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Reel di Tendenza

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#Data Management Reel by @jessramosdata (verified account) - 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, Ir
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@jessramosdata
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!
#Data Management Reel by @sundaskhalidd (verified account) - Repost to share with friends ♻️ Here's how to become a data analyst in 2026 and beyond? 📈 The original video was 5 minutes long and I had to cut it d
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@sundaskhalidd
Repost to share with friends ♻️ Here’s how to become a data analyst in 2026 and beyond? 📈 The original video was 5 minutes long and I had to cut it down to 3 minutes because instagram. One part that got cut off was the job market. Should I post a part 2? what are other skills that would you add to the list?? #dataanalysis #dataanalyst #sql #python
#Data Management 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
#Data Management 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]
#Data Management Reel by @sundaskhalidd (verified account) - Comment 'Projects' to get 5 Data Scientist Project ideas and a plan 👩🏻‍💻 ♻️ repost to share with friends. Here is how to become a data scientist i
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@sundaskhalidd
Comment ‘Projects’ to get 5 Data Scientist Project ideas and a plan 👩🏻‍💻 ♻️ repost to share with friends. Here is how to become a data scientist in 2026 and beyond 📈 the original video was 4 min Andi had to cut it down to 3 because instagram. Should I do a part 3v what are other skills that you would add to the list and let me know what I should cover in the next video 👩🏻‍💻 #datascientist #datascience #python #machinelearning #sql #ai
#Data Management Reel by @charlottechaze_ - People think data analytics = intense coding. It's really not. Anyone can learn it, and you lose nothing by trying! Most people feel empowered and ins
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@charlottechaze_
People think data analytics = intense coding. It’s really not. Anyone can learn it, and you lose nothing by trying! Most people feel empowered and inspired after running their first line of code within 20 minutes. It’s a powerful feeling. #dataanalytics #careerchange #techtransition #breakintotech #quityourjob #startyourcareer #jobsearch #linkedintips #highincomeskills
#Data Management Reel by @phoebeslifeindata - watch this if you want to become a data analyst in 2026, these are my top simple tips 📊 1. Learn SQL: its the tool you'll use to get data from datab
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@phoebeslifeindata
watch this if you want to become a data analyst in 2026, these are my top simple tips 📊 1. Learn SQL: its the tool you’ll use to get data from databases, and then use to analyse business performance 2. Learn Excel or something similar: it’s great for ad hoc analysis and building engaging charts and diagrams 3. Get familiar with a reporting tool, you don’t need to be great at this just an understanding is fine 4. The core skills are communicating your insights clearly and understanding business metrics Save this and come back to it when you’re planning what to learn, I have links on my profile for courses/guides for each of these aspects!
#Data Management 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
#Data Management Reel by @lillian__chiu (verified account) - how I analyze data as a Business Analyst at Spotify! Spotify商業分析師如何分析數據？ ft. @tableausoftware #womenintech #businessanalyst #dataanalyst #gendata
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@lillian__chiu
how I analyze data as a Business Analyst at Spotify! Spotify商業分析師如何分析數據？ ft. @tableausoftware #womenintech #businessanalyst #dataanalyst #gendata #datafam #spotify
#Data Management Reel by @muskan.khannaa - You DO NOT need to learn everything to become a Data Engineer. People often prepare for mid-level roles while applying for entry-level roles. Here's
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@muskan.khannaa
You DO NOT need to learn everything to become a Data Engineer. People often prepare for mid-level roles while applying for entry-level roles. Here’s what actually mattered for me in the beginning when switching from testing to a data engineer role. 1. SQL(non-negotiable): You’ll need to know the basics and complexities of sql along including subqueries and window functions. If you’re not strong in SQL, you won’t be able to move forward in interviews. 2. Python concepts basics like lists, dictionaries, sets and basic problem solving. You can solve questions in other languages too but I’d suggest Python as it’s easy to learn. You don’t need hardcore DSA for most entry-level Data Engineering roles, but DSA is definitely important. 3. Data warehousing concepts like facts vs dimension, star vs snowflake schema, SCD Type 1,2 etc. Understanding concepts and what data warehousing is and why it’s there mattered more than tools. 4. ETL and data pipeline understanding. How data is extracted, transformed, loaded is the CORE of Data Engineering. You don’t need spark understanding in the beginning, just the understanding of how data flows in and out. 5. System design basics, not like design twitter/uber. Simple understanding of how data moves end to end and overall understanding of data eco-systems. No deep design is expected at entry-level. 6. Pick any one cloud. Don’t chase all clouds, just any one cloud and cover its basics because you’d most likely be working on some cloud in your work. I moved from Testing to Data Engineering by focusing on these basics, instead of trying to learn every other tool out there, and it is still the very core of Data Engineering which one must know to crack interviews. Save this if you’re planning to make a switch into Data Engineering. . . . . . [data engineering roadmap, entry level data engineer preparation, switching to data engineering, testing to data engineering, data engineer interview preparation, sql for data engineering, python basics for data engineer, data engineers for beginners, microsoft data engineer] #dataengineer #dataengineering
#Data Management Reel by @maggieindata (verified account) - Cleaning messy data is the bread and butter of a data analyst and data scientist job. This question often comes up in the technical portion of your in
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@maggieindata
Cleaning messy data is the bread and butter of a data analyst and data scientist job. This question often comes up in the technical portion of your interviews. The hiring manager is looking for a structured and thoughtful response that involve lots of communications with stakeholders throughout the process💕 you got this #datascience #dataanalytics #breakintotech #careerindata #womenwhocode
#Data Management Reel by @marytheanalyst - Day 3: Importing Data into Power BI (+ importing data from the web!) #dataanalyst #dataanalysis #dataanalytics #powerbi #powerquery
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@marytheanalyst
Day 3: Importing Data into Power BI (+ importing data from the web!) #dataanalyst #dataanalysis #dataanalytics #powerbi #powerquery

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L'enorme raccolta #Data Management su Instagram presenta i video più coinvolgenti di oggi. I contenuti di @the.datascience.gal, @sundaskhalidd and @chrisoh.zip e altri produttori creativi hanno raggiunto 1 million post a livello globale.

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