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Data Careers Resources & Job Updates | iamrupnath

Data Careers Resources & Job Updates | iamrupnath

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👉 Connect LinkedIn : https://www.linkedin.com/in/rupnath-shaw Google Search => Techcompreviews IG: @iamrupnath Perfect channel for Data Careers, Job Updates Learn Excel, SQL, Python, Tableau, Power BI, AI tools, AI tips & tricks and many more

إظهار المزيد

📈 نظرة تحليلية على قناة تيليجرام Data Careers Resources & Job Updates | iamrupnath

تُعد قناة Data Careers Resources & Job Updates | iamrupnath (@codewithrup) في القطاع اللغوي الإنكليزية لاعباً نشطاً. يضم المجتمع حالياً 20 610 مشتركاً، محتلاً المرتبة 6 226 في فئة التكنولوجيات والتطبيقات والمرتبة 19 817 في منطقة الهند.

📊 مؤشرات الجمهور والحراك

منذ تأسيسه في невідомо، حقق المشروع نمواً سريعاً وجمع 20 610 مشتركاً.

بحسب آخر البيانات بتاريخ 30 سبتمبر, 2026، تحافظ القناة على نشاط مستقر. خلال آخر 30 يوماً تغيّر عدد الأعضاء بمقدار -370، وفي آخر 24 ساعة بمقدار -8، مع بقاء الوصول العام مرتفعاً.

  • حالة التحقق: غير موثّقة
  • معدل التفاعل (ER): يبلغ متوسط تفاعل الجمهور 3.89‎%. وخلال أول 24 ساعة من النشر يحصد المحتوى عادةً 1.11‎% من ردود الفعل نسبةً إلى إجمالي المشتركين.
  • وصول المنشورات: يحصل كل منشور على متوسط 802 مشاهدة. وخلال اليوم الأول يجمع عادةً 228 مشاهدة.
  • التفاعلات والاستجابة: يتفاعل الجمهور بانتظام؛ متوسط التفاعلات لكل منشور يبلغ 1.
  • الاهتمامات الموضوعية: يركز المحتوى على مواضيع رئيسية مثل apply, qualification, bachelor, degree, engineer.

📝 الوصف وسياسة المحتوى

يصف المؤلف القناة بأنها مساحة للتعبير عن الآراء الذاتية:
“👉 Connect LinkedIn : https://www.linkedin.com/in/rupnath-shaw Google Search => Techcompreviews IG: @iamrupnath Perfect channel for Data Careers, Job Updates Learn Excel, SQL, Python, Tableau, Power BI, AI tools, AI tips & tricks and many more”

بفضل وتيرة التحديث المرتفعة (أحدث البيانات بتاريخ 01 أكتوبر, 2026) تحافظ القناة على حداثتها ومستوى وصول مرتفع. وتُظهر التحليلات تفاعلاً نشطاً من الجمهور، ما يجعلها نقطة تأثير مهمة ضمن فئة التكنولوجيات والتطبيقات.

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-37030 أيام
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Here's a suggested 4-day, 2-hours-per-day step-by-step plan for revising Python for data analysis: Day 1: Python Basics and Data Structures (2 hours) 1. Review Python basics (30 minutes): - Variables, data types, operators - Control structures (if/else, for loops, while loops) - Functions 2. Data Structures (45 minutes): - Lists: indexing, slicing, methods (append, extend, sort) - Tuples: creation, indexing, slicing - Dictionaries: creation, key-value pairs, methods (get, items, keys) - Sets: creation, operations (union, intersection, difference) 3. Practice exercises (45 minutes): - LeetCode, HackerRank, or (link unavailable) exercises Day 2: NumPy, Pandas, and Data Manipulation (2 hours) 1. NumPy (45 minutes): - Arrays: creation, indexing, slicing, operations (basic math, aggregation) - Array methods (reshape, transpose, concatenate) 2. Pandas (45 minutes): - DataFrames: creation, indexing, selecting data - Data manipulation: filtering, grouping, sorting - Merging and joining DataFrames 3. Practice exercises (30 minutes): - Pandas tutorials, DataCamp exercises Day 3: Data Visualization and File Handling (2 hours) 1. Data Visualization (45 minutes): - Matplotlib: plotting basics, customizing plots - Seaborn: visualization library, plotting distributions 2. File Handling (45 minutes): - Reading and writing CSV files (pandas) - Reading and writing Excel files (pandas) - Handling JSON files (json library) 3. Practice exercises (30 minutes): - Visualization exercises, DataCamp projects Day 4: Data Analysis and Machine Learning (2 hours) 1. Data Analysis (45 minutes): - Descriptive statistics (mean, median, mode) - Inferential statistics (hypothesis testing) - Data correlation and regression 2. Machine Learning (45 minutes): - Scikit-learn: introduction, basic algorithms (linear regression, decision trees) - Model evaluation metrics (accuracy, precision, recall) 3. Practice exercises (30 minutes): - Scikit-learn tutorials, Kaggle competitions Additional Tips: - Practice with real-world datasets (e.g., UCI Machine Learning Repository) - Use Jupyter Notebooks or Google Colab for interactive coding - Review documentation and official tutorials for each library - Join online communities (e.g., Reddit's r/learnpython, r/dataanalysis) for support Resources: - w3school - DataCamp - Kaggle - UCI Machine Learning Repository Stay focused, and you'll be well-prepared for data analysis with Python! Would you like me to suggest specific exercises or projects? Join WhatsApp Channel - https://whatsapp.com/channel/0029VaAVyoAEawe0EEAA4d1h

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