#Master Data

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#Master Data Reel by @askdatadawn (verified account) - FREE Data Analytics learning resources. Seriously, start here before paying for any courses. These are FREE & a great introduction for any skill you
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@askdatadawn
FREE Data Analytics learning resources. Seriously, start here before paying for any courses. These are FREE & a great introduction for any skill you want to learn. - SQL: https://www.youtube.com/watch?v=7S_tz1z_5bA - Excel: https://www.youtube.com/watch?v=pCJ15nGFgVg - Tableau: https://www.youtube.com/watch?v=aHaOIvR00So - Python: https://www.youtube.com/watch?v=LHBE6Q9XlzI #dataanalytics #dataanalyst #datascience #womenintech #aiengineering #techcareers
#Master Data Reel by @marytheanalyst - I won't be mad if you copy this entire roadmap… #dataanalyst #dataanalysis #dataanalytics #data #analyst #techjobs #breakintotech #wfh #workfromhome
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@marytheanalyst
I won’t be mad if you copy this entire roadmap… #dataanalyst #dataanalysis #dataanalytics #data #analyst #techjobs #breakintotech #wfh #workfromhome #wfhjobs #remotejobs #remotework #excel #sql #tableau #python
#Master Data Reel by @woman.engineer (verified account) - 📍How to prepare for Data Scientist role in 2026 🚀 CORE SKILLS YOU MUST MASTER: Programming You must be fluent in: ● Python ● NumPy ● Pandas ● S
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@woman.engineer
📍How to prepare for Data Scientist role in 2026 🚀 CORE SKILLS YOU MUST MASTER: Programming You must be fluent in: ● Python ● NumPy ● Pandas ● Scikit-learn Writing clean, readable, bug free code Data transformations without IDE help Expect: ● Data cleaning ● Feature extraction ● Aggregations ● Writing logic heavy code SQL Almost every Data Science role tests SQL. You should be comfortable with: ● Joins - inner, left, self ● Window functions ● Grouping & aggregations ● Subqueries ● Handling NULLs Statistics & Probability: ● Probability distributions ● Hypothesis testing ● Confidence intervals ● A/B testing ● Correlation vs causation ● Sampling bias Machine Learning Fundamentals. You must know: ● Supervised vs Unsupervised learning ● Regression & Classification ● Bias Variance tradeoff ● Overfitting / Underfitting Evaluation metrics: ● Accuracy ● Precision / Recall ● F1-score ● ROC-AUC ● RMSE FEATURE ENGINEERING & DATA UNDERSTANDING: ● This is where strong candidates stand out. ● Handling missing data ● Encoding categorical variables ● Feature scaling ● Outlier treatment CORE SKILLS YOU MUST MASTER: Programming You must be fluent in: ● Python ● NumPy ● Pandas ● Scikit-learn Writing clean, readable, bug free code Data transformations without IDE help Expect: ● Data cleaning ● Feature extraction ● Aggregations ● Writing logic heavy code SQL Almost every Data Science role tests SQL. You should be comfortable with: ● Joins - inner, left, self ● Window functions ● Grouping & aggregations ● Subqueries ● Handling NULLs Statistics & Probability: ● Probability distributions ● Hypothesis testing ● Confidence intervals ● A/B testing ● Correlation vs causation ● Sampling bias Machine Learning Fundamentals. You must know: ● Supervised vs Unsupervised learning ● Regression & Classification ● Bias Variance tradeoff ● Overfitting / Underfitting Evaluation metrics: ● Accuracy ● Precision / Recall ● F1-score ● ROC-AUC ● RMSE +++ for more look at the comment #datascientist #aiengineer #softwareengineer #datascience #dataengineer
#Master Data Reel by @chrisoh.zip - The best projects serve a real use case Comment "data" for all the links and project descriptions #tech #data #datascience #ml #explore
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@chrisoh.zip
The best projects serve a real use case Comment “data” for all the links and project descriptions #tech #data #datascience #ml #explore
#Master Data Reel by @rebellionrider - 🚀 2-Month Roadmap to Data Analyst Mastery 📊 👉 SAVE THIS to become data Pro! 👈 Otherwise, you'll miss out on learning how to become an SQL Expert!
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@rebellionrider
