Data Science
Your Data Science adventure made more exciting. A Perfect Combination of Series of Free Data Science tutorials, practicals and projects. P.S. - The tutorials are arranged with relevant topics next to each other so you can follow them in order.
- 24 часа
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- 7 дней
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- 30 дней
- −52
- Реакции
- 0
- Репосты
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- Комментарии
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Динамика подписчиков
Подписчиков у канала «Data Science»: 5 264. Отток за последние 30 дней: 52 (-1 %).
По дням, последние 30 дней
| Дата | Подписчики | Изменение | Посты | Ср. просмотры поста |
|---|---|---|---|---|
| 11 ноя | 5 264 | −2 | 0 | – |
| 10 ноя | 5 266 | +1 | 0 | – |
| 9 ноя | 5 265 | −1 | 0 | – |
| 8 ноя | 5 266 | −47 | 0 | – |
| 14 окт | 5 313 | – | 1 | 320 |
Когда публикует
Время по UTC. 7 постов за 90 дней.
Последние посты
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🚀 Data Science Is More Than Just Python! Different data roles require different skills and tools. Here's a quick roadmap to help you get started: 🔹 Data Analyst: Excel, SQL, Power BI, Tableau 🔹 Data Scientist: Python, Pandas, NumPy, Scikit-learn 🔹 ML Engineer: Python, TensorFlow, PyTorch, Docker 🔹 Data Engineer: SQL, Python, Spark, Airflow, Kafka 🔹 AI Engineer: PyTorch, Hugging Face, LangChain, AI APIs 🔹 Business Analyst: Excel, SQL, Power BI, Tableau 🔹 Statistician: R, Python, SAS, SPSS 🔹 Data Architect: AWS, Azure, GCP, Snowflake 🔹 AI/ML Research Scientist: Python, PyTorch, JAX 🔹 Big Data Engineer: Hadoop, Spark, Hive, Flink 📌 How to use this roadmap? ✅ Choose a r...
275 просмотров Фото -
📊 Data Roles Compared Which Career Path Is Right for You? Explore five popular data careers and their key responsibilities: 🔹 Data Analyst: Analyzes data using SQL, Excel, and Power BI to generate business insights. 🔹 Data Engineer: Builds data pipelines and infrastructure using Python, SQL, and Spark. 🔹 Data Scientist: Uses statistics and machine learning to discover patterns and make predictions. 🔹 Machine Learning Engineer: Develops and deploys ML models for intelligent applications. 🔹 Data Architect: Designs scalable, secure, and efficient data systems. 💡 Career Tip: Learn Python, SQL, and problem-solving, then choose a specialization that matches your interests. 🚀 Which...
320 просмотров Фото -
🚀 The AI Stack Is Bigger Than Just LLMs Building production-ready AI isn’t just about choosing a powerful LLM. It requires multiple layers working together: 🔹 LLMs: Qwen, Claude, Gemini, Llama, OpenAI 🔹 Vector DBs: Pinecone, Weaviate, Qdrant, Milvus 🔹 Embeddings: OpenAI, Cohere, Voyage AI, SBERT 🔹 Data Extraction: Docling, Firecrawl, LlamaParse 🔹 Open LLM Access: Hugging Face, Groq, Ollama 🔹 Frameworks: LangChain, LlamaIndex, Haystack 🔹 Evaluation: Ragas, Giskard, TruLens 💡 Key Takeaway: AI engineering is not just about using LLMs. Understanding data, retrieval, orchestration, tools, and evaluation is essential for building reliable AI applications. 👉 Learn the entire sta...
