Common Table Expressions (CTEs) in SQL 👇👇

CTEs (WITH statement) help write cleaner and more readable SQL queries. They are like temporary result sets that can be referenced within the main query.

1️⃣ Basic Syntax of CTE

WITH cte_name AS ( SELECT column1, column2 FROM table_name WHERE condition ) SELECT * FROM cte_name;

✔ The CTE cte_name is defined and then used in the main SELECT query.

2️⃣ Simple CTE Example

🔹 Find employees earning more than $70,000

WITH high_earners AS ( SELECT name, salary, department_id FROM employees WHERE salary > 70000 ) SELECT * FROM high_earners; 

✔ The CTE high_earners filters employees with high salaries before selecting all columns from it.

3️⃣ CTE with Aggregation

🔹 Find departments where the average salary is above $80,000

WITH department_salary AS ( SELECT department_id, AVG(salary) AS avg_salary FROM employees GROUP BY department_id ) SELECT department_id, avg_salary FROM department_salary WHERE avg_salary > 80000; 

✔ The CTE department_salary calculates the average salary per department and filters out low-paying ones.

4️⃣ CTE for Recursive Queries (Hierarchy Example)

🔹 Find an employee hierarchy (who reports to whom)

WITH RECURSIVE employee_hierarchy AS ( SELECT employee_id, name, manager_id FROM employees WHERE manager_id IS NULL -- Start with top-level manager UNION ALL SELECT e.employee_id, e.name, e.manager_id FROM employees e INNER JOIN employee_hierarchy eh ON e.manager_id = eh.employee_id ) SELECT * FROM employee_hierarchy; 

✔ This recursive CTE finds an employee hierarchy starting from the top-level manager.

5️⃣ Why Use CTEs Instead of Subqueries?

✅ Better Readability – Makes complex queries easier to understand
✅ Reusability – Can be referenced multiple times in the main query
✅ Performance – Some databases optimize CTEs better than nested subqueries

Mini Task for You: Write an SQL query using a CTE to find departments with more than 5 employees.

You can find free SQL Resources here
👇👇
https://news.1rj.ru/str/mysqldata

Like this post if you want me to continue covering all the topics! ❤️

Share with credits: https://news.1rj.ru/str/sqlspecialist

Hope it helps :)

#sql

Window Functions in SQL

Window functions perform calculations across a set of table rows related to the current row. Unlike aggregation functions, they do not collapse rows but retain all rows while providing additional insights.

1️⃣ Common Window Functions

ROW_NUMBER() → Assigns a unique rank to each row within a partition
RANK() → Similar to ROW_NUMBER(), but gives same rank to duplicates
DENSE_RANK() → Similar to RANK(), but without skipping numbers
NTILE(n) → Divides the result into n equal parts
SUM() OVER() → Running total (cumulative sum)
AVG() OVER() → Moving average
LAG() → Gets the previous row’s value
LEAD() → Gets the next row’s value

2️⃣ Basic Syntax

SELECT column1, column2, window_function() OVER (PARTITION BY column ORDER BY column) AS alias FROM table_name;

✔ PARTITION BY groups rows before applying the function
✔ ORDER BY determines the ranking or sequence

3️⃣ Using ROW_NUMBER()

🔹 Assign a unique row number to each employee based on salary (highest first)

SELECT name, department, salary, ROW_NUMBER() OVER (PARTITION BY department ORDER BY salary DESC) AS row_num FROM employees;



✔ Each employee gets a unique row number within their department.

4️⃣ Using RANK() and DENSE_RANK()

🔹 Rank employees by salary within each department

SELECT name, department, salary, RANK() OVER (PARTITION BY department ORDER BY salary DESC) AS rank, DENSE_RANK() OVER (PARTITION BY department ORDER BY salary DESC) AS dense_rank FROM employees;



✔ RANK() skips numbers when there’s a tie
✔ DENSE_RANK() does not skip numbers

5️⃣ Using NTILE() for Distribution

🔹 Divide employees into 4 salary groups per department

SELECT name, department, salary, NTILE(4) OVER (PARTITION BY department ORDER BY salary DESC) AS salary_quartile FROM employees;



✔ Useful for dividing salaries into percentiles (e.g., top 25%, bottom 25%)

