Data Science & Machine Learning

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Join this channel to learn data science, artificial intelligence and machine learning with funny quizzes, interesting projects and amazing resources for free For collaborations: @love_data

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📈 Telegram kanali Data Science & Machine Learning analitikasi

Data Science & Machine Learning (@datasciencefun) Ingliz til segmentidagi kanali faol ishtirokchi. Hozirda hamjamiyat 75 645 obunachidan iborat bo'lib, Taʼlim toifasida 2 114-o'rinni va Hindiston mintaqasida 4 359-o'rinni egallagan.

📊 Auditoriya ko‘rsatkichlari va dinamika

невідомо sanasidan buyon loyiha tez o‘sib, 75 645 obunachiga ega bo‘ldi.

11 Iyun, 2026 dagi oxirgi ma’lumotlarga ko‘ra kanal barqaror faollikka ega. Oxirgi 30 kunda obunachilar soni 911 ga, so‘nggi 24 soatda esa 29 ga o‘zgardi va umumiy qamrov yuqori darajada qolmoqda.

Tasdiqlash holati: Tasdiqlanmagan
Jalb etish (ER): Auditoriya o‘rtacha 3.63% darajada jalb etiladi. Nashrdan keyingi dastlabki 24 soatda kontent odatda umumiy obunachilar sonining 1.36% ini tashkil etuvchi reaksiyalarni to‘playdi.
Post qamrovi: Har bir post o‘rtacha 2 747 marta ko‘riladi; birinchi sutkada odatda 1 032 ta ko‘rish yig‘iladi.
Reaksiyalar va o‘zaro ta’sir: Auditoriya faol: har bir postga o‘rtacha 5 ta reaksiya keladi.
Tematik yo‘nalishlar: Kontent learning, accuracy, distribution, panda, dataset kabi asosiy mavzularga jamlangan.

📝 Tavsif va kontent siyosati

Muallif resursni shaxsiy fikrni ifoda etish maydoni sifatida ta’riflaydi:
“Join this channel to learn data science, artificial intelligence and machine learning with funny quizzes, interesting projects and amazing resources for free For collaborations: @love_data”

Yuqori yangilanish chastotasi (oxirgi ma’lumot 12 Iyun, 2026 da olingan) sababli kanal doimo dolzarb va katta qamrovli bo‘lib qoladi. Analitika auditoriya kontent bilan faol hamkorlik qilishini, uni Taʼlim toifasidagi muhim ta’sir nuqtasiga aylantirishini ko‘rsatadi.

75 645

Obunachilar

+2924 soatlar

+2107 kunlar

+91130 kunlar

2 747

Post ko'rishlar

~ 1 03224 soatlar

~ 1 40648 soatlar

3.63%

Muloqot nisbati

~ 2

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75 645

Which function is used to display a plot?

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75 645

Which library is used for basic plotting in Python?

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✅ Data Science Interview Prep Guide 📊🧠 Whether you're a fresher or career-switcher, here’s how to prep step-by-step: 1️⃣ Understand the Role Data scientists solve problems using data. Core responsibilities: • Data cleaning & analysis • Building predictive models • Communicating insights • Working with business/product teams 2️⃣ Core Skills Needed ✔️ Python (NumPy, Pandas, Matplotlib, Scikit-learn) ✔️ SQL ✔️ Statistics & probability ✔️ Machine Learning basics ✔️ Data storytelling & visualization (Power BI / Tableau / Seaborn) 3️⃣ Key Interview Areas A. Python & Coding • Write code to clean and analyze data • Solve logic problems (e.g., reverse a list, group data by key) • List vs Dict vs DataFrame usage B. Statistics & Probability • Hypothesis testing • p-values, confidence intervals • Normal distribution, sampling C. Machine Learning Concepts • Supervised vs unsupervised learning • Overfitting, regularization, cross-validation • Algorithms: Linear Regression, Decision Trees, KNN, SVM D. SQL • Joins, GROUP BY, subqueries • Window functions • Data aggregation and filtering E. Business & Communication • Explain model results to non-tech stakeholders • What metrics would you track for [business case]? • Tell me about a time you used data to influence a decision 4️⃣ Build Your Portfolio ✅ Do projects like: • E-commerce sales analysis • Customer churn prediction • Movie recommendation system ✅ Host on GitHub or Kaggle ✅ Add visual dashboards and insights 5️⃣ Practice Platforms • LeetCode (SQL, Python) • HackerRank • StrataScratch (SQL case studies) • Kaggle (competitions & notebooks) 💬 Tap ❤️ for more!

