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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
Больше📈 Аналитический обзор Telegram-канала Data Analyst Interview Resources
Канал Data Analyst Interview Resources (@dataanalystinterview) языкового сегмента Английский является активным участником. Сейчас сообщество объединяет 52 280 подписчиков, занимая 3 330 место в категории Образование и 7 186 место в регионе Индия.
📊 Показатели аудитории и динамика
С момента создания невідомо проект демонстрирует стремительный рост, собрав аудиторию из 52 280 подписчиков.
Согласно последним данным от 11 июня, 2026, канал показывает стабильную активность. За последние 30 дней изменение числа участников составило 247, а за последние 24 часа — 13, при этом общий охват остаётся высоким.
- Статус верификации: Не верифицирован
- Уровень вовлечённости (ER): Средний показатель вовлечённости аудитории составляет 2.55%. В первые 24 часа после публикации контент обычно набирает 0.92% реакций от общего числа подписчиков.
- Охват публикаций: В среднем каждый пост получает 1 332 просмотров. В течение первых суток публикация набирает 479 просмотров.
- Реакции и взаимодействия: Аудитория активно поддерживает контент: среднее количество реакций на один пост — 3.
- Тематические интересы: Контент сосредоточен на ключевых темах, таких как sql, row, |--, dataset, visualization.
📝 Описание и контентная политика
Автор описывает ресурс как площадку для выражения субъективного мнения:
“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”
Благодаря высокой частоте обновлений (последние данные получены 12 июня, 2026) канал поддерживает актуальность и высокий уровень охвата публикаций. Аналитика показывает, что аудитория активно взаимодействует с контентом, что делает его важной точкой влияния в категории Образование.
52 280
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+677 дней
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🗄️ SQL Developer Roadmap
📂 SQL Basics (SELECT, WHERE, ORDER BY)
∟📂 Joins (INNER, LEFT, RIGHT, FULL)
∟📂 Aggregate Functions (COUNT, SUM, AVG)
∟📂 Grouping Data (GROUP BY, HAVING)
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∟📂 Data Modification (INSERT, UPDATE, DELETE)
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∟📂 Stored Procedures & Functions
∟📂 Transactions & Locks
∟📂 Views & Triggers
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∟📂 Working with NoSQL basics (optional)
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∟✅ Apply for SQL Dev Roles
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Uber Business Analyst Interview: 1-3 Years Experience
SQL Queries:
1. Develop an SQL query to retrieve the third transaction for each user, including user ID, transaction amount, and date.
2. Compute the average driver rating for each city using data from the rides and ratings tables.
3. Construct an SQL query to identify users registered with Gmail addresses from the 'users' database.
4. Define database denormalization.
5. Analyze click-through conversion rates using data from the
ad_clicks and cab_bookings tables.
6. Define a self-join and provide a practical application example.
Scenario-Based Question:
1. Determine the probability that at least two of three recommended driver routes are the fastest, assuming a 70% success rate for each route.
Guesstimate Questions:
1. Estimate the number of Uber drivers operating in Delhi.
2. Estimate the daily departure volume of Uber vehicles from Bengaluru Airport.
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Data Analyst interview questions
1) What joins are mostly used in SQL?
2) Use cases of Cross and Self Joins?
3) Write a query to exclude weekends from a table?
4) What are Window Functions?
5) What is the difference between CTEs and Subqueries?
6) How can you optimize SQL queries?
7) How can you convert data from rows into columns?
8) If there are 10 different KPIs calculated from different tables on a daily basis, how would you compile them into a single report?
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✅ Top 7 Must-Prepare Topics for Data Analyst Interviews (2025 Edition) 📊🕵️♂️
1️⃣ SQL Mastery
⦁ Joins, subqueries, window functions
⦁ Aggregations & groupings
⦁ Query optimization & data manipulation
2️⃣ Excel Skills
⦁ Pivot tables & charts
⦁ VLOOKUP, INDEX-MATCH
⦁ Data cleaning & conditional formatting
3️⃣ Data Visualization
⦁ Tools: Power BI, Tableau basics
⦁ Creating dashboards & reports
⦁ Storytelling with data
4️⃣ Statistics & Probability
⦁ Descriptive stats (mean, median, mode)
⦁ Probability concepts & distributions
⦁ Hypothesis testing & confidence intervals
5️⃣ Data Cleaning & Wrangling
⦁ Handling missing values & outliers
⦁ Data validation & transformation
⦁ Working with messy datasets
6️⃣ Basic Programming (Python/R)
⦁ Data manipulation with Pandas/R tidyverse
⦁ Writing functions & automation scripts
⦁ Simple EDA (exploratory data analysis)
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