🚀 2-Month Roadmap to Data Analyst Mastery 📊 👉 SAVE THIS to become data Pro! 👈 Otherwise, you’ll miss out on learning how to become an SQL Expert! Month 1: Week 1-2: 📚 Foundation Building: • Master the basics of statistics, SQL, and Python/R through online courses and tutorials. Focus on understanding data structures and manipulation. Week 3-4: 🔍 Dive into Data Exploration: • Practice data analysis techniques using datasets from platforms like Kaggle. Learn to clean, preprocess, and visualize data to extract meaningful insights. Month 2: Week 1-2: 💼 Real-world Applications: • Engage in hands-on projects or internships to apply your skills to real business problems. Collaborate with peers and seek feedback to refine your approach. Week 3-4: 📈 Advanced Techniques: • Explore advanced topics such as machine learning algorithms, predictive modeling, and data storytelling. Experiment with different tools and techniques to enhance your analytical capabilities. 🎓 Congratulations! You’ve completed your 2-month journey to becoming a proficient data analyst. Remember to stay curious, keep learning, and embrace challenges as opportunities for growth. #DataAnalyst #CareerGrowth #DataSkills 🌟 #sqldatabase #sqlite #datascientist #datasciences #sqltraining #sqlinterview #dataanalystics #dataanalysis
#Master Data Reel by @onseventhsky (verified account) - Data Analytics Road map (6-9 months) https://drive.google.com/drive/folders/17KOCp6F1JGqOCwIdryzcDykNCSu93Ltc?usp=sharing Built from my personal int
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@onseventhsky
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
#Master Data Reel by @volkan.js (verified account) - Comment "DATA" for the links. You Will Never Struggle With Data Science Again 📌 Learn the most important foundations with these beginner-friendly r
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@volkan.js
Comment "DATA" for the links. You Will Never Struggle With Data Science Again 📌 Learn the most important foundations with these beginner-friendly resources: 1️⃣ Learn Python for Data Science – FreeCodeCamp’s full beginner course 2️⃣ Essence of Linear Algebra – 3Blue1Brown’s visual, intuitive playlist 3️⃣ Statistics – A Full Lecture (2025) – step-by-step breakdown of core stats concepts Stop feeling overwhelmed by Python, statistics, or linear algebra. These tutorials simplify the fundamentals of Data Science with clear explanations, visuals, and real-world examples. Whether you’re preparing for a career in Data Science, getting into machine learning, or just curious about data analysis, this is the fastest way to finally understand how it all fits together. Save this post, share it, and turn confusion into clarity with Python, Stats, and Linear Algebra for Data Science 📊
#Master Data Reel by @jayenthakker - If you're starting your Data Analyst journey or want to grow in your role, certifications can help you stand out. Here are 5 top certifications to co
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@jayenthakker
If you’re starting your Data Analyst journey or want to grow in your role, certifications can help you stand out. Here are 5 top certifications to consider: 1. Microsoft Certified: Power BI Data Analyst Associate - Master Power BI for creating impactful visuals. Link : https://learn.microsoft.com/en-us/credentials/certifications/data-analyst-associate/?practice-assessment-type=certification 2. Google Data Analytics Certification Learn the basics like SQL, Excel, and Tableau. Link : https://grow.google/intl/en_in/data-analytics-course/ 3. Meta Data Analyst Professional Certificate Hands-on with SQL, Python, and visualization. Link : https://www.coursera.org/professional-certificates/meta-data-analyst 4. IBM Data Analyst Professional Certificate Develop skills in Excel, Python, and SQL. Link : https://www.coursera.org/professional-certificates/ibm-data-analyst 5. Tableau Certified Data Analyst Become a pro in building dashboards with Tableau. Link : https://www.tableau.com/learn/certification/certified-data-analyst Certifications like these are a great way to validate your skills, build confidence, and stand out to recruiters. Which one are you planning to pursue? Let me know in the comments! Follow @jayenthakker Dedicated to helping aspiring data analysts thrive in their careers. ➕ Follow @metricminds.in for more tips, insights, and support on your data journey! #DataAnalytics #DataAnalysts #Tableau #freeresources #onlinecourses
#Master Data Reel by @dhanyindraswara (verified account) - 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
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@dhanyindraswara
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
#Master Data Reel by @the.datascience.gal (verified account) - Here's a roadmap to help you go from a software engineer to a data scientist 👩‍💻 👇 If you're tired of writing vanilla apps and want to build ML sy
