Data Analyst Interview Resources

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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

Сітка:Data Analytics Індія7 186 Освіта3 330...

📈 Аналітичний огляд Telegram-каналу Data Analyst Interview Resources

Канал Data Analyst Interview Resources (@dataanalystinterview) у мовному сегменті Англійська є активним учасником. На даний момент спільнота об'єднує 52 280 підписників, посідаючи 3 330 місце в категорії Освіта та 7 186 місце у регіоні Індія.

📊 Показники аудиторії та динаміка

З моменту свого створення невідомо, проект продемонстрував стрімке зростання, зібравши аудиторію у 52 280 підписників.

За останніми даними від 11 червня, 2026, канал демонструє стабільну активність. Хоча за останні 30 днів спостерігається зміна кількості учасників на 247, а за останні 24 години на 13, загальне охоплення залишається високим.

Статус верифікації: Не верифікований
Рівень залученості (ER): Середній показник залученості аудиторії становить 2.55%. Протягом перших 24 годин після публікації контент зазвичай збирає 0.92% реакцій від загальної кількості підписників.
Охоплення публікацій: В середньому кожен допис отримує 1 332 переглядів. Протягом першої доби публікація в середньому набирає 479 переглядів.
Реакції та взаємодія: Аудиторія активно підтримує контент: середня кількість реакцій на один пост – 3.
Тематичні інтереси: Контент зосереджений навколо ключових тем, таких як sql, row, |--, dataset, visualization.

📝 Опис та контентна політика

Автор описує ресурс як майданчик для висловлення суб'єктивної думки:
“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”

Завдяки високій частоті оновлень (останні дані отримано 12 червня, 2026), канал підтримує актуальність та високий рівень охоплення публікацій. Аналітика показує, що аудиторія активно взаємодіє з контентом, що робить його важливою точкою впливу в категорії Освіта.

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Підписники

+1324 години

+677 днів

+24730 день

1 332

Перегляди допису

~ 47924 години

~ 62248 годин

2.55%

Коефіцієнт залучення

~ 1

Дописів на день

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✅ Data Analyst Interview Questions 📊 🟨 SQL 1️⃣ Write a query to find the second highest salary in the employee table.

SELECT MAX(salary) AS second_highest
FROM employee
WHERE salary < (SELECT MAX(salary) FROM employee);

(Handles ties; alternative: use DENSE_RANK() for modern SQL.) 2️⃣ Get the top 3 products by revenue from sales table.

SELECT product_id, SUM(revenue) AS total_revenue
FROM sales
GROUP BY product_id
ORDER BY total_revenue DESC
LIMIT 3;

3️⃣ Use JOIN to combine customer and order data.

SELECT c.customer_name, o.order_date, o.amount
FROM customers c
INNER JOIN orders o ON c.customer_id = o.customer_id;

(Use INNER for matches; LEFT for all customers.) 4️⃣ Difference between WHERE and HAVING? WHERE filters rows before grouping; HAVING filters after GROUP BY (e.g., on aggregates like SUM). WHERE is for individual rows, HAVING for grouped results. 5️⃣ Explain INDEX and how it improves performance. An INDEX speeds up data retrieval by creating a data structure (like a B-tree) for quick lookups on columns. It reduces full table scans but adds overhead on inserts/updates. 🟦 Excel / Power BI 1️⃣ How would you clean messy data in Excel? Use Text to Columns for splitting, Find & Replace for errors, Remove Duplicates tool, and Power Query for advanced ETL (e.g., trim spaces, handle dates). 2️⃣ What is the difference between Pivot Table and Power Pivot? Pivot Tables summarize data visually; Power Pivot adds data modeling (relationships, DAX) for larger datasets and complex calculations beyond standard Pivots. 3️⃣ Explain DAX measures vs calculated columns. Measures are dynamic formulas (e.g., SUM for totals) computed on-the-fly for reports; calculated columns are static, row-by-row computations stored in the model. 4️⃣ How to handle missing values in Power BI? Use Power Query to replace nulls (e.g., with averages via "Replace Values"), or DAX like IF(ISBLANK()) in visuals. For viz, filter them out or use "Show items with no data." 5️⃣ Create a KPI visual comparing actual vs target sales. In Power BI, drag KPI visual, add actual sales to Value, target to Target, and trend metric. Set variance to show % difference—green/red indicators highlight performance. 🟩 Python 1️⃣ Write a function to remove outliers from a list using IQR.