6️⃣ Running Total with SUM() OVER()

🔹 Calculate cumulative salary per department

SELECT name, department, salary, SUM(salary) OVER (PARTITION BY department ORDER BY salary DESC) AS running_total FROM employees;



✔ Useful for tracking cumulative totals

7️⃣ Using LAG() and LEAD()

🔹 Compare an employee’s salary with the previous and next employee’s salary


SELECT name, department, salary, LAG(salary) OVER (PARTITION BY department ORDER BY salary DESC) AS previous_salary, LEAD(salary) OVER (PARTITION BY department ORDER BY salary DESC) AS next_salary FROM employees; 

✔ LAG() gets the previous row’s value
✔ LEAD() gets the next row’s value

Mini Task for You: Write an SQL query to assign a unique rank to employees based on their salary within each department using RANK().

You can find free SQL Resources here
👇👇
https://news.1rj.ru/str/mysqldata

Like this post if you want me to continue covering all the topics! ❤️

Share with credits: https://news.1rj.ru/str/sqlspecialist

Hope it helps :)

#sql

Indexing in SQL

Indexes improve the speed of data retrieval by optimizing how queries access tables. They work like a book’s index—allowing you to find information faster instead of scanning every page.

1️⃣ Types of Indexes in SQL:

Primary Index → Automatically created on the primary key
Unique Index → Ensures all values in a column are unique
Composite Index → Created on multiple columns
Clustered Index → Determines the physical order of data storage
Non-Clustered Index → Creates a separate structure for faster lookups
Full-Text Index → Optimized for text searches

2️⃣ Creating an Index

🔹 Create an index on the "email" column in the "users" table

CREATE INDEX idx_email ON users(email); 

✔ Speeds up searches for users by email

3️⃣ Creating a Unique Index
🔹 Ensure that no two users have the same email

CREATE UNIQUE INDEX idx_unique_email ON users(email); 

✔ Prevents duplicate emails from being inserted

4️⃣ Composite Index for Multiple Columns

🔹 Optimize queries that filter by first name and last name

CREATE INDEX idx_name ON users(first_name, last_name); 

✔ Faster lookups when filtering by both first name and last name

5️⃣ Clustered vs. Non-Clustered Index

Clustered Index → Physically rearranges table data (only one per table)
Non-Clustered Index → Stores a separate lookup table for faster access

🔹 Create a clustered index on the "id" column

CREATE CLUSTERED INDEX idx_id ON users(id); 

🔹 Create a non-clustered index on the "email" column

CREATE NONCLUSTERED INDEX idx_email ON users(email); 

✔ Clustered indexes speed up searches when retrieving all columns
✔ Non-clustered indexes speed up searches for specific columns

6️⃣ Checking Indexes on a Table

🔹 Find all indexes on the "users" table

SELECT * FROM sys.indexes WHERE object_id = OBJECT_ID('users'); 

7️⃣ When to Use Indexes?

✅ Columns frequently used in WHERE, JOIN, ORDER BY
✅ Large tables that need faster searches
✅ Unique columns that should not allow duplicates
❌ Avoid indexing on columns with highly repetitive values (e.g., boolean columns)
❌ Avoid too many indexes, as they slow down INSERT, UPDATE, DELETE operations

Mini Task for You: Write an SQL query to create a unique index on the "phone_number" column in the "customers" table.

You can find free SQL Resources here
👇👇
https://news.1rj.ru/str/mysqldata

Like this post if you want me to continue covering all the topics! ❤️

Share with credits: https://news.1rj.ru/str/sqlspecialist

Hope it helps :)

#sql

Normalization in SQL

Normalization is the process of organizing a database to reduce redundancy and improve efficiency. It ensures data is stored logically by breaking it into smaller, related tables.

1️⃣ Why Normalize a Database?

