#Data Masters

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#Data Masters 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 Masters 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 Masters 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 Masters Reel by @young4stem - Where are all our data scientists at! 👀👇🏻 #young4stem #datascience #job #reel #stem #computerscience
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@young4stem
Where are all our data scientists at! 👀👇🏻 #young4stem #datascience #job #reel #stem #computerscience
#Data Masters Reel by @vee_daily19 - If you want to crack Data Science jobs in the next 30 days, here's the three step process which you will follow which literally no one talks about. .
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@vee_daily19
If you want to crack Data Science jobs in the next 30 days, here’s the three step process which you will follow which literally no one talks about. . . . #datascience #data #interview
#Data Masters 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 Masters Reel by @mrk_talkstech (verified account) - Data Engineers work tirelessly behind the scenes to build the infrastructure for data projects. However, their efforts often remain invisible to busin
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@mrk_talkstech
Data Engineers work tirelessly behind the scenes to build the infrastructure for data projects. However, their efforts often remain invisible to business users, who focus on the end product and reward Data Scientists and Analysts with more recognition! #dataengineering #azure #pyspark #dataengineer #azuredataengineer #data #aws #gcp #azuredatabricks #dataanalyst #datascientist #datascience
#Data Masters Reel by @learnwidgiggs - Companies for Data Analyst: Save the reel, share with your friends and Follow me for more useful content 📌 Here is the list- ·        Tiger Analyt
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@learnwidgiggs
Companies for Data Analyst: Save the reel, share with your friends and Follow me for more useful content 📌 Here is the list- ·        Tiger Analytics ·        The MATH Company ·        Fractal Analytics ·        Absolutdata ·        Celebal Technologies ·        Tredence ·        Latent View ·        Analytics Quotient ·        Ugam Solutions ·        EXL Services ·        BRIDGEi2i(Acquired by Accenture) ·        Quantiphi ·        Manthan ·        Nielsen IQ(NIQ) ·        ZS Associates ·        Light Microfinance Pvt. Ltd ·        Recykal ·        Integ Enterprise Consulting ·        Manjushree Technopack Limited ·        Gartner ·        Clearwater Analytics ·        Experian India ·        CRIF India ·        DecisionTree  Analytics & Services ·        Jupiter ·        YASH Technologies ·        Internshala ·        Arus Info Hope this helps you 🙏 #powerbi #sql #excel #dataanalyst #dataanalytics #learnwidgiggs
#Data Masters Reel by @salarytransparentstreet (verified account) - How much do Data Scientists make? Watch this compilation to hear from Data Scientists we've interviewed in 5 different cities. Full interviews up on
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@salarytransparentstreet
How much do Data Scientists make? Watch this compilation to hear from Data Scientists we’ve interviewed in 5 different cities. Full interviews up on our YouTube channel 💚 According to the BLS, the median annual wage for data scientists was $100,910 in May 2021. Employment of data scientists is projected to grow 36% from 2021 to 2031, much faster than the average for all occupations. (Bureau of Labor Statistics) Check out our free resources! ⬇️ 🤑 Explore thousands of salaries reported in our Salary Database. 💰 Need help negotiating? Our Salary Negotiation Guide has scripts and advice to help you get the salary you deserve. 📚 Download our free Market Research Guide to learn what you’re worth. 🗞️ Subscribe to our weekly newsletter for news updates, professional deep dives, learning opportunities, and more! #SalaryTransparentStreet #SalaryTransparency #PayTransparency #HowMuchDoYouMake #DataScientist #DataScientistSalary #DataScientistPay #BreakIntoDataScience #HowtoBreakIntoDataScience #DataScienceCareerAdvice #DataScienceInterview #datasciencejobs #breakintotech #datasciencecertification
#Data Masters Reel by @woman.engineer (verified account) - 📍Learning to code and becoming a data scientist without a background in computer science or mathematics is absolutely possible, but it will require d
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@woman.engineer
📍Learning to code and becoming a data scientist without a background in computer science or mathematics is absolutely possible, but it will require dedication, time, and a structured approach. ✨👌🏻 🖐🏻Here’s a step-by-step guide to help you get started: 1. Start with the Basics: - Begin by learning the fundamentals of programming. Choose a beginner-friendly programming language like Python, which is widely used in data science. - Online platforms like Codecademy, Coursera, and Khan Academy offer interactive courses for beginners. 2. Learn Mathematics and Statistics: - While you don’t need to be a mathematician, a solid understanding of key concepts like algebra, calculus, and statistics is crucial for data science. - Platforms like Khan Academy and MIT OpenCourseWare provide free resources for learning math. 3. Online Courses and Tutorials: - Enroll in online data science courses on platforms like Coursera, edX, Udacity, and DataCamp. Look for beginner-level courses that cover data analysis, visualization, and machine learning. 4. Structured Learning Paths: - Follow structured learning paths offered by online platforms. These paths guide you through various topics in a logical sequence. 5. Practice with Real Data: - Work on hands-on projects using real-world data. Websites like Kaggle offer datasets and competitions for practicing data analysis and machine learning. 6. Coding Exercises: - Practice coding regularly to build your skills. Sites like LeetCode and HackerRank offer coding challenges that can help improve your programming proficiency. 7. Learn Data Manipulation and Analysis Libraries: - Familiarize yourself with Python libraries like NumPy, pandas, and Matplotlib for data manipulation, analysis, and visualization. For more look at the comment ⤵️ . . . #datascience #computerscience #datascientist #dataanalytics #dataanalyticstraining #python #softwaredeveloper #dataanalysis #bigdata #generativeai #codingbootcamp #businesswoman #veribilimi #codemotivation
#Data Masters Reel by @aiwithpj - 2026 Data Engineer Roadmap 🚀 (0 → Job Ready) Want to become a Data Engineer? Start with Python & advanced SQL → learn databases & data modeling → m
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@aiwithpj
2026 Data Engineer Roadmap 🚀 (0 → Job Ready) Want to become a Data Engineer? Start with Python & advanced SQL → learn databases & data modeling → master ETL pipelines → work with big data tools like Spark & Kafka → deploy on cloud platforms. This roadmap covers: Python • SQL • ETL • Airflow • dbt • Spark • Kafka • AWS/GCP • Data Warehousing • Real-time pipelines. Perfect for students, developers, and anyone entering data engineering. Save this reel & start building data pipelines today 📊🔥 #DataEngineer #DataEngineering #BigData #ETL #AIwithPJ

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