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Data cleaning is where real data science begins Before building models, every data analyst must master data cleaning using Python and Pandas. Start with an initial data check (df.head, df.info, df
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Still spending hours on repetitive tasks? That’s where smart analysts are changing the game. Today, data analysts aren’t Just building dashboards—they’re automating. workflows, cleaning data
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These projects prove you can: Automate boring work Save real business hours Add measurable value 📈 And THAT is why recruiters notice you → not because of a “chatbot,” but because of impact. ⚡ Ge
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These projects help you learn LangChain, OpenAI API, embeddings, HuggingFace, Streamlit, and Pinecone while building a strong portfolio. 📈 Normal Job = ₹4–6 LPA 🚀 GenAI-skilled Job = ₹18–30 LPA If
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Execution order is a crucial concept in SQL, Python, Power BI, and Machine Learning workflows. In SQL, queries are written starting with SELECT, but the database executes them in this order: FROM → W
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Gen AI Roadmap 2026 🚀 Want to learn Generative AI step-by-step? Start with Python basics, simple math, and core machine learning concepts. Then understand LLMs, transformers, attention, and token
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Before ML, there is Data Cleaning. 🔥 Most beginners jump into models, but 50–70% of real projects happen before you even train anything. Data cleaning isn’t about making data pretty. It’s about mak
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It’s not just about salary. It’s about expectations, decision power, impact, and skills. At 4 LPA, you’re paid to learn, follow instructions, and support teams with basic SQL, Python, and dashboard
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Exploratory Data Analysis (EDA) is the first and most important step in any data science project. 🔹 Load & inspect data using Pandas 🔹 Check missing values and data types 🔹 Handle missing data and
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What MOST Roadmaps Don’t Tell You (Reality Check) 💥70% of DS = data cleaning + SQL + thinking 📍ML won’t save weak fundamentals 📍Tools change, thinking doesn’t Consistency > speed💯 Time Reality
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Agentic AI is not just another buzzword — it’s how AI actually works in the real world. Traditional AI answers questions. AI agents take goals, plan steps, use tools, remember context, and execute en
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Struggling with Machine Learning basics or interview coding questions? This post is your one-stop ML basics cheat sheet 📘 Learn how to train a linear regression model, split data into train and te
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About @data.science.beginners

DATA SCIENCE is a Instagram macro influencer in the Education category. With 126.4K followers and 896 posts, data.science.beginners's account shows a follower-to-following ratio of 5054.1:1. This profile has significant reach and engagement potential for brand collaborations. View all content anonymously on Pictame - no login required.

data.science.beginners's engagement rate sits at 0.26%. For accounts with 100K-500K followers, the average is around 1.8%, so there's some room to grow — though every community is unique.

Out of the last 12 posts, 0% are videos and 100% are photos. Content shows up about every 6 days. Captions lean detailed, averaging 701 characters.

0.26% engagement rate — room to grow compared to average for 100K-500K follower accounts.
Posts about every 6 days.
5054.1:1 follower-to-following ratio.

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@data.science.beginners

DATA SCIENCE

PublicEducation
Posts
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#DataScience#GenerativeAI#GenAI#datascience#CareerGrowth#LPAJobs#AnalyticsCareer#DataScientist

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Data Scientist | 4 Yrs Experience | Gen AI Specializing in Python, SQL, ML, and career guidance

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