Eliminates duplicate data
Reduces data anomalies (insertion, update, deletion issues)
Improves data integrity
Makes queries faster and more efficient

2️⃣ Normal Forms (NF) in SQL

First Normal Form (1NF) → No duplicate rows, atomic values
Second Normal Form (2NF) → No partial dependency (remove redundant columns)
Third Normal Form (3NF) → No transitive dependency (separate non-key attributes)
Boyce-Codd Normal Form (BCNF) → More strict version of 3NF

3️⃣ First Normal Form (1NF) – Atomic Values

Problem: Storing multiple values in a single column

Example (Before Normalization):

OrderID: 1, Customer: John, Products: Laptop, Mouse
OrderID: 2, Customer: Alice, Products: Phone, Headphones

Fix: Create a separate table with atomic values

Example (After Normalization):

OrderID: 1, Customer: John, Product: Laptop
OrderID: 1, Customer: John, Product: Mouse
OrderID: 2, Customer: Alice, Product: Phone
OrderID: 2, Customer: Alice, Product: Headphones

4️⃣ Second Normal Form (2NF) – No Partial Dependencies

Problem: Columns dependent on only part of the primary key

Example (Before Normalization):

OrderID: 1, Product: Laptop, Supplier: Dell, SupplierPhone: 123-456
OrderID: 2, Product: Phone, Supplier: Apple, SupplierPhone: 987-654

Fix: Separate supplier details into another table

Example (After Normalization):

Orders Table:
OrderID: 1, Product: Laptop, SupplierID: 1
OrderID: 2, Product: Phone, SupplierID: 2

Suppliers Table:
SupplierID: 1, Supplier: Dell, SupplierPhone: 123-456
SupplierID: 2, Supplier: Apple, SupplierPhone: 987-654

5️⃣ Third Normal Form (3NF) – No Transitive Dependencies

Problem: Non-key column dependent on another non-key column

Example (Before Normalization):
CustomerID: 1, Name: John, City: NY, ZipCode: 10001
CustomerID: 2, Name: Alice, City: LA, ZipCode: 90001

Fix: Separate city and ZIP code into a new table

Example (After Normalization):
Customers Table:
CustomerID: 1, Name: John, ZipCode: 10001
CustomerID: 2, Name: Alice, ZipCode: 90001
Locations Table:
ZipCode: 10001, City: NY
ZipCode: 90001, City: LA

6️⃣ Boyce-Codd Normal Form (BCNF) – No Overlapping Candidate Keys

Problem: Multiple candidate keys with dependencies

Fix: Ensure every determinant is a candidate key by further splitting tables

7️⃣ When to Normalize and When to Denormalize?

Use normalization for transactional databases (banking, e-commerce)

Use denormalization for analytics databases (faster reporting queries)

Mini Task for You: Write an SQL query to split a "Customers" table by moving city details into a separate "Locations" table following 3NF.

You can find free SQL Resources here
👇👇
https://news.1rj.ru/str/mysqldata

Like this post if you want me to continue covering all the topics! ❤️

Share with credits: https://news.1rj.ru/str/sqlspecialist

Hope it helps :)

#sql

Let's move to our next topic now

Data Cleaning & Transformation

Data cleaning and transformation are critical for preparing raw data for analysis. It involves handling missing data, removing duplicates, standardizing formats, and optimizing data structures.

1️⃣ Handling Missing Data in SQL & Python

In SQL:

COALESCE(): Replaces NULL values with a default value

SELECT id, name, COALESCE(salary, 0) AS salary FROM employees; 
IFNULL(): Works similarly to COALESCE (MySQL) SELECT id, name, IFNULL(salary, 0) AS salary FROM employees; 

In Python (Pandas):

dropna(): Removes rows with missing values

df.dropna(inplace=True) 

fillna(): Fills missing values with a specified value

df['salary'].fillna(0, inplace=True) 

interpolate(): Fills missing values using interpolation

df.interpolate(method='linear', inplace=True)



2️⃣ Removing Duplicates
In SQL:

Remove duplicate rows using DISTINCT

SELECT DISTINCT name, department FROM employees; 

Delete duplicates while keeping only one row

DELETE FROM employees WHERE id NOT IN (SELECT MIN(id) FROM employees GROUP BY name, department); 

In Python (Pandas):

Remove duplicate rows

df.drop_duplicates(inplace=True) 

Keep only the first occurrence

df.drop_duplicates(subset=['name', 'department'], keep='first', inplace=True) 

3️⃣ Standardizing Formats (Data Normalization)

Standardizing Text Case:

SQL: Convert text to uppercase or lowercase

SELECT UPPER(name) AS name_upper FROM employees; 