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𝗧𝗼𝗽 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻𝘀 𝘁𝗼 𝗟𝗮𝗻𝗱 𝗮 𝗛𝗶𝗴𝗵-𝗣𝗮𝘆𝗶𝗻𝗴 𝗝𝗼𝗯 𝗶𝗻 𝟮𝟬𝟮𝟲🔥 Learn from scratch → Build real projects → Get placed ✅ 2000+ Students Already Placed 🤝 500+ Hiring Partners 💼 Avg Salary: ₹7.4 LPA 🚀 Highest Package: ₹41 LPA Fullstack :- https://pdlink.in/4hO7rWY Data Analytics :- https://pdlink.in/4fdWxJB 📈 Don’t just scroll… Start today & secure your 2026 job NOW

75 645

What does a histogram show?

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Which library is used for advanced and attractive visualizations?

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What type of chart is best for showing trends over time?

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75 645

Which function is used to display a plot?

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75 645

Which library is used for basic plotting in Python?

Anonymous voting

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Freshers are getting paid 10 - 15 Lakhs by learning AI & ML skill 📢 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗔𝗹𝗲𝗿𝘁 – 𝗔𝗿𝘁𝗶𝗳𝗶𝗰𝗶𝗮𝗹 𝗜𝗻𝘁𝗲𝗹𝗹𝗶𝗴𝗲𝗻𝗰𝗲 𝗮𝗻𝗱 𝗠𝗮𝗰𝗵𝗶𝗻𝗲 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 Open for all. No Coding Background Required 📊 Learn AI/ML from Scratch 🤖 AI Tools & Automation 📈 Build real world Projects for job ready portfolio 🎓 Vishlesan i-Hub, IIT Patna Certification Program 🔥Deadline :- 12th April 𝗔𝗽𝗽𝗹𝘆 𝗡𝗼𝘄👇 :- https://pdlink.in/41ZttiU . Get Placement Assistance With 5000+ Companies from Masai School

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✅ Data Cleaning in Pandas 🐍🧹 👉 In real projects, 80% of the work = Data Cleaning Because raw data is always messy 😅 🔹 1. Why Data Cleaning? Real-world data may have: ❌ Missing values ❌ Duplicate records ❌ Wrong formats ❌ Extra spaces 👉 Cleaning makes data usable for analysis & ML. 🔥 2. Handling Missing Values ✅ Check Missing Values df.isnull() df.isnull().sum() ✅ Remove Missing Values df.dropna() ✅ Fill Missing Values df.fillna(0) 👉 Replace missing values with 0 or mean. 🔹 3. Remove Duplicates df.drop_duplicates() 🔹 4. Rename Columns df.rename(columns={"Name": "Full_Name"}, inplace=True) 🔹 5. Change Data Types df["Age"] = df["Age"].astype(int) 🔹 6. Remove Extra Spaces df["Name"] = df["Name"].str.strip() 🔹 7. Replace Values df["City"] = df["City"].replace("NY", "New York") 🔹 8. Why This is Important? ✔ Clean data = better insights ✔ Clean data = better ML models ✔ Used in every real-world project 🎯 Today’s Goal ✔ Handle missing values ✔ Remove duplicates ✔ Fix data types ✔ Clean text data 👉 Double Tap ❤️ For More

