Data Analysis Books | Python | SQL | Excel | Artificial Intelligence | Power BI | Tableau | AI Resources
Open in Telegram
Data Analysis Useful Resources #dataanalysis #dataanalysisbooks #sqlbooks #pythonbooks #tableau #powerbi #datavisualization For promotions: @coderfun
Show more
Network:📈 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 53 322 subscribers, ranking 3 289 in the Education category and 6 571 in the India region.
📊 Audience metrics and dynamics
Since its creation on невідомо, the project has demonstrated rapid growth, gathering an audience of 53 322 subscribers.
According to the latest data from 25 October, 2026, the channel demonstrates stable activity. Although there has been a change in the number of participants by 62 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 6.99%. Within the first 24 hours after publication, content typically collects N/A% reactions from the total number of subscribers.
- Post reach: On average, each post receives 0 views. Within the first day, a publication typically gains 0 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 26 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 Education category.
53 322
Subscribers
-824 hours
-387 days
+6230 days
Posts Archive
Tired of AI that refuses to help?
@UnboundGPT_bot doesn't lecture. It just works.
✓ Multiple models (GPT-4o, Gemini, DeepSeek)
✓ Image generation & editing
✓ Video creation
✓ Persistent memory
✓ Actually uncensored
Free to try → @UnboundGPT_bot or https://ko2bot.com
Sber presented Europe’s largest open-source project at AI Journey as it opened access to its flagship models — the GigaChat Ultra-Preview and Lightning, in addition to a new generation of the GigaAM-v3 open-source models for speech recognition and a full range of image and video generation models in the new Kandinsky 5.0 line, including the Video Pro, Video Lite and Image Lite.
The GigaChat Ultra-Preview, a new MoE model featuring 702 billion parameters, has been compiled specifically with the Russian language in mind and trained entirely from scratch. Read a detailed post from the team here.
For the first time in Russia, an MoE model of this scale has been trained entirely from scratch — without relying on any foreign weights. Training from scratch, and on such a scale to boot, is a challenge that few teams in the world have taken on.
Our flagship Kandinsky Video Pro model has caught up with Veo 3 in terms of visual quality and surpassed Wan 2.2-A14B. Read a detailed post from the team here.
The code and weights for all models are now available to all users under MIT license, including commercial use.
📊 Data Analytics Basics Cheatsheet
1. What is Data Analytics?
Analyzing raw data to find patterns, trends, and insights to support decision-making.
2. Types of Data Analytics:
⦁ Descriptive: What happened?
⦁ Diagnostic: Why did it happen?
⦁ Predictive: What might happen next?
⦁ Prescriptive: What should be done?
3. Key Tools & Languages:
⦁ Excel – Quick analysis & charts
⦁ SQL – Query and manage databases
⦁ Python (Pandas, NumPy, Matplotlib)
⦁ Power BI / Tableau – Dashboards & visualization
4. Data Cleaning Basics:
⦁ Handle missing values
⦁ Remove duplicates
⦁ Convert data types
⦁ Standardize formats
5. Exploratory Data Analysis (EDA):
⦁ Summary stats (mean, median, mode)
⦁ Data distribution
⦁ Correlation matrix
⦁ Visual tools: bar charts, boxplots, scatter plots
6. Data Visualization:
⦁ Use charts to simplify insights
⦁ Choose chart types based on data (line for trends, bar for comparisons, pie for proportions)
7. SQL Essentials:
⦁ SELECT, WHERE, JOIN, GROUP BY, HAVING, ORDER BY
⦁ Aggregate functions: COUNT, SUM, AVG, MAX, MIN
8. Python for Analysis:
⦁ Pandas for dataframes
⦁ Matplotlib/Seaborn for plotting
⦁ Scikit-learn for basic ML models
*9. Metrics to Know:
⦁ Growth %, Conversion rate, Retention rate
⦁ KPIs specific to domain (finance, marketing, etc.)
*10. Real-World Use Cases:
⦁ Customer segmentation
⦁ Sales trend analysis
⦁ A/B testing
⦁ Forecasting demand
💬 Tap ❤️ for more!
Machine Learning Roadmap
PowerBI Interview Questions 🚀🔥
PowerBI_Interview_Kit.pdf5.67 MB
Useful websites to practice and enhance your data analytics skills
👇👇
1. Python
http://learnpython.org
2. SQL
https://www.sql-practice.com/
3. Excel
https://excel-practice-online.com/
4. Power BI
https://www.workout-wednesday.com/power-bi-challenges/
5. Quiz and Interview Questions
https://t.me/sqlspecialist
Haven't shared lot of resources to avoid too much distraction
Just focus on the basics, practice learnings and work on building projects to improve your skills. Thats the best way to learn in my opinion 😄
Join @free4unow_backup for more free courses
ENJOY LEARNING 👍👍
Greetings from PVR Cloud Tech!! 🌈
🚀 Along with our highly successful Azure Data Engineering program, we are now launching a brand-new Data Engineering with Snowflake, DBT, and Airflow training track!