Python: Convert text to lowercase

df['name'] = df['name'].str.lower() 

Date Formatting:

SQL: Convert string to date format SELECT


CONVERT(DATE, '2024-02-26', 120);

Python: Convert string to datetime

df['date'] = pd.to_datetime(df['date'], format='%Y-%m-%d')

4️⃣ ETL Process (Extract, Transform, Load)

Extract:

SQL: Retrieve data from databases

SELECT * FROM sales_data; 

Python: Load data from CSV

df = pd.read_csv('data.csv')

Transform:

SQL: Modify data (cleaning, aggregations)

SELECT category, SUM(sales) AS total_sales FROM sales_data GROUP BY category; 

Python: Apply transformations

df['total_sales'] = df.groupby('category')['sales'].transform('sum') 

Load:
SQL: Insert cleaned data into a new table

INSERT INTO clean_sales_data (category, total_sales) 
SELECT category, SUM(sales) FROM sales_data GROUP BY category; 

Python: Save cleaned data to a new CSV file

df.to_csv('cleaned_data.csv', index=False)

Mini Task for You: Write an SQL query to remove duplicate customer records, keeping only the first occurrence.

Here you can find the roadmap for data analyst: https://news.1rj.ru/str/sqlspecialist/1159

Like this post if you want me to continue covering all the topics! ❤️

Share with credits: https://news.1rj.ru/str/sqlspecialist

Hope it helps :)

#sql

Hi guys,

Many people charge too much to teach Excel, Power BI, SQL, Python & Tableau but my mission is to break down barriers. I have shared complete learning series to start your data analytics journey from scratch.

For those of you who are new to this channel, here are some quick links to navigate this channel easily.

Data Analyst Learning Plan 👇
https://news.1rj.ru/str/sqlspecialist/752

Python Learning Plan 👇
https://news.1rj.ru/str/sqlspecialist/749

Power BI Learning Plan 👇
https://news.1rj.ru/str/sqlspecialist/745

SQL Learning Plan 👇
https://news.1rj.ru/str/sqlspecialist/738

SQL Learning Series 👇
https://news.1rj.ru/str/sqlspecialist/567

Excel Learning Series 👇
https://news.1rj.ru/str/sqlspecialist/664

Power BI Learning Series 👇
https://news.1rj.ru/str/sqlspecialist/768

Python Learning Series 👇
https://news.1rj.ru/str/sqlspecialist/615

Tableau Essential Topics 👇
https://news.1rj.ru/str/sqlspecialist/667

Best Data Analytics Resources 👇
https://heylink.me/DataAnalytics

You can find more resources on Medium & Linkedin

Like for more ❤️

Thanks to all who support our channel and share it with friends & loved ones. You guys are really amazing.

Hope it helps :)

Exploratory Data Analysis (EDA)

EDA is the process of analyzing datasets to summarize key patterns, detect anomalies, and gain insights before applying machine learning or reporting.

1️⃣ Denoscriptive Statistics
Denoscriptive statistics help summarize and understand data distributions.

In SQL:

Calculate Mean (Average):

SELECT AVG(salary) AS average_salary FROM employees; 
Find Median (Using Window Functions) SELECT salary FROM ( SELECT salary, ROW_NUMBER() OVER (ORDER BY salary) AS row_num, COUNT(*) OVER () AS total_rows FROM employees ) subquery WHERE row_num = (total_rows / 2); 

Find Mode (Most Frequent Value)

SELECT department, COUNT(*) AS count FROM employees GROUP BY department ORDER BY count DESC LIMIT 1; 

Calculate Variance & Standard Deviation

SELECT VARIANCE(salary) AS salary_variance, STDDEV(salary) AS salary_std_dev FROM employees; 

In Python (Pandas):

Mean, Median, Mode

df['salary'].mean() df['salary'].median() df['salary'].mode()[0]

Variance & Standard Deviation

df['salary'].var() df['salary'].std()

2️⃣ Data Visualization

Visualizing data helps identify trends, outliers, and patterns.

