#Dataengineering Trends

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#Dataengineering Trends Reel by @jessramosdata (verified account) - Comment "trends" for my substack article with all the details and my youtube vid 🎥 Save this video for later and follow for parts 2 & 3 (data enginee
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@jessramosdata
Comment “trends” for my substack article with all the details and my youtube vid 🎥 Save this video for later and follow for parts 2 & 3 (data engineering and data science!) The top 2026 Data Analytics trends: 1. AI-driven analytics: leveraging AI tools to write code and work more efficiently 2. AI integrated into tools: tools like Power BI & Excel have Copilot, Snowflake & Databricks have built-in AI assistants. 3. Agentic AI: AI chatbots aren’t enough anymore. Agents can complete end-to-end workflows and act as another coworker. What other trends do you think we’ll see this year? #data #dataanalytics #ai #dataengineering #datascience
#Dataengineering Trends Reel by @eczachly (verified account) - Comment roadmap to get sent my free and complete data engineering roadmap!
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@eczachly
Comment roadmap to get sent my free and complete data engineering roadmap!
#Dataengineering Trends Reel by @jayenthakker - You probably Googled "How to learn Data Analytics"… And got 100 tabs open. Courses, tools, bootcamps, blogs, YouTube videos, each saying "Start here.
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@jayenthakker
You probably Googled “How to learn Data Analytics”… And got 100 tabs open. Courses, tools, bootcamps, blogs, YouTube videos, each saying “Start here.” But no one told you what not to do. No one gave you a starter kit that actually made sense. So I built. And now I use this same plan to guide beginners I mentor. Here’s the Data Analytics Starter Kit I wish everyone should have👇 1. Start with Excel → It’s not outdated, it’s underrated. → Master formulas, Pivot Tables, and charts. 2. Then SQL → Learn how to query real data. → SELECT, WHERE, GROUP BY, and JOIN. That’s 80% of your job. 3. Add one viz tool → Pick Tableau or Power BI. → Focus on storytelling, not fancy dashboards. 4. Forget 100-hour courses Instead, build 3 small projects: ⤷ A Sales Dashboard in Excel ⤷ A Customer Retention Report in SQL ⤷ A Visual Story in Tableau 5. Use GitHub + LinkedIn → Document your projects. → Share your process. → Visibility builds credibility. 6. Give it 6–8 weeks Learn 1 skill → Apply it → Move to the next. If you're just starting out, don't chase 10 tools. Build your foundation first. Want my full Data Analytics Starter Kit with a roadmap, tool list, and project ideas? Drop "Community" to join my community here. #datavisualization #dataanalyst #datascience #data #sql #excel #python #career #careerswitch #trending #learning #interviewtips #india #metricminds
#Dataengineering Trends Reel by @vee_daily19 - DATA ENG - 90 day prep resources . . . {data engineering , resource , tech ,projects, internships, job search } . . #technology #trending #jobsear
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@vee_daily19
DATA ENG - 90 day prep resources . . . {data engineering , resource , tech ,projects, internships, job search } . . #technology #trending #jobsearch #parttime #techconsulting #tech #hacks #behavioral #nodaysoff #veeconsistent #linkedin #emails #dataengineering
#Dataengineering Trends Reel by @nataindata (verified account) - If you need to learn DSA, try these 🏷️ data structures, algorithms, dsa, data science, big data, data engineering, ai, coding, ai for beginners, fy
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@nataindata
If you need to learn DSA, try these 🏷️ data structures, algorithms, dsa, data science, big data, data engineering, ai, coding, ai for beginners, fyp, ai girl
#Dataengineering Trends 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]
#Dataengineering Trends Reel by @priyal.py - 1. Netflix Show Clustering Group similar shows using K-Means based on genre, rating, and duration. Tech Stack: Python, Pandas, Scikit-learn, Seaborn
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@priyal.py
