Available now! Telegram Research 2025 โ the year's key insights 
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 more๐ 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 51 931 subscribers, ranking 3 319 in the Education category and 6 943 in the India region.
๐ Audience metrics and dynamics
Since its creation on ะฝะตะฒัะดะพะผะพ, the project has demonstrated rapid growth, gathering an audience of 51 931 subscribers.
According to the latest data from 26 June, 2026, the channel demonstrates stable activity. Although there has been a change in the number of participants by 399 over the last 30 days and by 10 over the last 24 hours, overall reach remains high.
- Verification status: Not verified
- Engagement rate (ER): The average audience engagement rate is 6.05%. Within the first 24 hours after publication, content typically collects 1.22% reactions from the total number of subscribers.
- Post reach: On average, each post receives 3 142 views. Within the first day, a publication typically gains 634 views.
- Reactions and interaction: The audience actively supports content: the average number of reactions per post is 5.
- Thematic interests: Content is focused on key topics such as analyst, |--, excel, visualization, analytic.
๐ Description and content policy
The author describes the resource as a platform for expressing subjective opinions:
โData Analysis Useful Resources
#dataanalysis
#dataanalysisbooks
#sqlbooks
#pythonbooks
#tableau
#powerbi
#datavisualization
For promotions: @coderfunโ
Thanks to the high frequency of updates (latest data received on 27 June, 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.
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Channel Posts
You donโt need to pay $10,000 to learn data analytics
The best ones are often free.
Here are the free resources I recommend that have proven effective:
๐๐๐ & ๐๐๐ญ๐๐๐๐ฌ๐๐ฌ
โณ Mode SQL Tutorial (interactive): https://lnkd.in/ddy6tUJW
โณ SQLBolt (beginner-friendly): https://sqlbolt.com
โณ W3Schools SQL: https://lnkd.in/e6scAPms
๐๐ฑ๐๐๐ฅ ๐๐จ๐ซ ๐๐๐ญ๐ ๐๐ง๐๐ฅ๐ฒ๐ฌ๐ข๐ฌ
โณ Chandoo's Free 14-Week Course: https://lnkd.in/d2zVWHU5
โณ ExcelIsFun YouTube Channel: https://lnkd.in/dCz7V2Xm
๐๐ฒ๐ญ๐ก๐จ๐ง ๐๐จ๐ซ ๐๐๐ญ๐ ๐๐ง๐๐ฅ๐ฒ๐ฌ๐ข๐ฌ
โณ freeCodeCamp (free certificate): https://lnkd.in/drMQePcp
โณ Kaggle Learn: https://lnkd.in/dAQdczQ9
๐๐๐ญ๐ ๐๐ข๐ฌ๐ฎ๐๐ฅ๐ข๐ณ๐๐ญ๐ข๐จ๐ง
โณ Tableau Public (free): https://lnkd.in/dPj-V6gC
โณ Looker Studio (free): https://lnkd.in/dZj4tc7Z
๐๐จ๐ฆ๐ฉ๐ฅ๐๐ญ๐ ๐๐ซ๐จ๐ ๐ซ๐๐ฆ๐ฌ (๐๐ฎ๐๐ข๐ญ ๐
๐ซ๐๐)
โณ Google Data Analytics Certificate: https://lnkd.in/diTs5J-e
โณ IBM Data Analyst: https://lnkd.in/dvN9AWDN
โณ HubSpot Business Analytics (100% free + certificate): https://lnkd.in/d5RW6KBK
๐๐จ๐ฎ๐๐ฎ๐๐ ๐๐ก๐๐ง๐ง๐๐ฅ๐ฌ ๐ ๐๐๐๐จ๐ฆ๐ฆ๐๐ง๐
โณ Alex The Analyst: https://lnkd.in/dDt2HRMx
โณ Codebasics: https://lnkd.in/de8dg4v8
โณ Luke Barousse: https://lnkd.in/dDm_2GAF
โณ Data with Baraa: https://lnkd.in/dPRB2hAV
๐๐ซ๐๐๐ญ๐ข๐๐ ๐ฐ๐ข๐ญ๐ก ๐๐๐๐ฅ ๐๐๐ญ๐
โณ Kaggle Datasets: https://lnkd.in/ee9wkuxr
โณ Google Dataset Search: https://lnkd.in/ezaHtmxs
๐๐ซ๐จ ๐ญ๐ข๐ฉ: Start with SQL + Excel โ Add Python โ Then visualization tools.
| 2 | โ
๐ค AโZ of Data Analyst ๐๐ผ
A โ Analytics
The process of analyzing data to discover insights and support decision-making.