Course: Snowflake + DBT + Airflow
📌 Start Date: 24th Nov 2025
⏰ Time: 8 PM – 9 PM IST | Monday
🔹 Course Content:
https://drive.google.com/file/d/1luKHrhYZ6zKuXZpVPGzMydrU_6R2yQnL/view
📱 Join WhatsApp Group:
https://chat.whatsapp.com/EZghn5PVmryDgJZ1TjIMRk?mode=wwt
📥 Register Now:
https://forms.gle/Vaofd52rkJcUpKPV7
📺 WhatsApp Channel:
https://www.whatsapp.com/channel/0029Vb60rGU8V0thkpbFFW2n
Team
PVR Cloud Tech:)
+91-9346060794
Skills Needed To Become Data Analyst
Hey guys 👋
I was working on something big from last few days.
Finally, I have curated best 80+ top-notch Data Analytics Resources 👇👇
https://topmate.io/analyst/861634
If you go on purchasing these books, it will cost you more than 15000 but I kept the minimal price for everyone's benefit.
I hope these resources will help you in data analytics journey.
I will add more resources here in the future without any additional cost.
All the best for your career ❤️
🎯 2 Power-Packed Courses to Boost Your Tech Career! 💻🚀
Whether you're preparing for placements or starting your coding journey — we’ve got you covered!
✅ 1. DSA Self-Paced Course
📌 Master Data Structures & Algorithms
– Perfect for SDE interviews & competitive coding
– Covers Arrays, Trees, Graphs, DP & more
🔗 Join now: https://gfgcdn.com/tu/W84/
✅ 2. Python Beginner to Advanced
📌 Learn Python from scratch to expert level
– Covers basics, OOPs, file handling, projects & more
🔗 Start here: https://gfgcdn.com/tu/W8D/
🎁 Use Coupon: GFGWINTERARC for 25% OFF (Limited Time!)
These GeeksforGeeks courses are top-rated for 2025 placements—DSA's got 60+ hours of videos, 200+ problems in C++/Python/Java, and real projects to nail interviews at FAANG-level companies! Which one are you grabbing first? 😊
🎯 2 Power-Packed Courses to Boost Your Tech Career! 💻🚀
Whether you're preparing for placements or starting your coding journey — we’ve got you covered!
✅ 1. DSA Self-Paced Course
📌 Master Data Structures & Algorithms
– Perfect for SDE interviews & competitive coding
– Covers Arrays, Trees, Graphs, DP & more
🔗 Join now: https://gfgcdn.com/tu/W84/
✅ 2. Python Beginner to Advanced
📌 Learn Python from scratch to expert level
– Covers basics, OOPs, file handling, projects & more
🔗 Start here: https://gfgcdn.com/tu/W8D/
🎁 Use Coupon:
GFGWINTERARC for 25% OFF (Limited Time!)Free Data Analytics Courses With Certificate
👇👇
https://www.linkedin.com/posts/sql-analysts_dataanalyst-datascience-datacamp-activity-7392164126371958784-cFIc
Double Tap ♥️ For More Free Resources
📊 Core Data Analyst Interview Topics You Should Know ✅
1️⃣ Excel/Spreadsheet Skills
⦁ VLOOKUP, INDEX-MATCH, XLOOKUP (newer Excel fave)
⦁ Pivot Tables for summarizing data
⦁ Conditional Formatting to highlight trends
⦁ Data Cleaning & Validation with formulas like IFERROR
2️⃣ SQL & Databases
⦁ SELECT, JOINs (INNER, LEFT, RIGHT, FULL)
⦁ GROUP BY, HAVING, ORDER BY for aggregations
⦁ Subqueries & Window Functions (ROW_NUMBER, LAG)
⦁ CTEs for cleaner, reusable queries
3️⃣ Data Visualization
⦁ Tools: Power BI, Tableau, Excel, Google Data Studio
⦁ Best practices: Choose charts wisely (bar for comparisons, line for trends)
⦁ Dashboards & Interactivity with slicers/drill-downs
⦁ Storytelling with Data to make insights pop
4️⃣ Statistics & Probability
⦁ Mean, Median, Mode, Standard Deviation for summaries
⦁ Correlation vs. Causation (correlation doesn't imply cause!)
⦁ Hypothesis Testing (t-test, p-value for significance)
⦁ Confidence Intervals to gauge reliability
5️⃣ Python for Data Analysis
⦁ Libraries: Pandas for dataframes, NumPy for arrays, Matplotlib/Seaborn for plots
⦁ Data wrangling & cleaning (handling nulls, merging)
⦁ Basic EDA: Describe stats, visualizations, correlations
6️⃣ Business Understanding
⦁ KPI identification (e.g., conversion rate, churn)
⦁ Funnel analysis for drop-offs
⦁ A/B Testing basics to validate changes
⦁ Decision-making support with actionable recommendations
7️⃣ Problem Solving & Case Studies
⦁ Product metrics (DAU/MAU, retention)
⦁ Customer segmentation (RFM analysis)
⦁ Market trend analysis with time-series
8️⃣ ETL Concepts
⦁ Extract from sources, Transform (clean/aggregate), Load to warehouses
⦁ Data pipeline basics using tools like Airflow or dbt
9️⃣ Data Cleaning Techniques
⦁ Handling missing values (impute or drop)
⦁ Duplicates, outliers detection/removal
⦁ Data formatting (standardize dates, text)
🔟 Soft Skills & Communication
⦁ Explaining insights to non-technical stakeholders simply
⦁ Clear visualization storytelling (avoid clutter)
⦁ Collaborating with cross-functional teams for context
💬 Tap ❤️ for more!
