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  • 29 мая✅ 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!0,44%
  • 13 авг.📊 Data Science Roadmap 🚀 📂 Start Here ∟📂 What is Data Science & Why It Matters? ∟📂 Roles (Data Analyst, Data Scientist, ML Engineer) ∟📂 Setting Up Environment (Python, Jupyter Notebook) 📂 Python for Data Science ∟📂 Python Basics (Variables, Loops, Functions) ∟📂 NumPy for Numerical Computing ∟📂 Pandas for Data Analysis 📂 Data Cleaning & Preparation ∟📂 Handling Missing Values ∟📂 Data Transformation ∟📂 Feature Engineering 📂 Exploratory Data Analysis (EDA) ∟📂 Descriptive Statistics ∟📂 Data Visualization (Matplotlib, Seaborn) ∟📂 Finding Patterns & Insights 📂 Statistics & Probability ∟📂 Mean, Median, Mode, Variance ∟📂 Probability Basics ∟📂 Hypothesis Testing 📂 Machine Learning Basics ∟📂 Supervised Learning (Regression, Classification) ∟📂 Unsupervised Learning (Clustering) ∟📂 Model Evaluation (Accuracy, Precision, Recall) 📂 Machine Learning Algorithms ∟📂 Linear Regression ∟📂 Decision Trees & Random Forest ∟📂 K-Means Clustering 📂 Model Building & Deployment ∟📂 Train-Test Split ∟📂 Cross Validation ∟📂 Deploy Models (Flask / FastAPI) 📂 Big Data & Tools ∟📂 SQL for Data Handling ∟📂 Introduction to Big Data (Hadoop, Spark) ∟📂 Version Control (Git & GitHub) 📂 Practice Projects ∟📌 House Price Prediction ∟📌 Customer Segmentation ∟📌 Sales Forecasting Model 📂 ✅ Move to Next Level ∟📂 Deep Learning (Neural Networks, TensorFlow, PyTorch) ∟📂 NLP (Text Analysis, Chatbots) ∟📂 MLOps & Model Optimization Data Science Resources: https://whatsapp.com/channel/0029VaxbzNFCxoAmYgiGTL3Z React "❤️" for more! 🚀📊0,33%
  • 2 июл.🎯 7 YouTube Courses = 4 Years Degree👇 1/ N8N Full Course 6 Hours: https://youtu.be/2GZ2SNXWK-c?si=C1DRnvxBqNBdW5Vp 2/ The EASIEST Way to Build & Publish Mobile Apps Using Al (Anything + ChatGPT): https://youtu.be/LLjy46X9rJE?si=M5dxuW5yHLzlluK2 3/ ChatGPT Tutorial 2025: How to Use ChatGPT - Beginner to Pro!: https://youtu.be/zqVtHYFYQY8?si=ScN5YhJetg37EBIa 4/ How to Build & Sell Al Agents: Ultimate Beginner's Guide: https://youtu.be/w0H1-b044KY?si=J5ko8ovDSmzTvmbG 5/ FREE 8 Hour Copywriting Course For Beginners | $0-$10k/mo In 90 Days: https://youtu.be/OC0nBt3nuDg?si=h5IDlefnHmNB5Iig 6/ Improve Your Communication Skills with This! | John Maxwell: https://youtu.be/S0mbgU239ao?si=X3P-EKMXmCSNAab7 7/ How to Sell Better than 99% Of People (4 HOUR ULTIMATE GUIDE): https://youtu.be/JE2_7elAcxM?si=oVFqZkotLmCTw5hU0,31%
  • 11 июн.A-Z of essential data science concepts A: Algorithm - A set of rules or instructions for solving a problem or completing a task. B: Big Data - Large and complex datasets that traditional data processing applications are unable to handle efficiently. C: Classification - A type of machine learning task that involves assigning labels to instances based on their characteristics. D: Data Mining - The process of discovering patterns and extracting useful information from large datasets. E: Ensemble Learning - A machine learning technique that combines multiple models to improve predictive performance. F: Feature Engineering - The process of selecting, extracting, and transforming features from raw data to improve model performance. G: Gradient Descent - An optimization algorithm used to minimize the error of a model by adjusting its parameters iteratively. H: Hypothesis Testing - A statistical method used to make inferences about a population based on sample data. I: Imputation - The process of replacing missing values in a dataset with estimated values. J: Joint Probability - The probability of the intersection of two or more events occurring simultaneously. K: K-Means Clustering - A popular unsupervised machine learning algorithm used for clustering data points into groups. L: Logistic Regression - A statistical model used for binary classification tasks. M: Machine Learning - A subset of artificial intelligence that enables systems to learn from data and improve performance over time. N: Neural Network - A computer system inspired by the structure of the human brain, used for various machine learning tasks. O: Outlier Detection - The process of identifying observations in a dataset that significantly deviate from the rest of the data points. P: Precision and Recall - Evaluation metrics used to assess the performance of classification models. Q: Quantitative Analysis - The process of using mathematical and statistical methods to analyze and interpret data. R: Regression Analysis - A statistical technique used to model the relationship between a dependent variable and one or more independent variables. S: Support Vector Machine - A supervised machine learning algorithm used for classification and regression tasks. T: Time Series Analysis - The study of data collected over time to detect patterns, trends, and seasonal variations. U: Unsupervised Learning - Machine learning techniques used to identify patterns and relationships in data without labeled outcomes. V: Validation - The process of assessing the performance and generalization of a machine learning model using independent datasets. W: Weka - A popular open-source software tool used for data mining and machine learning tasks. X: XGBoost - An optimized implementation of gradient boosting that is widely used for classification and regression tasks. Y: Yarn - A resource manager used in Apache Hadoop for managing resources across distributed clusters. Z: Zero-Inflated Model - A statistical model used to analyze data with excess zeros, commonly found in count data. Best Data Science & Machine Learning Resources: https://topmate.io/coding/914624 Credits: https://t.me/datasciencefun Like if you need similar content 😄👍 Hope this helps you 😊0,30%
  • 23 июл.If you’re a student, graduate, or someone looking for a career switch, read this. Most people spend months watching random YouTube videos and still don’t become job-ready. Instead, learn in a structured offline classroom. 📌 Data Analytics with GenAI 📌 Python + SQL + Power BI 📌 6-Month Program 📌 1:1 Mentorship 📌 Job Assistance 📍Now available in your city. Seats are limited. 👉 Register Here: https://lp.pwskills.com/data-analytics-course-offline-batch0?utm_source=telegram&utm_medium=influencer&utm_campaign=daoffline0,29%
  • 24 июл.We’re looking for an experienced Data Scientist to support a pricing-strategy project. 📍 Berlin — 2 days per week in the office ⏳ 3–4-month contract 📅 Start: ASAP 🤝 Direct contract with the client You’ll need strong Python and SQL skills, experience building predictive models and simulations, and the ability to explain complex insights clearly. Experience in fintech, insurance, pricing, or other complex systems would be ideal. We’re looking for someone highly analytical, commercially minded, and a great communicator. Interested anyone know someone or share your updated resume marat@zerotoonesearch.com0,28%
  • 7 авг.Lanmea is hiring Role: SWE and AI Internship Batch: 2026/27 passouts Link: https://www.lanmea.com/careers/ai-software-engineering-intern 👉WhatsApp Channel: https://whatsapp.com/channel/0029VaI5CV93AzNUiZ5Tt226 👉Telegram Link: https://t.me/addlist/4q2PYC0pH_VjZDk5 Like for more job opportunities ❤️0,25%