B โ Business Intelligence (BI)
Technologies and tools used to analyze business data (Power BI, Tableau).
C โ Cleaning (Data Cleaning)
Removing errors, duplicates, and inconsistencies from data.
D โ Dashboard
A visual display of key metrics and insights.
E โ ETL (Extract, Transform, Load)
Process of collecting, cleaning, and storing data for analysis.
F โ Forecasting
Predicting future trends using historical data.
G โ Group By
A method to organize data into categories for analysis.
H โ Hypothesis Testing
Testing assumptions using statistical methods.
I โ Insight
Meaningful information derived from data analysis.
J โ Join
Combining data from multiple tables (SQL concept).
K โ KPI (Key Performance Indicator)
A measurable value showing business performance.
L โ Linear Regression
A statistical method used to predict relationships between variables.
M โ Metrics
Quantifiable measures used to track performance.
N โ Normalization
Organizing data to reduce redundancy and improve efficiency.
O โ Outlier
A data point significantly different from others.
P โ Pivot Table
A tool used to summarize and analyze data quickly.
Q โ Query
A request to retrieve data from a database.
R โ Reporting
Presenting data insights through charts and summaries.
S โ SQL
Language used to manage and analyze structured data.
T โ Trend Analysis
Identifying patterns or changes over time.
U โ Unstructured Data
Data without predefined format (text, images).
V โ Visualization
Representing data using charts or graphs.
W โ Warehousing (Data Warehouse)
Central storage of large structured datasets.
X โ X-axis
Horizontal axis in charts representing variables.
Y โ YoY (Year-over-Year)
Comparing data from one year to another.
Z โ Z-Score
Statistical measure showing how far a value is from the mean.
Double Tap โฅ๏ธ For More | 1 002 |
| 3 | โ
If you're serious about learning Power BI โ follow this roadmap ๐๐
1. Understand the basics of data visualization: Importance, principles, and best practices ๐จ
2. Get familiar with Power BI components: Power BI Desktop, Power BI Service, and Power BI Mobile ๐ฑ
3. Install Power BI Desktop: Set up your environment to start building reports ๐ฅ๏ธ
4. Learn about data sources: Connect to various data sources (Excel, SQL Server, Web, etc.) ๐
5. Explore the Power Query Editor: Data transformation and cleaning techniques (ETL processes) ๐
6. Understand data modeling concepts: Relationships, tables, and data hierarchies ๐
7. Study DAX (Data Analysis Expressions): Basic formulas and functions for calculations ๐ข
8. Create visualizations: Charts, tables, maps, and custom visuals ๐
9. Learn about interactive features: Slicers, filters, tooltips, and drill-through options ๐
10. Design effective dashboards: Layout, color schemes, and user experience principles ๐๏ธ
11. Explore Power BI Service: Publishing reports, sharing dashboards, and collaboration features ๐
12. Understand row-level security (RLS): Implementing security measures for data access ๐
13. Learn about Power BI apps: Creating and managing apps for users ๐ฆ
14. Explore advanced DAX functions: Time intelligence, CALCULATE, and context transition โณ
15. Familiarize yourself with Power BI Report Server: On-premises reporting solutions ๐ข
16. Integrate with other Microsoft tools: Excel, Teams, and SharePoint for enhanced collaboration ๐
17. Study performance optimization techniques: Improving report performance and efficiency โก
18. Stay updated on new features and updates: Follow the Power BI blog and community forums ๐ฐ
19. Practice with sample datasets: Use resources like Microsoftโs sample data or Kaggle datasets ๐
20. Consider obtaining certifications: Microsoft Certified: Data Analyst Associate ๐
21. Join online communities: Engage with forums like Power BI Community, LinkedIn groups, or Reddit ๐ข
22. Build a portfolio of projects: Showcase your skills with real-world examples and case studies ๐
23. Attend webinars and workshops: Learn from experts and gain insights into best practices ๐ค
24. Experiment with storytelling through data: Craft narratives that convey insights effectively ๐
Tip: Focus on practical applicationโbuild reports based on real business scenarios!
