Data Analysis Books | Python | SQL | Excel | Artificial Intelligence | Power BI | Tableau | AI Resources
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Data Analysis Useful Resources #dataanalysis #dataanalysisbooks #sqlbooks #pythonbooks #tableau #powerbi #datavisualization For promotions: @coderfun
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📈 Analytical overview of Telegram channel Data Analysis Books | Python | SQL | Excel | Artificial Intelligence | Power BI | Tableau | AI Resources
Channel Data Analysis Books | Python | SQL | Excel | Artificial Intelligence | Power BI | Tableau | AI Resources (@learndataanalysis) in the English language segment is an active participant. Currently, the community unites 53 310 subscribers, ranking 3 304 in the Education category and 6 419 in the India region.
📊 Audience metrics and dynamics
Since its creation on невідомо, the project has demonstrated rapid growth, gathering an audience of 53 310 subscribers.
According to the latest data from 03 November, 2026, the channel demonstrates stable activity. Although there has been a change in the number of participants by 23 over the last 30 days and by -1 over the last 24 hours, overall reach remains high.
- Verification status: Not verified
- Engagement rate (ER): The average audience engagement rate is 7.12%. Within the first 24 hours after publication, content typically collects 1.16% reactions from the total number of subscribers.
- Post reach: On average, each post receives 0 views. Within the first day, a publication typically gains 616 views.
- Reactions and interaction: The audience actively supports content: the average number of reactions per post is 0.
- Thematic interests: Content is focused on key topics such as analyst, |--, excel, visualization, analytic.
📝 Description and content policy
The author describes the resource as a platform for expressing subjective opinions:
“Data Analysis Useful Resources
#dataanalysis
#dataanalysisbooks
#sqlbooks
#pythonbooks
#tableau
#powerbi
#datavisualization
For promotions: @coderfun”
Thanks to the high frequency of updates (latest data received on 04 November, 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 Education category.
53 310
Subscribers
-124 hours
-167 days
+2330 days
Posts Archive
Some basic concepts regarding data and database
Data is representation of the facts, measurements, figures, or concepts in a formalized manner having no
specific meaning.
Database is an organized collection of the data stored and can be accessed electronically in a computer system.
DBMS are software systems that enable users to store, retrieve, define and manage data in a database easily.
RDBMS is a type of DBMS that stores data in a row-based table structure which connects related data elements.
SQL is a database query language used for storing and managing data in RDBMS.
Data Analytics Career Path
Complete Roadmap to become Data Analyst in 2024
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Repost from Machine Learning with Python
This channels is for Programmers, Coders, Software Engineers.
0️⃣ Python
1️⃣ Data Science
2️⃣ Machine Learning
3️⃣ Data Visualization
4️⃣ Artificial Intelligence
5️⃣ Data Analysis
6️⃣ Statistics
7️⃣ Deep Learning
8️⃣ programming Languages
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This channels is for Programmers, Coders, Software Engineers.
0️⃣ Python
1️⃣ Data Science
2️⃣ Machine Learning
3️⃣ Data Visualization
4️⃣ Artificial Intelligence
5️⃣ Data Analysis
6️⃣ Statistics
7️⃣ Deep Learning
8️⃣ programming Languages
✅ https://t.me/addlist/8_rRW2scgfRhOTc0
✅ https://t.me/Python53
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🔟 Project Ideas for a data analyst
Customer Segmentation: Analyze customer data to segment them based on their behaviors, preferences, or demographics, helping businesses tailor their marketing strategies.
Churn Prediction: Build a model to predict customer churn, identifying factors that contribute to churn and proposing strategies to retain customers.
Sales Forecasting: Use historical sales data to create a predictive model that forecasts future sales, aiding inventory management and resource planning.
Market Basket Analysis: Analyze
transaction data to identify associations between products often purchased together, assisting retailers in optimizing product placement and cross-selling.
Sentiment Analysis: Analyze social media or customer reviews to gauge public sentiment about a product or service, providing valuable insights for brand reputation management.
Healthcare Analytics: Examine medical records to identify trends, patterns, or correlations in patient data, aiding in disease prediction, treatment optimization, and resource allocation.
Financial Fraud Detection: Develop algorithms to detect anomalous transactions and patterns in financial data, helping prevent fraud and secure transactions.
A/B Testing Analysis: Evaluate the results of A/B tests to determine the effectiveness of different strategies or changes on websites, apps, or marketing campaigns.
Energy Consumption Analysis: Analyze energy usage data to identify patterns and inefficiencies, suggesting strategies for optimizing energy consumption in buildings or industries.
Real Estate Market Analysis: Study housing market data to identify trends in property prices, rental rates, and demand, assisting buyers, sellers, and investors in making informed decisions.
Remember to choose a project that aligns with your interests and the domain you're passionate about.
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SQL Quiz: https://youtube.com/shorts/fAiC9Kn17cs?si=wvpVptniYe3v3l__
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Career Path for a Data Analyst
Education: Start by earning a bachelor's degree in fields like math, stats, economics, or computer science.
Skills Growth: Learn programming (Python/R), data tools (SQL/Excel), and visualization. Master data analysis basics.
Entry-Level Role: Begin as a Junior Data Analyst. Learn data cleaning, organization, and basic analysis.
Specialization: Deepen your expertise in a specific industry. Explore advanced analytics and visualization tools.
Advanced Analytics: Move up to Senior Data Analyst. Tackle complex projects and predictive modeling.
Machine Learning: Explore machine learning and data modeling techniques. Familiarize yourself with algorithms, and learn how to implement predictive and classification models.
Domain Expertise: Develop expertise in a particular industry, such as healthcare, finance, e-commerce, etc. This knowledge will enable you to provide more valuable insights from data.
Leadership Roles: As you gain experience, you can move into roles like Data Analytics Manager or Data Science Manager, where you'll oversee teams and projects.
Continuous Learning: Stay updated with the latest tools, techniques, and industry trends. Attend workshops, conferences, and online courses to keep your skills relevant.
Networking: Build a strong professional network within the data analytics community. This can open up opportunities and help you stay informed about industry developments.
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