  • 4 авг.Two Scholarships Open Now — Airtel (UG Engineering) & IDFC FIRST Bank (MBA) Two separate scholarship programs are currently accepting applications for Indian students. Scholarship 1: Bharti Airtel Scholarship 2026-27 Who can apply: First year UG or 5 year integrated engineering students at Top 50 NIRF ranked institutes Fields: ECE, Telecom, IT, CS, Data Science, Aerospace, Emerging Tech Indian citizen and resident Family income under ₹8.5 lakh per annum What you get: 100% tuition, hostel and mess fees, plus a free laptop (first year only, one time) Deadline: July 31, 2026 Apply here: https://bhartiairtelfoundation.org/bharti-airtel-scholarship Scholarship 2: IDFC FIRST Bank MBA Scholarship 2026-28 Who can apply: First year students of a 2 year full time MBA/PGDM program at listed eligible colleges Family income under ₹6 lakh per annum Age 35 or below on date of application Valid mobile number linked to Aadhaar What you get: ₹2 lakh total over 2 years (₹1 lakh per year) toward MBA tuition fees only. Does not cover hostel, mess, books, or travel Deadline: August 8, 2026 Apply here: https://www.idfcfirstbank.com/csr-activities/educational-initiatives/mba-scholarship Note: IDFC FIRST Bank does not charge any application fee at any stage. If you need more such type of content then do let me know by responding to this message. 😊0,21%
  • 22 июл.Here’s a solid 𝗕𝗘𝗛𝗔𝗩𝗜𝗢𝗥𝗔𝗟 𝗥𝗢𝗨𝗡𝗗 𝗧𝗜𝗣 to boost your chances to nail that job offer! Technical skills might get you through initial rounds, but behavioral rounds are where many stumble — especially with senior managers who really want to know if you fit the team. Here’s how to ace it: 1️⃣ When HR shares your interviewer's name, hunt for their LinkedIn profile. 2️⃣ Check out their work history and interests to find common ground. 3️⃣ Mention something relevant during the chat — it shows you’ve done your homework and builds rapport. 4️⃣ Remember, this round is two-way: they’re checking if you suit their culture, and you’re seeing if they suit your career goals. 5️⃣ So, ask smart questions about the role and company culture — it proves you’re genuinely interested. 💡 𝗣𝗿𝗼 𝘁𝗶𝗽: Stay polite but confident; senior leaders love that mix!0,17%
  • 12 июн.We're Hiring: AI Engineer Intern Are you passionate about AI, Generative AI, and building intelligent applications? We're looking for an AI Engineer Intern with knowledge of: ✅ Python ✅ LLMs (OpenAI, Claude, Gemini, Llama) ✅ Prompt Engineering & RAG ✅ REST APIs & Git ✅ LangChain, LangGraph, LlamaIndex, CrewAI, or Hugging Face (preferred) What you'll work on: 🔹 AI Agents & Workflow Automation 🔹 LLM-Powered Applications 🔹 Vector Databases (Chroma, FAISS, Milvus) 🔹 AI Integrations & Real-World Projects This is a great opportunity to gain hands-on experience in production-grade AI development and work on cutting-edge technologies. 📩 Interested candidates can share their resume at Simran@massistcrm.com0,15%
  • 16 июл.Aaj hi ek certified Hackar bano!💻 Shuru se saari cheeze seekho bilkul basic se!! PW skills leke aaya h certified Ethical Hacking ka course!! Isme milega : ✅ Hands on Practice ✅ LIVE Hacking Labs ✅ Certificate after Completion Sirf Rs 4999 mai Abhi enroll karo HACK30 Coupon code use karke 30% OFF milega! Enroll NOW : https://pwskills.com/web-development/certified-ethical-hacking-course-035473/?source=pwskills.com&position=course_dropdown&from=home_page&utm_source=pwskills&utm_medium=telegram&utm_campaign=ethical_hacking0,13%
  • 30 июн.Top companies currently hiring data analysts Based on the current job market, here are the top companies hiring data analysts: ## Top Tech Companies - Meta: Investing heavily in AI with significant GPU investments - Amazon: Offers diverse data analyst roles with complex responsibilities - Google (Alphabet): Leverages massive data ecosystems - JP Morgan Chase & Co.: Strong focus on data-driven banking transformation ## Specialized Data Analytics Firms - Tiger Analytics: Specializes in AI/ML solutions - SG Analytics: Provides data-driven insights - Monte Carlo Data: Focuses on data observability - CB Insights: Excels in market intelligence ## Emerging Opportunities Companies like Samsara, ScienceSoft, and Forage are also actively recruiting data analysts, offering competitive salaries ranging from $85,000 to $207,000 annually. Share with credits: https://t.me/sqlspecialist Hope it helps :)0,13%