In SQL (For Basic Visualization in Some Databases Like PostgreSQL):

Create Histogram (Approximate in SQL)

SELECT salary, COUNT(*) FROM employees GROUP BY salary ORDER BY salary; 

In Python (Matplotlib & Seaborn):

Bar Chart (Category-Wise Sales)

import matplotlib.pyplot as plt 
import seaborn as sns 
df.groupby('category')['sales'].sum().plot(kind='bar') 
plt.noscript('Total Sales by Category') 
plt.xlabel('Category') 
plt.ylabel('Sales') 
plt.show() 

Histogram (Salary Distribution)

sns.histplot(df['salary'], bins=10, kde=True) 
plt.noscript('Salary Distribution') 
plt.show() 

Box Plot (Outliers in Sales Data)

sns.boxplot(y=df['sales']) 
plt.noscript('Sales Data Outliers') 
plt.show()

Heatmap (Correlation Between Variables)

sns.heatmap(df.corr(), annot=True, cmap='coolwarm') plt.noscript('Feature Correlation Heatmap') plt.show() 

3️⃣ Detecting Anomalies & Outliers

Outliers can skew results and should be identified.

In SQL:

Find records with unusually high salaries

SELECT * FROM employees WHERE salary > (SELECT AVG(salary) + 2 * STDDEV(salary) FROM employees); 

In Python (Pandas & NumPy):

Using Z-Score (Values Beyond 3 Standard Deviations)

from scipy import stats df['z_score'] = stats.zscore(df['salary']) df_outliers = df[df['z_score'].abs() > 3] 

Using IQR (Interquartile Range)

Q1 = df['salary'].quantile(0.25) 
Q3 = df['salary'].quantile(0.75) 
IQR = Q3 - Q1 
df_outliers = df[(df['salary'] < (Q1 - 1.5 * IQR)) | (df['salary'] > (Q3 + 1.5 * IQR))] 

4️⃣ Key EDA Steps

Understand the Data → Check missing values, duplicates, and column types

Summarize Statistics → Mean, Median, Standard Deviation, etc.

Visualize Trends → Histograms, Box Plots, Heatmaps

Detect Outliers & Anomalies → Z-Score, IQR

Feature Engineering → Transform variables if needed

Mini Task for You: Write an SQL query to find employees whose salaries are above two standard deviations from the mean salary.

Here you can find the roadmap for data analyst: https://news.1rj.ru/str/sqlspecialist/1159

Like this post if you want me to continue covering all the topics! ❤️

Share with credits: https://news.1rj.ru/str/sqlspecialist

Hope it helps :)

#sql

Business Intelligence & Reporting

Business Intelligence (BI) and reporting involve transforming raw data into actionable insights using visualization tools like Power BI, Tableau, and Google Data Studio.

1️⃣ Power BI & Tableau Basics

These tools help create interactive dashboards, reports, and visualizations.
Power BI: Uses DAX (Data Analysis Expressions) for calculations and Power Query for data transformation.

Tableau: Uses calculated fields and built-in functions for dynamic reporting.

2️⃣ Essential Features in Power BI & Tableau

🔹 Dashboards: Interactive visualizations combining multiple reports.

🔹 Filters & Slicers: Allow users to focus on specific data.

🔹 Drill-through & Drill-down: Navigate from high-level to detailed data.

🔹 Calculated Fields: Custom metrics for analysis.

🔹 Data Blending: Combine multiple sources into a single report.

3️⃣ Power BI Key Concepts

✔ DAX (Data Analysis Expressions): Used for creating custom calculations.

Example:

Calculate Total Sales
Total_Sales = SUM(Sales[Revenue])

Create a Year-over-Year Growth Rate
YoY Growth = ( [Current Year Sales] - [Previous Year Sales] ) / [Previous Year Sales]

✔ Power Query: Used for data cleaning and transformation.
Remove duplicates
Merge datasets
Pivot/Unpivot data

✔ Power BI Visuals
Bar, Line, Pie Charts
KPI Indicators
Maps (for geographic analysis)

4️⃣ Tableau Key Concepts

✔ Calculated Fields: Used to create new metrics.