361 просмотров Фото -
📊 4 Levels of Data Analytics — Where Do You Fit? Data analytics goes beyond dashboards. It’s about turning data into better business decisions. 🔹 Level 1 — Descriptive: What happened? • Reports & dashboards • Performance tracking 🔹 Level 2 — Diagnostic: Why did it happen? • Root cause analysis • Data segmentation 🔹 Level 3 — Predictive: What will happen? • Forecasting • Machine Learning 🔹 Level 4 — Prescriptive: What should we do? • Optimization • Recommendations 💡 Career Tip: Build strong foundations in SQL, reporting, visualization, and business understanding, then progress toward statistics, predictive modeling, and advanced analytics. 🎯 The goal isn’t simply to reach Level...
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🤖 𝗔𝗴𝗲𝗻𝘁𝗶𝗰 𝗔𝗜: 𝗪𝗵𝗮𝘁 𝗟𝗲𝗮𝗱𝗲𝗿𝘀 𝗡𝗲𝗲𝗱 𝘁𝗼 𝗞𝗻𝗼𝘄 AI is moving beyond generating content toward systems that can plan, use tools, take actions, and adapt. 🔹 AI & ML → Prediction, classification & reasoning 🔹 Generative AI → Creates text, code, images & more 🔹 AI Agents → Execute multi-step tasks using tools 🔹 Agentic AI → Coordinates agents, tools, memory, governance & feedback to achieve goals 𝗪𝗵𝗮𝘁 𝗹𝗲𝗮𝗱𝗲𝗿𝘀 𝘀𝗵𝗼𝘂𝗹𝗱 𝗳𝗼𝗰𝘂𝘀 𝗼𝗻: ✅ Business goals ✅ AI autonomy & human oversight ✅ Security & governance ✅ Monitoring & observability ✅ Cost & scalability ✅ Failure recovery 📌 The key shift: Generative AI → Create an answer AI Agents → Complete a task Agentic AI → Achieve a goal through coordi...
444 просмотров Фото -
📊 Data Analyst Career Checklist Becoming a Data Analyst is more than learning tools. Build a combination of technical skills, business knowledge, problem-solving, and communication. 🔹 Excel — Formulas, Lookups, Pivot Tables, Power Query 🔹 SQL — Joins, CTEs, Window Functions, Stored Procedures 🔹 Python — Pandas & DataFrame fundamentals 🔹 Power BI — Power Query, DAX, Data Modeling & Dashboards 🔹 Business Acumen — KPIs, Revenue, Profit & Market Metrics 🔹 AI & Problem-Solving — Use AI to boost productivity, not replace thinking 🔹 Communication — Present insights and recommendations clearly 🔹 Portfolio — Build real-world projects that demonstrate impact 🔹 Resume & LinkedIn — S...
597 просмотров Фото -
📊 Statistics for Data Science: 10 Must-Know Concepts Statistics is the foundation of Data Science. It helps you understand data, identify patterns, measure uncertainty, and make better decisions. 🔟 Essential Concepts: 1️⃣ Mean — Average value 2️⃣ Median — Middle value; useful with outliers 3️⃣ Mode — Most frequent value 4️⃣ Variance — Measures data spread 5️⃣ Standard Deviation — Typical distance from the mean 6️⃣ Probability — Understands uncertainty 7️⃣ Correlation — Measures relationships between variables 8️⃣ Hypothesis Testing — Tests statistical significance 9️⃣ Confidence Interval — Estimates a plausible parameter range 🔟 Regression — Analyzes relationships and enables...
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📊 24 Mathematical Concepts Every Data Scientist Should Understand Data Science isn’t just Python and ML libraries. A strong math foundation helps you understand how models learn, optimize, and make predictions. 🔹 Statistics & Probability • Normal Distribution • Z-Score • Correlation • Entropy • Naive Bayes • MLE 🔹 ML Fundamentals • Gradient Descent • Regression • Sigmoid • ReLU • Softmax • SVM • F1 Score • MSE • Log Loss • R² 🔹 Linear Algebra • Eigenvectors • SVD • Cosine Similarity • PCA 🔹 Optimization & Information Theory • Lagrange Multipliers • KL Divergence • OLS 💡 Don’t just memorize formulas. Understand: What? → Why? → Where? → What problem does it solve? 📌 Save this as...