๐ฌ Tap โค๏ธ for more! | 1 212 |
| 4 | How to Crack a Data Analyst Job Faster
1๏ธโฃ Fix Your Resume
- One page, clean layout, show impact (not tools)
- Example: Improved sales reporting accuracy by 18% using SQL & Power BI
- Add links: GitHub, Portfolio, LinkedIn
2๏ธโฃ Prepare Smart for Interviews
- SQL: joins, window functions, CTEs (daily practice)
- Excel: case questions (pivots, formulas)
- Power BI/Tableau: explain one dashboard end-to-end
- Python: pandas (groupby, merge, missing values)
3๏ธโฃ Master Business Thinking
- Ask why the data exists
- Translate numbers into decisions
- Example: High month-2 churn โ poor onboarding
4๏ธโฃ Build a Strong Portfolio
- 3 solid projects > 10 weak ones
- Projects:
- Customer churn analysis
- Sales performance dashboard
- Marketing funnel analysis
5๏ธโฃ Apply With Strategy
- Apply to 5-10 roles daily
- Customize resume keywords
- Reach out to hiring managers (referrals = 3x interviews)
6๏ธโฃ Track Progress
- Maintain interview log
- Fix gaps weekly
๐ฏ Skills get you shortlisted. Thinking gets you hired. | 2 681 |
| 5 |  No text... | 3 128 |
| 6 | A step-by-step guide to land a job as a data analyst
Landing your first data analyst job is toughhhhh.
Here are 11 tips to make it easier:
- Master SQL.
- Next, learn a BI tool.
- Drink lots of tea or coffee.
- Tackle relevant data projects.
- Create a relevant data portfolio.
- Focus on actionable data insights.
- Remember imposter syndrome is normal.
- Find ways to prove youโre a problem-solver.
- Develop compelling data visualization stories.
- Engage with LinkedIn posts from fellow analysts.
- Illustrate your analytical impact with metrics & KPIs.
- Share your career story & insights via LinkedIn posts.
I have curated best 80+ top-notch Data Analytics Resources ๐๐
https://whatsapp.com/channel/0029VaGgzAk72WTmQFERKh02
Hope this helps you ๐ | 3 618 |
| 7 |  No text... | 5 149 |
| 8 |  No text... | 4 890 |
| 9 |  No text... | 4 395 |
| 10 |  No text... | 4 698 |
| 11 |  No text... | 4 304 |
| 12 |  No text... | 3 820 |
| 13 | 7 Misconceptions About Data Analytics (and Whatโs Actually True): ๐๐
โ You need to be a math or statistics genius
โ
Basic math + logical thinking is enough. Most real-world analytics is about understanding data, not complex formulas.
โ You must learn every tool before applying for jobs
โ
Start with core tools (Excel, SQL, one BI tool). Master fundamentals โ tools can be learned on the job.
โ Data analytics is only about numbers
โ
Itโs about storytelling with data โ explaining insights clearly to non-technical stakeholders.
โ You need coding skills like a software developer
โ
Not required. SQL + basic Python/R is enough for most analyst roles. Deep coding is optional, not mandatory.
โ Analysts just make dashboards all day
โ
Dashboards are just one part. Real work includes data cleaning, business understanding, ad-hoc analysis, and decision support.
โ You need huge datasets to be a โrealโ data analyst
โ
Even small datasets can provide powerful insights if the questions are right.