Example:

Total Profit Calculation
SUM([Sales]) - SUM([Cost])

Sales Growth Percentage
(SUM([Sales]) - LOOKUP(SUM([Sales]), -1)) / LOOKUP(SUM([Sales]), -1)

✔ Tableau Filters
Dimension Filter (Category, Region)
Measure Filter (Sales > $10,000)
Top N Filter (Top 10 Products by Sales)

✔ Dashboards in Tableau
Drag & drop visualizations
Add filters and parameters
Customize tooltips

5️⃣ Google Data Studio (Looker Studio)

A free tool for creating interactive reports.

✔ Connects to Google Sheets, BigQuery, and SQL databases.
✔ Drag-and-drop report builder.
✔ Custom calculations using formulas like in Excel.

Example: Create a Revenue per Customer metric:
SUM(Revenue) / COUNT(DISTINCT Customer_ID)

6️⃣ Best Practices for BI Reporting

✅ Keep Dashboards Simple → Only show key KPIs.
✅ Use Consistent Colors & Formatting → Makes insights clear.
✅ Optimize Performance → Avoid too many calculations on large datasets.
✅ Enable Interactivity → Filters, drill-downs, and slicers improve user experience.

Mini Task for You: In Power BI, create a DAX formula to calculate the Cumulative Sales over time.

Data Analyst Roadmap: 👇
https://news.1rj.ru/str/sqlspecialist/1159

Like this post if you want me to continue covering all the topics! ❤️

Share with credits: https://news.1rj.ru/str/sqlspecialist

Hope it helps :)

#sql

Data Analyst Interview Questions & Preparation Tips

Be prepared with a mix of technical, analytical, and business-oriented interview questions.

1. Technical Questions (Data Analysis & Reporting)

SQL Questions:

How do you write a query to fetch the top 5 highest revenue-generating customers?

Explain the difference between INNER JOIN, LEFT JOIN, and FULL OUTER JOIN.

How would you optimize a slow-running query?

What are CTEs and when would you use them?

Data Visualization (Power BI / Tableau / Excel)

How would you create a dashboard to track key performance metrics?

Explain the difference between measures and calculated columns in Power BI.

How do you handle missing data in Tableau?

What are DAX functions, and can you give an example?

ETL & Data Processing (Alteryx, Power BI, Excel)

What is ETL, and how does it relate to BI?

Have you used Alteryx for data transformation? Explain a complex workflow you built.

How do you automate reporting using Power Query in Excel?


2. Business and Analytical Questions

How do you define KPIs for a business process?

Give an example of how you used data to drive a business decision.

How would you identify cost-saving opportunities in a reporting process?

Explain a time when your report uncovered a hidden business insight.


3. Scenario-Based & Behavioral Questions

Stakeholder Management:

How do you handle a situation where different business units have conflicting reporting requirements?

How do you explain complex data insights to non-technical stakeholders?

Problem-Solving & Debugging:

What would you do if your report is showing incorrect numbers?

How do you ensure the accuracy of a new KPI you introduced?

Project Management & Process Improvement:

Have you led a project to automate or improve a reporting process?

What steps do you take to ensure the timely delivery of reports?


4. Industry-Specific Questions (Credit Reporting & Financial Services)

What are some key credit risk metrics used in financial services?

How would you analyze trends in customer credit behavior?

How do you ensure compliance and data security in reporting?


5. General HR Questions

Why do you want to work at this company?

Tell me about a challenging project and how you handled it.

What are your strengths and weaknesses?

Where do you see yourself in five years?

How to Prepare?

Brush up on SQL, Power BI, and ETL tools (especially Alteryx).

Learn about key financial and credit reporting metrics.(varies company to company)

Practice explaining data-driven insights in a business-friendly manner.

Be ready to showcase problem-solving skills with real-world examples.

React with ❤️ if you want me to also post sample answer for the above questions

Share with credits: https://news.1rj.ru/str/sqlspecialist

Hope it helps :)

Python for Data Analysts: From Basics to Advanced Level

🔹 Basics of Python

➊ Python Syntax & Data Types
↳ Variables, data types (int, float, string, bool)
↳ Type conversion and basic operations

➋ Control Flow & Loops
↳ if-else, for, while loops
↳ List comprehensions for efficient iteration

➌ Functions & Lambda Expressions
↳ Defining functions and using *args & **kwargs
↳ Anonymous functions with lambda

➍ Error Handling
↳ try-except for handling errors gracefully
↳ Raising custom exceptions