568 просмотров Фото -
Want to Build a Career in AI & Data Science? Don’t just watch random tutorials. Know what to learn, how to learn & how to become job-ready. 🔥 FREE AI & Data Science Career Masterclass 📅 9th August | 5:30 PM IST 🎯 Discover: ✅ Skills companies are hiring for ✅ AI & Data Science career opportunities ✅ Job-ready learning roadmap ✅ Salary & job-role insights ✅ LIVE guidance from an Industry Expert ⚡️ FREE Registration | Limited Seats 👉 Register Now: https://us06web.zoom.us/meeting/register/NKEVBNGLSpKmNuvdId7JhA Your AI career could start with this 1 session. 🚀
617 просмотров Фото -
🚀 21 Essential Pandas Functions Every Data Analyst Should Know You don't need to memorize hundreds of functions—master these essentials to clean, analyze, and explore data efficiently. 🧹 Data Cleaning: head(), info(), describe(), dropna(), fillna(), rename() 🔍 Filtering: loc[], iloc[], query(), isin() 📊 Aggregation: groupby(), agg(), sum(), mean(), count() 🔗 Data Combining: merge(), concat(), join() 📈 Exploration: value_counts(), pivot_table(), plot() 💡 Note: The image says Top 20 Python Functions, but it actually includes 21 functions. Practice these functions with real datasets, combine them to solve business problems, and you'll build a strong foundation in Python for Data...
609 просмотров Фото
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📊 Data Roles Compared Which Career Path Is Right for You? Explore five popular data careers and their key responsibilities: 🔹 Data Analyst: Analyzes data using SQL, Excel, and Power BI to generate business insights. 🔹 Data Engineer: Builds data pipelines and infrastructure using Python, SQL, and Spark. 🔹 Data Scientist: Uses statistics and machine learning to discover patterns and make predictions. 🔹 Machine Learning Engineer: Develops and deploys ML models for intelligent applications. 🔹 Data Architect: Designs scalable, secure, and efficient data systems. 💡 Career Tip: Learn Python, SQL, and problem-solving, then choose a specialization that matches your interests. 🚀 Which...
320 просмотров Фото -
🚀 Data Science Is More Than Just Python! Different data roles require different skills and tools. Here's a quick roadmap to help you get started: 🔹 Data Analyst: Excel, SQL, Power BI, Tableau 🔹 Data Scientist: Python, Pandas, NumPy, Scikit-learn 🔹 ML Engineer: Python, TensorFlow, PyTorch, Docker 🔹 Data Engineer: SQL, Python, Spark, Airflow, Kafka 🔹 AI Engineer: PyTorch, Hugging Face, LangChain, AI APIs 🔹 Business Analyst: Excel, SQL, Power BI, Tableau 🔹 Statistician: R, Python, SAS, SPSS 🔹 Data Architect: AWS, Azure, GCP, Snowflake 🔹 AI/ML Research Scientist: Python, PyTorch, JAX 🔹 Big Data Engineer: Hadoop, Spark, Hive, Flink 📌 How to use this roadmap? ✅ Choose a r...
275 просмотров Фото
Вопросы о канале «Data Science»
Сколько подписчиков у канала «Data Science»?
У канала «Data Science» (@datasciencemorons) 5 264 подписчика на 12 ноября 2026 г.
Растёт ли канал «Data Science»?
Сейчас нет. Отток за последние 30 дней: 52 (-1 %).
О чём канал «Data Science»?
Канал в категории «Технологии», язык постов: английский. Описание канала: «Your Data Science adventure made more exciting. A Perfect Combination of Series of Free Data Science tutorials, practicals and projects. P.S. - The tutorials are arranged with relevant topics next to...»
Какое место занимает канал «Data Science»?
По числу подписчиков: №117 в категории «Технологии», №3 186 среди каналов на языке «английский», №3 702 в мире.
Статистика обновлена 41 минуту назад.