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@the.datascience.gal
Here’s a roadmap to help you go from a software engineer to a data scientist 👩‍💻 👇 If you’re tired of writing vanilla apps and want to build ML systems instead, this one’s for you. Step 1 – Learn Python and SQL (not Java, C++, or JavaScript). → Focus on pandas, numpy, scikit-learn, matplotlib → For SQL: use LeetCode or StrataScratch to practice real-world queries → Don’t just write code—learn to think in data Step 2 – Build your foundation in statistics + math. → Start with Practical Statistics for Data Scientists → Learn: probability, hypothesis testing, confidence intervals, distributions → Brush up on linear algebra (vectors, dot products) and calculus (gradients, chain rule) Step 3 – Learn ML the right way. → Do Andrew Ng’s ML course (Deeplearning.ai) → Master the full pipeline: cleaning → feature engineering → modeling → evaluation → Read Elements of Statistical Learning or Sutton & Barto if you want to go deeper Step 4 – Build 2–3 real, messy projects. → Don’t follow toy tutorials → Use APIs or scrape data, build full pipelines, and deploy using Streamlit or Gradio → Upload everything to GitHub with a clear README Step 5 – Become a storyteller with data. → Read Storytelling with Data by Cole Knaflic → Learn to explain your findings to non-technical teams → Practice communicating precision/recall/F1 in simple language Step 6 – Stay current. Never stop learning. → Follow PapersWithCode (it's now sun-setted, use huggingface.co/papers/trending, ArXiv Sanity, and follow ML practitioners on LinkedIn → Join communities, follow researchers, and keep shipping new experiments ------- Save this for later. Tag a friend who’s trying to make the switch. [software engineer to data scientist, ML career roadmap, python for data science, SQL for ML, statistics for ML, data science career guide, ML project ideas, data storytelling, becoming a data scientist, ML learning path 2025]
#Master Data Reel by @sdw.online (verified account) - A data warehouse is a single source of truth that helps business functions perform their data analysis operations easier. Here's what a simple data
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@sdw.online
A data warehouse is a single source of truth that helps business functions perform their data analysis operations easier. Here's what a simple data warehouse looks like: 1. Data sources 2. Bronze layer 3. Silver layer 4. Gold layer 5. Analytics There's so much more that goes into a data warehouse (e.g. ingestion frequency, data governance policies, data validation checks etc), but this is a high level design you can start with. Different companies may configure the stages in different ways according to their users' unique requirements, but the generic workflow applies to all! #dataanalytics #dataengineering #datascience #techtok #dejavu

✨ #Master Data Discovery Guide

Instagram hosts thousands of posts under #Master Data, creating one of the platform's most vibrant visual ecosystems. This massive collection represents trending moments, creative expressions, and global conversations happening right now.

#Master Data is one of the most engaging trends on Instagram right now. With over thousands of posts in this category, creators like @onseventhsky, @marytheanalyst and @rebellionrider are leading the way with their viral content. Browse these popular videos anonymously on Pictame.

What's trending in #Master Data? The most watched Reels videos and viral content are featured above. Explore the gallery to discover creative storytelling, popular moments, and content that's capturing millions of views worldwide.

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Analysis of 12 reels

✅ Moderate Competition

💡 Top performing posts average 2.4M views (2.6x above average). Moderate competition - consistent posting builds momentum.

Post consistently 3-5 times/week at times when your audience is most active

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💡 Top performing content gets over 10K views - focus on engaging first 3 seconds

📹 High-quality vertical videos (9:16) perform best for #Master Data - use good lighting and clear audio

✨ Many verified creators are active (67%) - study their content style for inspiration

✍️ Detailed captions with story work well - average caption length is 910 characters

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