import numpy as np
def remove_outliers(data):
    Q1 = np.percentile(data, 25)
    Q3 = np.percentile(data, 75)
    IQR = Q3 - Q1
    lower = Q1 - 1.5 * IQR
    upper = Q3 + 1.5 * IQR
    return [x for x in data if lower <= x <= upper]

2️⃣ Convert a nested list to a flat list.

nested = [[1, 2], [3, 4]]
flat = [item for sublist in nested for item in sublist]
# Or: import itertools; list(itertools.chain.from_iterable(nested))

3️⃣ Read a CSV file and count rows with nulls.

import pandas as pd
df = pd.read_csv('file.csv')
null_counts = df.isnull().sum(axis=1)
print(null_counts[null_counts > 0].count())  # Rows with at least one null

4️⃣ How do you handle missing data in pandas? Use df.fillna(value) for imputation (e.g., mean), df.dropna() to drop rows/cols, or df.interpolate() for time series. Check with df.isnull().sum() first. 5️⃣ Explain the difference between loc[] and iloc[]. loc[] uses labels (e.g., df.loc['row_label']) for selection; iloc[] uses integer positions (e.g., df.iloc[0:2])—great for slicing by index. 💡 Pro Tip: Practice with mock datasets from Kaggle + build dashboards on Power BI to showcase in interviews. These hit the core skills employers test in 2025! 💬 Tap ❤️ for detailed answers! SQL's often the make-or-break—want code breakdowns or Python tips next? 😊

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🎯 Top 20 SQL Interview Questions You Must Know SQL is one of the most in-demand skills for Data Analysts. Here are 20 SQL interview questions that frequently appear in job interviews. 📌 Basic SQL Questions 1️⃣ What is the difference between INNER JOIN and LEFT JOIN? 2️⃣ How does GROUP BY work, and why do we use it? 3️⃣ What is the difference between HAVING and WHERE? 4️⃣ How do you remove duplicate rows from a table? 5️⃣ What is the difference between RANK(), DENSE_RANK(), and ROW_NUMBER()? 📌 Intermediate SQL Questions 6️⃣ How do you find the second highest salary from an Employee table? 7️⃣ What is a Common Table Expression (CTE), and when should you use it? 8️⃣ How do you identify missing values in a dataset using SQL? 9️⃣ What is the difference between UNION and UNION ALL? 🔟 How do you calculate a running total in SQL? 📌 Advanced SQL Questions 1️⃣1️⃣ How does a self-join work? Give an example. 1️⃣2️⃣ What is a window function, and how is it different from GROUP BY? 1️⃣3️⃣ How do you detect and remove duplicate records in SQL? 1️⃣4️⃣ Explain the difference between EXISTS and IN. 1️⃣5️⃣ What is the purpose of COALESCE()? 📌 Real-World SQL Scenarios 1️⃣6️⃣ How do you optimize a slow SQL query? 1️⃣7️⃣ What is indexing in SQL, and how does it improve performance? 1️⃣8️⃣ Write an SQL query to find customers who have placed more than 3 orders. 1️⃣9️⃣ How do you calculate the percentage of total sales for each category? 2️⃣0️⃣ What is the use of CASE statements in SQL? React with ♥️ if you want me to post the correct answers in next posts! ⬇️ Share with credits: https://t.me/sqlspecialist Hope it helps :)

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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 2

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