โ Once you learn analytics, your learning is done
โ
Data analytics evolves constantly โ new tools, business problems, and techniques mean continuous learning.
๐ฌ Tap โค๏ธ if you agree | 3 885 |
| 14 | ๐งโ๐ผ Interviewer: What's the difference between VLOOKUP and HLOOKUP in Excel?
๐จโ๐ป Me: VLOOKUP searches vertically down columns (great for column-based data like employee lists), while HLOOKUP searches horizontally across rows (ideal for row-based setups like category headers).
โ Key Differences:
โ VLOOKUP: Looks for a value in the first column of a range, returns from the same row in a specified columnโsyntax: =VLOOKUP(lookup_value, table_array, col_index_num, [range_lookup]). Use for vertical data; e.g., find salary by ID in a table.
โ HLOOKUP: Looks for a value in the first row of a range, returns from the same column in a specified rowโsyntax: =HLOOKUP(lookup_value, table_array, row_index_num, [range_lookup]). Use for horizontal data; e.g., pull metrics by month across a header row.
๐ Example:
Vertical sales table (IDs in col A, amounts in B): VLOOKUP(ID, A:B, 2, FALSE) gets amount.
Horizontal (months in row 1, sales in row 2): HLOOKUP("Jan", 1:3, 2, FALSE) gets Jan sales.
๐ก VLOOKUP's more common (90% of lookups), but both support exact (FALSE) or approx (TRUE) matchesโswitch to XLOOKUP in modern Excel for bidirectional flexibility!
๐ฌ Tap โค๏ธ for more! | 4 230 |
| 15 | Data Analytics Interview Preparation
[Questions with Answers]
How did you get your job?
I was hired after an internship.ย
To get the internship, I prepared a bunch for general Python questions (LeetCode etc.) and studied the basics of machine learning (several different algorithms, how they work, when they're useful, metricsย
to measure their performance, how to train them in practice etc.).ย
To get the internship I had to pass a technical interview as well as a take-home machine learning (ML) exercise. Then, it was just a question of doing a good job in the internship!ย
What are your data related responsibilities in your job?ย
I work on our recommendation system. Itโs deep learning based. I work on a lot of features to try andย
improve it (reinforcement learning & NLP etc). Since I'm in a start-up, it's also up to our team to put the models we design into production. So, after a phase of research & development and model design, in notebooks, it's time to create a real pipeline, by creating scripts.ย
This enables us to define, train, replace, compare and check the status of the models in production. It's basically all in Python, using Keras/TensorFlow, Pandas, Scikit-learn and NumPy. We also do a lot of analysis for the business team to help them compute metrics of interest (related toย
revenue, acquisition etc.). For that, we use an external utility called Metabase. It is is hooked up to our database where we write SQL queries and visualize the results and create dashboards (usingย
Tableau/Looker etc).ย
I would say my role is quite "full-stack" since we are all involved from the phase of R&D to deployment on our cluster.ย
Was it difficult to get this role?
I got hired after an internship. If you come from a scientific background, it's not that hard to transition into data science. All the math is something you will probably have seen already (especially if you'reย
doing maths or physics). So, with some preparation and coding practice, you can start applying to internships.ย
It took me maybe a month or two of preparation to get some basic ideas of the typical Python data stack (Pandas, Keras, SciKit-learn etc) before I started to send out CVs. Then, if you get an internship, try your best to do the best you can and then maybe you'll be hired after!
I have curated best 80+ top-notch Data Analytics Resources ๐๐
https://whatsapp.com/channel/0029VaGgzAk72WTmQFERKh02
Hope it helps :) | 3 724 |
| 16 | ๐๐จ๐ฐ ๐ญ๐จ ๐๐ซ๐๐ฉ๐๐ซ๐ ๐ญ๐จ ๐๐๐๐จ๐ฆ๐ ๐ ๐๐๐ญ๐ ๐๐ง๐๐ฅ๐ฒ๐ฌ๐ญ
๐. ๐๐ฑ๐๐๐ฅ- Learn formulas, Pivot tables, Lookup, VBA Macros.