These cover 80% of what you'll face in 2025 interviews—focus on SQL and Python for tech-heavy roles! Which topic are you prepping most? 😊
+7Excel Formulas every data analyst should know
Free Access to our premium Data Science Channel
👇👇
https://whatsapp.com/channel/0029Va4QUHa6rsQjhITHK82y
Amazing premium resources only for my subscribers
🎁 Free Data Science Courses
🎁 Machine Learning Notes
🎁 Python Free Learning Resources
🎁 Learn AI with ChatGPT
🎁 Build Chatbots using LLM
🎁 Learn Generative AI
🎁 Free Coding Certified Courses
Join fast ❤️
ENJOY LEARNING 👍👍
If I had to start learning data analyst all over again, I'd follow this:
1- Learn SQL:
---- Joins (Inner, Left, Full outer and Self)
---- Aggregate Functions (COUNT, SUM, AVG, MIN, MAX)
---- Group by and Having clause
---- CTE and Subquery
---- Windows Function (Rank, Dense Rank, Row number, Lead, Lag etc)
2- Learn Excel:
---- Mathematical (COUNT, SUM, AVG, MIN, MAX, etc)
---- Logical Functions (IF, AND, OR, NOT)
---- Lookup and Reference (VLookup, INDEX, MATCH etc)
---- Pivot Table, Filters, Slicers
3- Learn BI Tools:
---- Data Integration and ETL (Extract, Transform, Load)
---- Report Generation
---- Data Exploration and Ad-hoc Analysis
---- Dashboard Creation
4- Learn Python (Pandas) Optional:
---- Data Structures, Data Cleaning and Preparation
---- Data Manipulation
---- Merging and Joining Data (Merging and joining DataFrames -similar to SQL joins)
---- Data Visualization (Basic plotting using Matplotlib and Seaborn)
Hope this helps you 😊
Grab the deal before it over
Best deal from admin
One month premium of perplexity ai with comet browser worth 200$ for free
No payment no checkout direct redeem.
Open link, login & download comet browser.
Ask anything to comet you get best answers for your learning
https://pplx.ai/bhavinahir537
Python Interview Questions with Answers Part-1: ☑️
1. What is Python and why is it popular for data analysis?
Python is a high-level, interpreted programming language known for simplicity and readability. It’s popular in data analysis due to its rich ecosystem of libraries like Pandas, NumPy, and Matplotlib that simplify data manipulation, analysis, and visualization.
2. Differentiate between lists, tuples, and sets in Python.
⦁ List: Mutable, ordered, allows duplicates.
⦁ Tuple: Immutable, ordered, allows duplicates.
⦁ Set: Mutable, unordered, no duplicates.
3. How do you handle missing data in a dataset?
Common methods: removing rows/columns with missing values, filling with mean/median/mode, or using interpolation. Libraries like Pandas provide
.dropna(), .fillna() functions to do this easily.
4. What are list comprehensions and how are they useful?
Concise syntax to create lists from iterables using a single readable line, often replacing loops for cleaner and faster code.
Example: [x**2 for x in range(5)] → ``
5. Explain Pandas DataFrame and Series.
⦁ Series: 1D labeled array, like a column.
⦁ DataFrame: 2D labeled data structure with rows and columns, like a spreadsheet.
6. How do you read data from different file formats (CSV, Excel, JSON) in Python?
Using Pandas:
⦁ CSV: pd.read_csv('file.csv')
⦁ Excel: pd.read_excel('file.xlsx')
⦁ JSON: pd.read_json('file.json')
7. What is the difference between Python’s append() and extend() methods?
⦁ append() adds its argument as a single element to the end of a list.
⦁ extend() iterates over its argument adding each element to the list.
8. How do you filter rows in a Pandas DataFrame?
Using boolean indexing:
df[df['column'] > value] filters rows where ‘column’ is greater than value.
9. Explain the use of groupby() in Pandas with an example.
groupby() splits data into groups based on column(s), then you can apply aggregation.
Example: df.groupby('category')['sales'].sum() gives total sales per category.
10. What are lambda functions and how are they used?
Anonymous, inline functions defined with lambda keyword. Used for quick, throwaway functions without formally defining with def.
Example: df['new'] = df['col'].apply(lambda x: x*2)
React ♥️ for Part 2Master Power-bi in 15 days 💪🔥
Do not forget to React ❤️ to this Message for More Content Like this
Thanks For Joining All ❤️🙏
