𝟱 𝗠𝘂𝘀𝘁-𝗙𝗼𝗹𝗹𝗼𝘄 𝗬𝗼𝘂𝗧𝘂𝗯𝗲 𝗖𝗵𝗮𝗻𝗻𝗲𝗹𝘀 𝗳𝗼𝗿 𝗔𝘀𝗽𝗶𝗿𝗶𝗻𝗴 𝗗𝗮𝘁𝗮 𝗦𝗰𝗶𝗲𝗻𝘁𝗶𝘀𝘁𝘀 𝗶𝗻 𝟮𝟬𝟮𝟱😍

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Kavitha's Journey to become a Data Engineer 👇👇

1. Startup to Dream Job Journey:
- Started at a startup in India, transitioned to Infosys, then grabbed UK opportunity.
- Shifted from legacy Mainframe to AWS Cloud, pursued Master's from illinoisstateu, and secured dream job at Statefarm.
2. Learn Fundamentals:
- Assess skills, understand role.
- Gain proficiency in Python, SQL.
- Learn data technologies.
3. Database and Modeling Skills:
- Understand databases, gain proficiency.
- Learn data modeling principles.
4. Master ETL, Warehousing, and Visualization:
- Understand ETL, data warehousing.
- Gain experience in building warehouses.
- Familiarize with visualization tools.
- Got Certified as AWS Solutions Architect.
5. Utilize LinkedIn for Job Search:
- Network and connect with professionals.
- Showcase skills and achievements.
- Utilize job search feature, leading to dream job at Statefarm.

Data Engineering Interview Preparation Resources: https://whatsapp.com/channel/0029Vaovs0ZKbYMKXvKRYi3C

🎓 𝗟𝗲𝗮𝗿𝗻 𝗖𝗼𝗺𝗽𝘂𝘁𝗲𝗿 𝗦𝗰𝗶𝗲𝗻𝗰𝗲 𝗳𝗼𝗿 𝗙𝗿𝗲𝗲 𝗳𝗿𝗼𝗺 𝗛𝗮𝗿𝘃𝗮𝗿𝗱, 𝗦𝘁𝗮𝗻𝗳𝗼𝗿𝗱, 𝗠𝗜𝗧 & 𝗚𝗼𝗼𝗴𝗹𝗲😍

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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 ❤️💪

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✨️📌

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Enjoy Learning ✅️

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

𝗦𝗤𝗟 𝟭𝟬𝟬% 𝗙𝗥𝗘𝗘 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗖𝗼𝘂𝗿𝘀𝗲𝘀 😍

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𝟱 𝗙𝗿𝗲𝗲 𝗠𝗜𝗧 𝗖𝗼𝘂𝗿𝘀𝗲𝘀 𝗬𝗼𝘂 𝗖𝗮𝗻 𝗧𝗮𝗸𝗲 𝗢𝗻𝗹𝗶𝗻𝗲 𝗶𝗻 𝟮𝟬𝟮𝟱😍

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𝗠𝗮𝘀𝘁𝗲𝗿 𝗣𝗿𝗼𝗺𝗽𝘁 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴 𝗳𝗼𝗿 𝗙𝗿𝗲𝗲 𝗶𝗻 𝟮𝟬𝟮𝟱!😍

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Your gateway to a smarter career✅️

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 𝗣𝗼𝘄𝗲𝗿𝗳𝘂𝗹 𝗚𝗶𝘁𝗛𝘂𝗯 𝗥𝗲𝗽𝗼𝘀𝗶𝘁𝗼𝗿𝗶𝗲𝘀 𝘁𝗼 𝗠𝗮𝘀𝘁𝗲𝗿 𝗣𝘆𝘁𝗵𝗼𝗻 𝗳𝗼𝗿 𝗙𝗿𝗲𝗲😍

Looking to Master Python for Free?✨️

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𝐋𝐢𝐧𝐤👇:-

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📌 Save this post & share it with a Python learner!

𝟲 𝗙𝗥𝗘𝗘 𝗢𝗻𝗹𝗶𝗻𝗲 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻𝘀 𝗧𝗼 𝗖𝗵𝗮𝗻𝗴𝗲 𝗬𝗼𝘂𝗿 𝗖𝗮𝗿𝗲𝗲𝗿 𝗜𝗻 𝟮𝟬𝟮𝟱 😍

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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 👍👍

𝐈𝐁𝐌 𝐅𝐑𝐄𝐄 𝐂𝐞𝐫𝐭𝐢𝐟𝐢𝐜𝐚𝐭𝐢𝐨𝐧 𝐂𝐨𝐮𝐫𝐬𝐞𝐬😍

🚀 Dive into the world of Data Analytics with these 6 free courses by IBM!

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𝐋𝐢𝐧𝐤 👇:- 
 
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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.

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𝟭𝟬𝟬𝟬+ 𝗙𝗿𝗲𝗲 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗲𝗱 𝗖𝗼𝘂𝗿𝘀𝗲𝘀 𝗯𝘆 𝗜𝗻𝗳𝗼𝘀𝘆𝘀 – 𝗟𝗲𝗮𝗿𝗻, 𝗚𝗿𝗼𝘄, 𝗦𝘂𝗰𝗰𝗲𝗲𝗱!😍

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