๐. ๐๐๐- Joins, Windows, CTE is the most important
๐. ๐๐จ๐ฐ๐๐ซ ๐๐- Power Query Editor(PQE), DAX, MCode, RLS
๐. ๐๐ฒ๐ญ๐ก๐จ๐ง- Basics & Libraries(mainly pandas, numpy, matplotlib and seaborn libraries)
5. Practice SQL and Python questions on platforms like ๐๐๐๐ค๐๐ซ๐๐๐ง๐ค or ๐๐๐๐๐ก๐จ๐จ๐ฅ๐ฌ.
6. Know the basics of descriptive statistics(mean, median, mode, Probability, normal, binomial, Poisson distributions etc).
7. Learn to use ๐๐/๐๐จ๐ฉ๐ข๐ฅ๐จ๐ญ ๐ญ๐จ๐จ๐ฅ๐ฌ like GitHub Copilot or Power BI's AI features to automate tasks, generate insights, and improve your projects(Most demanding in Companies now)
8. Get hands-on experience with one cloud platform: ๐๐ณ๐ฎ๐ซ๐, ๐๐๐, ๐จ๐ซ ๐๐๐
9. Work on at least two end-to-end projects.
10. Prepare an ATS-friendly resume and start applying for jobs.
11. Prepare for interviews by going through common interview questions on Google and YouTube.
I have curated top-notch Data Analytics Resources ๐๐
https://whatsapp.com/channel/0029VaGgzAk72WTmQFERKh02
Hope this helps you ๐ | 3 590 |
| 17 | How to Crack a Data Analyst Job Faster
1๏ธโฃ Fix Your Resume
- One page, clean layout, show impact (not tools)
- Example: Improved sales reporting accuracy by 18% using SQL & Power BI
- Add links: GitHub, Portfolio, LinkedIn
2๏ธโฃ Prepare Smart for Interviews
- SQL: joins, window functions, CTEs (daily practice)
- Excel: case questions (pivots, formulas)
- Power BI/Tableau: explain one dashboard end-to-end
- Python: pandas (groupby, merge, missing values)
3๏ธโฃ Master Business Thinking
- Ask why the data exists
- Translate numbers into decisions
- Example: High month-2 churn โ poor onboarding
4๏ธโฃ Build a Strong Portfolio
- 3 solid projects > 10 weak ones
- Projects:
- Customer churn analysis
- Sales performance dashboard
- Marketing funnel analysis
5๏ธโฃ Apply With Strategy
- Apply to 5-10 roles daily
- Customize resume keywords
- Reach out to hiring managers (referrals = 3x interviews)
6๏ธโฃ Track Progress
- Maintain interview log
- Fix gaps weekly
๐ฏ Skills get you shortlisted. Thinking gets you hired. | 3 603 |
| 18 | โ
Excel Text Functions Cheatsheet ๐ง ๐
1๏ธโฃ UPPER โ =UPPER(A1)
๐น Converts text to uppercase
2๏ธโฃ LOWER โ =LOWER(A1)
๐น Converts text to lowercase
3๏ธโฃ PROPER โ =PROPER(A1)
๐น Capitalizes the first letter of each word
4๏ธโฃ CONCAT / TEXTJOIN โ =CONCAT(A1, B1) or =TEXTJOIN(" ", TRUE, A1:A3)
๐น Joins text values
5๏ธโฃ LEFT / RIGHT โ =LEFT(A1, 5) / =RIGHT(A1, 3)
๐น Extracts specific number of characters from the start or end
6๏ธโฃ MID โ =MID(A1, 3, 4)
๐น Extracts text starting at a position
7๏ธโฃ LEN โ =LEN(A1)
๐น Counts characters in a cell
8๏ธโฃ FIND / SEARCH โ =FIND("a", A1) / =SEARCH("a", A1)
๐น Finds the position of a character
๐ฌ Double tap โค๏ธ for more! | 3 927 |
| 19 | Data Analytics isn't rocket science. It's just a different language.
