍 🎯 Want to break into Data Science without spending a single rupee?💰 Harvard University is offering a goldmine of free courses that make top-tier education accessible to anyone, anywhere👨‍💻✨️ 𝐋𝐢𝐧𝐤👇:- https://pdlink.in/3HxOgTW These courses are designed by Ivy League experts and are trusted by thousands globally✅️

𝟲 𝗙𝗥𝗘𝗘 𝗢𝗻𝗹𝗶𝗻𝗲 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻𝘀 𝗧𝗼 𝗖𝗵𝗮𝗻𝗴𝗲 𝗬𝗼𝘂𝗿 𝗖𝗮𝗿𝗲𝗲𝗿 𝗜𝗻 𝟮𝟬𝟮𝟱 😍 🎯 Want to switch careers or upgrade your skills — without spending a single rupee? Check out 6 handpicked, beginner-friendly courses in high-demand fields like Data Science, Web Development, Digital Marketing, Project Management, and more. 🚀 𝐋𝐢𝐧𝐤👇:- https://pdlink.in/4e1I17a 💥 Start learning today and build the skills top companies want!✅️

𝟱 𝗣𝗼𝘄𝗲𝗿𝗳𝘂𝗹 𝗚𝗶𝘁𝗛𝘂𝗯 𝗥𝗲𝗽𝗼𝘀𝗶𝘁𝗼𝗿𝗶𝗲𝘀 𝘁𝗼 𝗠𝗮𝘀𝘁𝗲𝗿 𝗣𝘆𝘁𝗵𝗼𝗻 𝗳𝗼𝗿 𝗙𝗿𝗲𝗲😍 Looking to Master Python for Free?✨️ These 5 GitHub repositories are all you need to level up — from beginner to advanced! 💻 𝐋𝐢𝐧𝐤👇:- https://pdlink.in/3FG7DcW 📌 Save this post & share it with a Python learner!

Tips to become a Data Engineer 👇👇 1. Data Engineering Basics: At its core, it's about efficiently moving and reshaping data from one place/format to another. 2. Be Curious: The field is vast. Dive deep, ask questions, and always be in the mode of learning and experimenting. 3. Master Data: Understand the intricacies of data types, where they originate, and how they're structured. 4. Programming: Grasping a language is crucial. If you're unsure, start with Python – it's versatile and widely used in the industry. 5. SQL: A timeless tool for querying databases. Mastering SQL will empower you to work with data across various platforms. 6. Command Line: Familiarizing yourself with command line operations can save a lot of time, especially for quick and repetitive tasks. 7. Know Computers: A basic understanding of how computers communicate and process information can guide better data engineering decisions. 8. Personal Projects: Practical experience is invaluable. Start projects, learn from them, and showcase your work on platforms like GitHub. 9. APIs and JSON: Many modern data sources are API-based. Understanding how to extract and manipulate JSON data will be a daily task. 10. Tools Mastery: Get proficient with your primary tools, but stay updated with emerging technologies and platforms. 11. Data Storage Basics: Know the difference and use-cases for Databases, Data Lakes, and Data Warehouses. Understand the distinction between OLTP (online transaction processing) and OLAP (online analytical processing). 12. Cloud Platforms: The cloud is the future. AWS, Azure, and GCP offer free tiers to start experimenting. 13. Business Acumen: A data engineer who understands business metrics and their implications can offer more value. 14. Data Grain: Dive deep into datasets to understand their finest level of detail. It aids in more precise querying and analytics. 15. Data Formats: Recognizing main data formats (like JSON, XML, CSV, SQLite, Database) will help you navigate different datasets with ease.

𝟱 𝗙𝗥𝗘𝗘 𝗠𝗜𝗧 𝗖𝗼𝘂𝗿𝘀𝗲𝘀 𝘁𝗼 𝗟𝗲𝗮𝗿𝗻 𝗧𝗲𝗰𝗵, 𝗔𝗜 & 𝗗𝗮𝘁𝗮 𝗦𝗰𝗶𝗲𝗻𝗰𝗲😍 Dreaming of an MIT education without the tuition fees? 🎯 These 5 FREE courses from MIT will help you master the fundamentals of programming, AI, machine learning, and data science—all from the comfort of your home! 🌐✨ 𝐋𝐢𝐧𝐤👇:- https://pdlink.in/45cvR95 Your gateway to a smarter career✅️

𝗠𝗮𝘀𝘁𝗲𝗿 𝗣𝗿𝗼𝗺𝗽𝘁 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴 𝗳𝗼𝗿 𝗙𝗿𝗲𝗲 𝗶𝗻 𝟮𝟬𝟮𝟱!😍 Want to communicate with AI like a pro? 🤖 Whether you’re a data analyst, AI developer, content creator, or student, this is the must-have skill of 2025✨️ 𝐋𝐢𝐧𝐤👇:- https://pdlink.in/456lMuf Save this now & unlock your AI potential!⚡

