Data Analyst Interview Resources
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Show more📈 Analytical overview of Telegram channel Data Analyst Interview Resources
Channel Data Analyst Interview Resources (@dataanalystinterview) in the English language segment is an active participant. Currently, the community unites 52 643 subscribers, ranking 3 266 in the Education category and 6 692 in the India region.
📊 Audience metrics and dynamics
Since its creation on невідомо, the project has demonstrated rapid growth, gathering an audience of 52 643 subscribers.
According to the latest data from 02 September, 2026, the channel demonstrates stable activity. Although there has been a change in the number of participants by 7 over the last 30 days and by -7 over the last 24 hours, overall reach remains high.
- Verification status: Not verified
- Engagement rate (ER): The average audience engagement rate is 1.84%. Within the first 24 hours after publication, content typically collects 0.82% reactions from the total number of subscribers.
- Post reach: On average, each post receives 970 views. Within the first day, a publication typically gains 430 views.
- Reactions and interaction: The audience actively supports content: the average number of reactions per post is 2.
- Thematic interests: Content is focused on key topics such as sql, row, |--, dataset, visualization.
📝 Description and content policy
The author describes the resource as a platform for expressing subjective opinions:
“Join our telegram channel to learn how data analysis can reveal fascinating patterns, trends, and stories hidden within the numbers! 📊
For ads & suggestions: @love_data”
Thanks to the high frequency of updates (latest data received on 03 September, 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.
NULLs, constraints
🧠 Interview Tip: Be able to explain Primary vs Foreign Key.
2️⃣ Basic Queries
🔹 SELECT, FROM, WHERE, ORDER BY, LIMIT
🧠 Practice: Filter and sort data by multiple columns.
3️⃣ Joins – Very Frequently Asked!
🔹 INNER, LEFT, RIGHT, FULL OUTER JOIN
🧠 Interview Tip: Explain the difference with examples.
🧪 Practice: Write queries using joins across 2–3 tables.
4️⃣ Aggregations & GROUP BY
🔹 COUNT, SUM, AVG, MIN, MAX, HAVING
🧠 Common Question: Total sales per category where total > X.
5️⃣ Window Functions
🔹 ROW_NUMBER(), RANK(), DENSE_RANK(), LAG(), LEAD()
🧠 Interview Favorite: Top N per group, previous row comparison.
6️⃣ Subqueries & CTEs
🔹 Write queries inside WHERE, FROM, and using WITH
🧠 Use Case: Filtering on aggregated data, simplifying logic.
7️⃣ CASE Statements
🔹 Add logic directly in SELECT
🧠 Example: Categorize users based on spend or activity.
8️⃣ Data Cleaning & Transformation
🔹 Handle NULLs, format dates, string manipulation (TRIM, SUBSTRING)
🧠 Real-world Task: Clean user input data.
9️⃣ Query Optimization Basics
🔹 Understand indexing, query plan, performance tips
🧠 Interview Tip: Difference between WHERE and HAVING.
🔟 Real-World Scenarios
🧠 Must Practice:
• Sales funnel
• Retention cohort
• Churn rate
• Revenue by channel
• Daily active users
🧪 Practice Platforms
• LeetCode (Easy–Hard SQL)
• StrataScratch (Real business cases)
• Mode Analytics (SQL + Visualization)
• HackerRank SQL (MCQs + Coding)
💼 Final Tip:
Explain why your query works, not just what it does. Speak your logic clearly.
💬 Tap ❤️ for more!