Here's a beginner's guide to the world of data analytics:
1) Understand the fundamentals:
- Mathematics
- Statistics
- Technology
2) Learn the tools:
- SQL
- Python
- Excel (yes, it's still relevant!)
3) Understand the data:
- What do you want to measure?
- How are you measuring it?
- What metrics are important to you?
4) Data Visualization:
- A picture is worth a thousand words
5) Practice:
- There's no better way to learn than to do it yourself.
Data Analytics is a valuable skill that can help you make better decisions, understand your audience better, and ultimately grow your business.
It's never too late to start learning! | 0 |
| 20 | โ
Complete Roadmap to Learn SQL in 2026 ๐
๐ SQL powers 80% of data analytics jobs.
๐ ๐น SQL FOUNDATIONS
๐ฏ 1๏ธโฃ SELECT Basics (Week 1)
- SELECT \*, specific columns
- FROM tables
- WHERE filters
- ORDER BY, LIMIT
๐ข Practice: Query your first dataset today
๐ 2๏ธโฃ Filtering Mastery
- Comparison operators (=, >, BETWEEN)
- Logical: AND, OR, IN
- Pattern matching: LIKE, %
- NULL handling
๐ 3๏ธโฃ Aggregate Power
- COUNT(\*), SUM, AVG, MIN/MAX
- GROUP BY essentials
- HAVING vs WHERE
- DISTINCT counts
๐ ๐ฅ SQL CORE SKILLS
๐ 4๏ธโฃ JOINS (Most Important โญ)
- INNER JOIN (must-know)
- LEFT, RIGHT, FULL JOIN
- Multi-table joins
- Self-joins
โก 5๏ธโฃ Subqueries & CTEs
- Subqueries in WHERE/FROM
- WITH clause (CTEs)
- Multiple CTE chains
- EXISTS/NOT EXISTS
๐ 6๏ธโฃ Window Functions (Game-Changer โญ)
- ROW_NUMBER(), RANK()
- PARTITION BY magic
- LAG/LEAD (trends)
- Running totals
๐จ ๐ ADVANCED SQL MASTERY
โฐ 7๏ธโฃ Date & Time
- DATEADD, DATEDIFF
- DATE_TRUNC, EXTRACT
- Date filtering patterns
- Cohort analysis
๐ค 8๏ธโฃ String Functions
- CONCAT, SUBSTRING
- TRIM, UPPER/LOWER
- LENGTH, REPLACE
๐ค 9๏ธโฃ CASE Statements
- Simple vs searched CASE
- Nested logic
- Policy calculations
โ๏ธ ๐ง PERFORMANCE & JOBS
๐ 1๏ธโฃ0๏ธโฃ Indexing Basics
- CREATE INDEX strategies
- EXPLAIN query plans
- Composite indexes
๐ป 1๏ธโฃ1๏ธโฃ Practice Platforms
- LeetCode SQL (50 problems)
- HackerRank SQL
- StrataScratch (real cases)
- DDIA datasets
๐ฑ 1๏ธโฃ2๏ธโฃ Modern SQL Tools
- pgAdmin (PostgreSQL)
- DBeaver (universal)
- BigQuery Sandbox (free)
- dbt + SQL
๐ผ โก INTERVIEW READY
๐ฏ 1๏ธโฃ3๏ธโฃ Top Interview Questions
- Find 2nd highest salary
- Nth highest records
- Duplicate detection
- Window ranking
๐ 1๏ธโฃ4๏ธโฃ Real Projects
- Sales dashboard queries
- Customer segmentation
- Inventory optimization
- Build GitHub portfolio
๐จ โญ ESSENTIAL SQL TOOLS 2026
- PostgreSQL (free, powerful)
- MySQL Workbench
- BigQuery (cloud-native)
- Snowflake (trial)
1๏ธโฃ5๏ธโฃ FREE RESOURCES
๐ SQLBolt (interactive)
๐ Mode Analytics Tutorial
โก LeetCode SQL 50
๐ฅ DataCamp SQL (free tier)
๐ W3schools
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