𝟱 𝗙𝗿𝗲𝗲 𝗠𝗜𝗧 𝗖𝗼𝘂𝗿𝘀𝗲𝘀 𝗬𝗼𝘂 𝗖𝗮𝗻 𝗧𝗮𝗸𝗲 𝗢𝗻𝗹𝗶𝗻𝗲 𝗶𝗻 𝟮𝟬𝟮𝟱😍 🎓No MIT Admission? No Problem — Learn from MIT for Free!🔥 MIT is known for world-class education—but you don’t need to walk its halls to access its knowledge📚📌 𝐋𝐢𝐧𝐤👇:- https://pdlink.in/4jBNtP2 These courses offer industry-relevant skills & completion certificates at no cost✅️

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𝗦𝗤𝗟 𝟭𝟬𝟬% 𝗙𝗥𝗘𝗘 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗖𝗼𝘂𝗿𝘀𝗲𝘀 😍 Looking to master SQL for Data Analytics or prep for your dream tech job? 💼 These 3 Free SQL resources will help you go from beginner to job-ready—without spending a single rupee! 📊✨ 𝐋𝐢𝐧𝐤👇:- https://pdlink.in/3TcvfsA 💥 Start learning today and build the skills top companies want!✅️

ETL vs ELT – Explained Using Apple Juice analogy! 🍎🧃 We often hear about ETL and ELT in the data world — but how do they actually apply in tools like Excel and Power BI? Let’s break it down with a simple and relatable analogy 👇 ✅ ETL (Extract → Transform → Load) 🧃 First you make the juice, then you deliver it ➡️ Apples → Juice → Truck 🔹 In Power BI / Excel: You clean and transform the data in Power Query Then load the final data into your report or sheet 💡 That’s ETL – transformation happens before loading ✅ ELT (Extract → Load → Transform) 🍏 First you deliver the apples, and make juice later ➡️ Apples → Truck → Juice 🔹 In Power BI / Excel: You load raw data into your model or sheet Then transform it using DAX, formulas, or pivot tables 💡 That’s ELT – transformation happens after loading

𝗟𝗲𝗮𝗿𝗻 𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀 𝗳𝗼𝗿 𝗙𝗿𝗲𝗲 𝗼𝗻 𝗬𝗼𝘂𝗧𝘂𝗯𝗲 – 𝗖𝗼𝗺𝗽𝗹𝗲𝘁𝗲 𝗣𝗹𝗮𝘆𝗹𝗶𝘀𝘁 𝗚𝘂𝗶𝗱𝗲😍 🎥 YouTube is the ultimate free classroom—and this is your Data Analytics syllabus in one post!👨‍💻 From Python and SQL to Power BI, Machine Learning, and Data Science, these carefully curated playlists will take you from complete beginner to job-ready✨️📌 𝐋𝐢𝐧𝐤👇:- https://pdlink.in/4jzVggc Enjoy Learning ✅️

Python Detailed Roadmap 🚀 📌 1. Basics ◼ Data Types & Variables ◼ Operators & Expressions ◼ Control Flow (if, loops) 📌 2. Functions & Modules ◼ Defining Functions ◼ Lambda Functions ◼ Importing & Creating Modules 📌 3. File Handling ◼ Reading & Writing Files ◼ Working with CSV & JSON 📌 4. Object-Oriented Programming (OOP) ◼ Classes & Objects ◼ Inheritance & Polymorphism ◼ Encapsulation 📌 5. Exception Handling ◼ Try-Except Blocks ◼ Custom Exceptions 📌 6. Advanced Python Concepts ◼ List & Dictionary Comprehensions ◼ Generators & Iterators ◼ Decorators 📌 7. Essential Libraries ◼ NumPy (Arrays & Computations) ◼ Pandas (Data Analysis) ◼ Matplotlib & Seaborn (Visualization) 📌 8. Web Development & APIs ◼ Web Scraping (BeautifulSoup, Scrapy) ◼ API Integration (Requests) ◼ Flask & Django (Backend Development) 📌 9. Automation & Scripting ◼ Automating Tasks with Python ◼ Working with Selenium & PyAutoGUI 📌 10. Data Science & Machine Learning ◼ Data Cleaning & Preprocessing ◼ Scikit-Learn (ML Algorithms) ◼ TensorFlow & PyTorch (Deep Learning) 📌 11. Projects ◼ Build Real-World Applications ◼ Showcase on GitHub 📌 12. ✅ Apply for Jobs ◼ Strengthen Resume & Portfolio ◼ Prepare for Technical Interviews Like for more ❤️💪