🔹 Intermediate Python for Data Analytics

➎ Working with Lists, Tuples, and Dictionaries
↳ List, tuple, and dictionary operations
↳ Dictionary and list comprehensions

➏ String Manipulation & Regular Expressions
↳ String formatting and manipulation
↳ Extracting patterns with re module

➐ Date & Time Handling
↳ Working with datetime and pandas.to_datetime()
↳ Formatting, extracting, and calculating time differences

➑ File Handling (CSV, JSON, Excel)
↳ Reading and writing structured files using pandas
↳ Handling large files efficiently using chunks

🔹 Data Analysis with Python

➒ Pandas for Data Manipulation
↳ Reading, cleaning, filtering, and transforming data
↳ Aggregations using .groupby(), .pivot_table()
↳ Merging and joining datasets

➓ NumPy for Numerical Computing
↳ Creating and manipulating arrays
↳ Vectorized operations for performance optimization

⓫ Handling Missing Data
↳ .fillna(), .dropna(), .interpolate()
↳ Imputing missing values for better analytics

⓬ Data Visualization with Matplotlib & Seaborn
↳ Creating plots (line, bar, scatter, histogram)
↳ Customizing plots for presentations
↳ Heatmaps for correlation analysis

🔹 Advanced Topics for Data Analysts

⓭ SQL with Python
↳ Connecting to databases using sqlalchemy
↳ Writing and executing SQL queries in Python (pandas.read_sql())
↳ Merging SQL and Pandas for analysis

⓮ Working with APIs & Web Scraping
↳ Fetching data from APIs using requests
↳ Web scraping using BeautifulSoup and Selenium

⓯ ETL (Extract, Transform, Load) Pipelines
↳ Automating data ingestion and transformation
↳ Cleaning and loading data into databases

⓰ Time Series Analysis
↳ Working with time-series data in Pandas
↳ Forecasting trends using moving averages

⓱ Machine Learning Basics for Data Analysts
↳ Introduction to Scikit-learn (Linear Regression, KNN, Clustering)
↳ Feature engineering and model evaluation

🚀 The best way to learn Python is by working on real-world projects!

Data Analytics Projects: https://news.1rj.ru/str/sqlproject

Share with credits: https://news.1rj.ru/str/sqlspecialist

Hope it helps :)

SQL for Data Analysts: From Basics to Advanced

🔹 Basics of SQL

➊ SQL Syntax & Basic Queries
↳ SELECT, FROM, WHERE for data retrieval
↳ Filtering data using AND, OR, BETWEEN, LIKE, IN

➋ Sorting & Limiting Data
↳ ORDER BY for sorting results
↳ LIMIT & OFFSET for pagination

➌ Data Filtering & Aggregation
↳ COUNT(), SUM(), AVG(), MIN(), MAX()
↳ Grouping data using GROUP BY and HAVING

➍ Joins & Relationships
↳ INNER JOIN, LEFT JOIN, RIGHT JOIN, FULL OUTER JOIN
↳ Self-joins & cross-joins for complex relationships

➎ Subqueries & CTEs
↳ Writing subqueries for better query organization
↳ Using WITH to create Common Table Expressions (CTEs)

🔹 Intermediate SQL for Data Analysis

➏ Window Functions for Advanced Aggregation
↳ ROW_NUMBER(), RANK(), DENSE_RANK(), NTILE()
↳ LEAD() & LAG() for time-based analysis

➐ String & Date Functions
↳ CONCAT(), UPPER(), LOWER(), TRIM(), SUBSTRING()
↳ DATEPART(), DATEDIFF(), EXTRACT() for date manipulation

➑ Case Statements & Conditional Logic
↳ CASE WHEN for conditional transformations
↳ Nested CASE statements for advanced logic

➒ Pivoting & Unpivoting Data
↳ PIVOT() for transforming row-based data into columns
↳ UNPIVOT() for restructuring wide tables

➓ Handling Missing Data & NULL Values
↳ Using COALESCE() & NULLIF()
↳ Filtering and replacing NULL values

🔹 Advanced SQL for Data Analysts

⓫ Optimizing SQL Queries
↳ Using Indexes to improve performance
↳ Understanding EXPLAIN & query execution plans

