#Data Modeling

Data modeling in Power BI refers to the process of organizing data into a structured format by connecting tables, defining relationships, and designing an efficient schema. The purpose of data modeling is to ensure that visuals, filters, and calculations operate accurately and consistently across the report. A well-designed data model improves performance, enables correct aggregations, and provides a reliable foundation for analysis. #powerbi #datamodeling #dataanalytics #businessintelligence

Day 1 of becoming a PRO Data Engineer starts NOW! Ever wondered why we even need data modeling? Let me show you with one simple kitchen example If your data looks like a messy drawer… your queries are gonna suffer 💥 Let’s fix that, one table at a time! 📊 ➡️ Follow for my daily learning journey 💬 Drop a “🔥” if you’ve ever worked with messy data 📌 Save this for your next data study session! #DataEngineering #NoobToPro #Day1 #DataModeling #LearnSQL #AzureDataEngineer #viralreels #switch #corporate #DataEngineerJourney #StudyWithMe #TechContent #ReelStudy #ViralLearning #ReelTips #RelatableContent

Comment PROJECT to access my step-by-step Python tutorial that anyone can follow to build your very first geospatial dashboard web app! 🌍📊 A good number of portfolio projects is 3–5, and the types of projects you choose should reflect the kind of data role you’re going after. A data analyst portfolio should look very different from a machine learning engineer one. Even within data science, a product/decision data scientist portfolio should focus on A/B testing and metrics storytelling—while an algorithm data scientist portfolio might highlight modeling and experimentation. ✨ Especially if you’re building your very first project, prioritize: 🌱 Real-world messiness (not polished Kaggle sets) 🌱 Business context and decision-making 🌱 Clear documentation (what you did and why) 🌱Visuals to help your work stand out No one’s asking for perfection—they want to see how you think. #datascienceportfolio #dataanalyst #learnpython #codingjourney #techcareers

Comment “project” for my full video that breaks each of these projects down in detail with examples from my own work. If you’re using the Titanic, Iris, or COVID-19 dataset for data analytics projects, STOP NOW! These are so boring and over used and scream “newbie”. You can find way more interesting datasets for FREE on public data sites and you can even make your own using ChatGPT or Claude! Here are the 3 types of projects you need: ↳Exploratory Data Analysis (EDA): Exploring a dataset to uncover insights through descriptive statistics (averages, ranges, distributions) and data visualization, including analyzing relationships between variables ↳Full Stack Data Analytics Project: An end-to-end project that covers the entire data pipeline: wrangling data from a database, cleaning and transforming it. It demonstrates proficiency across multiple tools, not just one. ↳Funnel Analysis: Tracking users or items move from point A to point B, and how many make it through each step in between. This demonstrates a deeper level of business thinking by analyzing the process from beginning to end and providing actionable recommendations to improve it Save this video for later + send to a data friend!

The best projects serve a real use case Comment “data” for all the links and project descriptions #tech #data #datascience #ml #explore

comment statistics to get the link #datascience #machinelearning #womeninstem #learningtogether

In my first years as a data scientist, I wasted hours on broken SQL, slow pandas scripts, messy Flask deployments, and “works on my machine” chaos. These 4 tools fixed that: • dbt → modular, documented SQL transformations • Polars → faster, cleaner alternative to pandas • FastAPI → quick, reliable model deployment • Docker → consistent environments, no more deployment nightmares If you’re just starting out, learning these early will save you months of frustration.

Using real world data is always best for when building and learning because data is THE most important part of your model!!!! #data

Data Analytics Road map (6-9 months) https://drive.google.com/drive/folders/17KOCp6F1JGqOCwIdryzcDykNCSu93Ltc?usp=sharing Built from my personal interview experiences(Interviews given - 5+) Duration - 1-2 Months - Basics Learn basic - intermediate SQL(joins) from youtube/udemy Basic Python from youtube/udemy/Leetcode Basic Excel Duration 2-3 Months - Intermediate Practice intermediate to advanced SQL on Data Lemur/Leetcode/WiseOwl Practice easy-intermediate python questions on Leetcode/Hackerrank Start BI - Power BI tutorial from youtube/udemy Duration 3-4 Months - Advanced Learn Pandas/pyspark, practice EDA on csv files from Kaggle datasets on jupyter notebook/colab Practice advanced SQL questions(window functions) Build BI projects from kaggle datasets/Datacamp Github profile to showcase your projects + LinkedIn Theoretical knowledge on ETL pipelines/ Data warehousing concepts(Chat GPT) Resources SQL - Theory - W3Schools(free)/Udemy(paid), Practice - Leetcode/Data Lemur Python - Theory - Youtube/Udemy, Practice - Leetcode(easy to medium) Data Warehousing+ETL - Tutorials Point/Udemy, Datacamp/Chat GPT Power BI/Tableau - Datacamp, wiseowl Pandas/Pyspark - Datacamp, Leetcode, Kaggle Basic Excel . . . . . . #big4 #fyp #data #analytics #ootd #grwm

1. Look at the big picture. 2. Get the data. 3. Explore and visualize the data to gain insights. 4. Prepare the data for machine learning algorithms. 5,. Select a model and train it. 6. Fine-tune your model. 7. Present your solution. 8. Launch, monitor, and maintain your systen. The Unbelievable Perks Exclusive Instagram Subscribers🔻 ➡️ Resume review & ATS Editable Resume Template ➡️ Priority replys ➡️ Exclusive QA ➡️ Job Postings ➡️ MIT + Stanford notes ➡️ Data Science Masterclass PDF Notes ⭐️ And many more just for Rs.45/month #datascience #machinelearning #python #ai #dataanalytics #artificialintelligence #deeplearning #bigdata #agenticai #aiagents #statistics #dataanalysis #datavisualization #analytics #datascientist #neuralnetworks #100daysofcode #genai #llms #datasciencebootcamp

Data modeling in Power BI is like building a “map” for your data so tables can talk to each other. With the right relationships (for example, using a Product Key), your visuals become more accurate, structured, and easier to analyze. When the data structure is solid, Power BI from Microsoft isn’t just about good-looking dashboards, it delivers real insights. #PowerBI #DataModeling #BusinessIntelligence

Data Analysis in Excel with AI!✨ With Copilot in Excel, you can quickly gather insights and analyse data with just a prompt. For example, here I used copilot to get the average salary by designation data based on the data that we have. Copilot quickly got back with an exact answer. This can save countless hours and help you get more out of excel. I’m organising an excel workshop where you can learn tricks like these and more from Excel expert @amtechinmaya on 15th and 16th of november. Comment “workshop” and I’ll send you the link to register. Hurry up, only 20 seats left. Could be gone any minute. Send this to a friend. And follow me for more Excel tips and tricks. #msexcel #microsoftexcel #dataanalysis #exceltips #msexceltips #msexceltraining #exceltricks #exceltraining #exceltutorial