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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

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📈 Analytical overview of Telegram channel Data Careers Resources & Job Updates | iamrupnath

Channel Data Careers Resources & Job Updates | iamrupnath (@codewithrup) in the English language segment is an active participant. Currently, the community unites 20 615 subscribers, ranking 6 226 in the Technologies & Applications category and 19 817 in the India region.

📊 Audience metrics and dynamics

Since its creation on невідомо, the project has demonstrated rapid growth, gathering an audience of 20 615 subscribers.

According to the latest data from 30 September, 2026, the channel demonstrates stable activity. Although there has been a change in the number of participants by -370 over the last 30 days and by -8 over the last 24 hours, overall reach remains high.

  • Verification status: Not verified
  • Engagement rate (ER): The average audience engagement rate is 3.89%. Within the first 24 hours after publication, content typically collects 1.11% reactions from the total number of subscribers.
  • Post reach: On average, each post receives 802 views. Within the first day, a publication typically gains 228 views.
  • Reactions and interaction: The audience actively supports content: the average number of reactions per post is 1.
  • Thematic interests: Content is focused on key topics such as apply, qualification, bachelor, degree, engineer.

📝 Description and content policy

The author describes the resource as a platform for expressing subjective opinions:
“👉 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”

Thanks to the high frequency of updates (latest data received on 01 October, 2026), the channel maintains relevance and a high level of publication reach. Analytics show that the audience actively interacts with content, making it an important point of influence in the Technologies & Applications category.

20 615
Subscribers
-824 hours
-957 days
-37030 days
Posts Archive
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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

Data Analytics Data analytics is the process of examining data to draw conclusions, make informed decisions, and drive business growth. It involves various techniques, tools, and methodologies to transform raw data into actionable insights. Types of Data Analytics: 1. Descriptive Analytics (what happened) 2. Diagnostic Analytics (why it happened) 3. Predictive Analytics (what might happen) 4. Prescriptive Analytics (what should happen) Data Analytics Applications: 1. Business Intelligence (BI) 2. Customer Relationship Management (CRM) 3. Market Research 4. Operations Optimization 5. Healthcare Analytics 6. Financial Analysis 7. Cybersecurity Data Analytics Tools: 1. Excel 2. Tableau 3. Power BI 4. Python libraries (Pandas, NumPy, Matplotlib) 5. R programming language 6. SQL 7. Machine Learning frameworks (TensorFlow, PyTorch) Data Analytics Techniques: 1. Data Mining 2. Statistical Modeling 3. Data Visualization 4. Regression Analysis 5. Clustering 6. Decision Trees 7. Neural Networks Career Paths in Data Analytics: 1. Data Analyst 2. Data Scientist 3. Business Analyst 4. Data Engineer 5. Data Architect 6. Quantitative Analyst 7. Marketing Analyst Skills Required: 1. Mathematical and statistical knowledge 2. Programming skills (Python, R, SQL) 3. Data visualization expertise 4. Communication and storytelling 5. Business acumen 6. Analytical thinking 7. Problem-solving Would you like to explore: 1. A specific data analytics tool or technique? 2. Real-world applications or case studies? 3. Career development in data analytics? 4. Data science and machine learning? Let me know, and I'll provide more insights.

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