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𝗦𝗯𝗲𝗿𝟱𝟬𝟬 𝗕𝗮𝘁𝗰𝗵 𝟳 — 𝗙𝗿𝗲𝗲 𝗔𝗰𝗰𝗲𝗹𝗲𝗿𝗮𝘁𝗼𝗿 𝗳𝗼𝗿 𝗔𝗜 & 𝗗𝗲𝗲𝗽𝗧𝗲𝗰𝗵 𝗦𝘁𝗮𝗿𝘁𝘂𝗽𝘀 🚀 Ready to scale your startup beyond local market? Who should apply: ✅ Startups with MVP and early traction ✅ DeepTech: GenAI, robotics, advanced materials, photonics, quantum computing ✅ Applied AI for research, Earth remote sensing, autonomous transport ✅ International founders exploring the Russian market What you'll get: 📍 12-week online program in English 📍 International mentors (Europe, US, Asia, Middle East) 📍 Access to investors & corporate customers 📍 Demo Day at Moscow Startup Summit (Fall 2026) Results: 📈 Revenue grows 4x on average, up to 1,000x for some teams 🤝 10,900+ contracts and pilots with corporations (6 seasons) Program stages: 1️⃣ Online bootcamp for 150 teams 2️⃣ 25 best teams → intensive mentorship 3️⃣ Demo Day presentation Key details: 📅 Deadline: 10 April 2026 💰 Participation: Free of charge 🌐 Format: Online 💬 Language: English 𝗔𝗽𝗽𝗹𝘆 𝗡𝗼𝘄 👇 https://sberbank-500.ru/ 💥 Don't wait. Scale your startup with Sber500. React ❤️ for more startup opportunities! #DataScience #MachineLearning #DeepTech #GenAI #Startup #Accelerator #AI

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📊 Python for Data Science – Complete Beginner Roadmap 🐍🚀 🔹 What is Data Science? Data Science is about: Collecting data Cleaning it Analyzing it Finding insights Making predictions 👉 Example: - Predict sales 📈 - Analyze customer behavior 🛒 - Detect fraud 💳 🧭 Step-by-Step Roadmap 🔹 1️⃣ Strengthen Python Basics Focus on: Lists, dictionaries Loops & conditions Functions Basic file handling 👉 Because data is handled using these structures. 🔹 2️⃣ Learn NumPy (Numerical Computing) NumPy is used for: Fast calculations Working with arrays import numpy as np arr = np.array([1,2,3]) print(arr.mean()) 👉 Used in: Machine learning Scientific computing 🔹 3️⃣ Learn Pandas (Most Important 🔥) Pandas helps you: Read data (CSV, Excel) Clean data Analyze data import pandas as pd df = pd.read_csv("data.csv") print(df.head()) 👉 Must learn: head(), info() filtering groupby() merge() 🔹 4️⃣ Data Visualization Tools: matplotlib seaborn import matplotlib.pyplot as plt plt.plot([1,2,3],[10,20,30]) plt.show() 👉 Used to: Present insights Create reports Build dashboards 🔹 5️⃣ Statistics Basics (Very Important) Learn: Mean, Median, Mode Standard Deviation Probability basics 👉 Data science = math + logic + code 🔹 6️⃣ Data Cleaning (Real-World Skill) Real data is messy 😅 You should learn: - Handling missing values - Removing duplicates - Fixing data types df.dropna() df.fillna(0) 🔹 7️⃣ Intro to Machine Learning Using scikit-learn: from sklearn.linear_model import LinearRegression Learn: - Regression - Classification - Model training 🔹 8️⃣ Real Projects (Most Important 🚀) Start building: 💡 Project Ideas: - Sales analysis dashboard - IPL data analysis - Netflix dataset insights - Customer churn prediction 🧠 Double Tap ❤️ For More

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10 Simple Habits to Boost Your Data Science Skills 🧠📊 1) Practice data wrangling daily (Pandas, dplyr) 2) Work on small end-to-end projects (ETL, analysis, visualization) 3) Revisit and improve previous notebooks or scripts 4) Share findings in a clear, story-driven way 5) Follow data science blogs, newsletters, and researchers 6) Tackle weekly datasets or Kaggle competitions 7) Maintain a notebooks/journal with experiments and results 8) Version control your work (Git + GitHub) 9) Learn to communicate uncertainty (confidence intervals, p-values) 10) Stay curious about new tools (SQL, Python libs, ML basics) 💬 React "❤️" for more! 😊

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What will the following code return? df.head()

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