1. Netflix Show Clustering Group similar shows using K-Means based on genre, rating, and duration. Tech Stack: Python, Pandas, Scikit-learn, Seaborn 2. Spotify Audio Feature Analyzer Analyze songs by tempo, energy and danceability using Spotify API. Tech Stack: Python, Spotipy, Matplotlib, Plotly 3. YouTube Trending Video Analyzer Discover what makes a video go viral. Tech Stack: Python, Pandas, BeautifulSoup, Seaborn 4. Resume Scanner using NLP Parse and rank resumes based on job description matching. Tech Stack: Python, SpaCy, NLTK, Streamlit 5. Crypto Price Predictor Predict BTC/ETH prices using historical data. Tech Stack: Python, LSTM (Keras), Pandas, Matplotlib 6. Instagram Hashtag Recommender Suggest hashtags based on image captions or niche. Tech Stack: Python, NLP, TF-IDF, Cosine Similarity 7. Reddit Sentiment Tracker Analyze community sentiment on hot topics using Reddit API. Tech Stack: Python, PRAW, VADER, Plotly 8. AI Job Postings Dashboard Scrape and visualize job trends by tech stack and location. Tech Stack: Python, Selenium/BeautifulSoup, Streamlit 9. Airbnb Price Estimator Predict listing prices based on location and amenities. Tech Stack: Python, Scikit-learn, Pandas, XGBoost 10. Food Calorie Image Classifier Estimate calories from food images using CNNs. Tech Stack: Python, TensorFlow/Keras, OpenCV Each project can be completed in 1-2 weekends. #datascience #machinelearning #womeninstem #learningtogether #progresseveryday #tech #consistency #projects
#Dataengineering Trends Reel by @pascal_bornet - 📊 Everyone wants to talk about AI → But very few want to talk about data I see it constantly - visionary AI strategies and bold promises slowed down
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@pascal_bornet
📊 Everyone wants to talk about AI → But very few want to talk about data I see it constantly — visionary AI strategies and bold promises slowed down by one silent obstacle: data unpreparedness. It’s rarely the model that fails first — it’s the foundation. Here’s what usually happens: → Contracts stored across disconnected systems → Servers unable to handle modern workloads → Undefined standards (like what “client” even means) → Invoice data missing line-level details → Critical information trapped in paper or inboxes AI doesn’t fix chaos. It amplifies it. If humans can’t access your data, AI won’t either. That’s why every successful AI transformation starts with data readiness, not data ambition. So — is your organization truly ready for AI, or still waiting for your data to catch up? #AI #Data #Technology #Innovation #DigitalTransformation #Leadership #Strategy
#Dataengineering Trends Reel by @jayenthakker - You don't need to be a machine learning expert… …but knowing these 6 algorithms? That's how you stop being 'just another analyst' and start turning he
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@jayenthakker
You don’t need to be a machine learning expert… …but knowing these 6 algorithms? That’s how you stop being ‘just another analyst’ and start turning heads in the data world. 👀💡 From simple Linear Regression to powerful Decision Trees 🌳 — these algorithms help you do way more than just describe data. They help you predict, classify, and uncover patterns that would otherwise go unnoticed. And the best part? You don’t need a PhD to start using them — just curiosity and the right breakdown (which is exactly what this post gives you). 😉 -- Follow @metricminds.in and @jayenthakker ➕ Helping future analysts build confidence, skills & cleaner datasets. #DataCleaning #AnalyticsTips #DataCleaningMatters #LearnData #datavisualization #dataanalytics #datascience #metricminds #sql #python #ai #trending #foryoupage #india #LearnWithMe
#Dataengineering Trends 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
#Dataengineering Trends Reel by @itssimplyjordan (verified account) - How I'd become a Data Analyst in 2026 ⬇️ 1️⃣ Get in the door (any role) Data Analyst titles are hard to land, degree or not. So get into any role at
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@itssimplyjordan
How I’d become a Data Analyst in 2026 ⬇️ 1️⃣ Get in the door (any role) Data Analyst titles are hard to land, degree or not. So get into any role at a tech forward company with an analytics team/department . Sales. Ops. Data entry. Work up! Prove your value. That’s exactly what I did. 2️⃣ Improve what’s in front of you Look for small things you can control: • Excel • MS Access • Power Query Invoices research (ms access), trends, reports doesn’t matter, anything YOU can do. 3️⃣ Learn only what you need Target the tools you’re already working with/access too. (DataCamp and Codecademy worked for me) 4️⃣ Build something real Not tutorials. Build a tool people (and you) actually use even if it’s simple. Examples could be: Using forms and VBA/SQL in ms access to build a form for people to researching invoices! 5️⃣ Show your work Demo it. Explain the impact. Who uses it. Why it matters. And how it helps! 6️⃣ Say yes to opportunities Take on EVERYTHING, prove you can do the work, even if it adds more stress. That’s how you stack proof for the next role. No degree required. 👉 Follow if you’re breaking into data. #dataanalyst #howto #breakintotech #nodegree #2026goals
#Dataengineering Trends 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

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