⓬ Recursive Queries & Hierarchical Data
↳ WITH RECURSIVE for hierarchical relationships
↳ Organizing parent-child relationships in tables

⓭ Stored Procedures & Functions
↳ Writing reusable stored procedures
↳ Creating user-defined functions (UDFs)

⓮ Working with JSON & Semi-Structured Data
↳ Extracting and parsing JSON data using JSON_VALUE()
↳ Handling nested structures in SQL

⓯ Time Series & Trend Analysis
↳ Calculating moving averages
↳ Performing time-based aggregations

⓰ SQL in Python
↳ Connecting databases using SQLAlchemy
↳ Running SQL queries in pandas.read_sql()
↳ Merging SQL and Pandas for advanced analysis

🚀 The best way to master SQL is to work on real-world datasets and optimize queries for performance!

Free SQL Resources: https://whatsapp.com/channel/0029VanC5rODzgT6TiTGoa1v

Share with credits: https://news.1rj.ru/str/sqlspecialist

Hope it helps :)

Thanks for the amazing response

Here are the Answers for above Interview Questions

Technical Questions (Data Analysis & Reporting)

SQL Questions

Q1: How do you write a query to fetch the top 5 highest revenue-generating customers?

SELECT customer_id, SUM(revenue) AS total_revenue
FROM sales
GROUP BY customer_id
ORDER BY total_revenue DESC
LIMIT 5;

Q2: Explain the difference between INNER JOIN, LEFT JOIN, and FULL OUTER JOIN.

INNER JOIN: Returns only the matching records from both tables.

LEFT JOIN: Returns all records from the left table and matching records from the right table. If no match is found, it returns NULLs.

FULL OUTER JOIN: Returns all records from both tables, with NULLs where there is no match.


SELECT a.customer_id, a.order_id, b.payment_id
FROM orders a
INNER JOIN payments b ON a.order_id = b.order_id;

Q3: How would you optimize a slow-running query?

Use Indexes on frequently queried columns.

Avoid SELECT * and only select required columns.

Use EXPLAIN ANALYZE to check query performance.

Optimize JOINs and use WHERE instead of HAVING.

Consider using Partitioning and Materialized Views for large datasets.


Q4: What are CTEs and when would you use them?

A Common Table Expression (CTE) is a temporary result set used within a query. It improves readability and avoids redundant subqueries.

WITH sales_summary AS (
SELECT customer_id, SUM(revenue) AS total_revenue
FROM sales
GROUP BY customer_id
)
SELECT * FROM sales_summary WHERE total_revenue > 10000;

Data Visualization (Power BI / Tableau / Excel)

Q5: How would you create a dashboard to track key performance metrics?

1. Identify the KPIs (e.g., revenue, customer retention, processing time).


2. Extract data using SQL or ETL tools.


3. Clean and transform data in Power Query (Power BI) or Alteryx.


4. Use visualizations like bar charts, line graphs, and KPI cards.


5. Add filters and slicers for user interactivity.


6. Automate data refresh and ensure data integrity.



Q6: Explain the difference between measures and calculated columns in Power BI.

Measures: Dynamic calculations used in reports (e.g., SUM, AVERAGE). Computed only when needed.

Calculated Columns: Static calculations stored in the dataset. Used when a value is needed in a row-wise manner.


-- Measure:
Total Sales = SUM(Sales[Revenue])

-- Calculated Column:
Sales Category = IF(Sales[Revenue] > 10000, "High", "Low")

Q7: How do you handle missing data in Tableau?

Use Filters to remove nulls.

Use IFNULL() or ZN() functions to replace nulls.

Interpolate missing values using LODs (Level of Detail Expressions).

Use DATA BLENDING to merge datasets where missing data exists.


IFNULL(SUM(Sales), 0)

ETL & Data Processing (Alteryx, Power BI, Excel)

Q8: What is ETL, and how does it relate to BI?

ETL (Extract, Transform, Load) is the process of extracting data from various sources, transforming it for consistency, and loading it into a BI system for analysis.

Q9: Have you used Alteryx for data transformation? Explain a complex workflow you built.

Yes, I built an Alteryx workflow to:

1. Connect to multiple data sources (Excel, SQL).


2. Clean and merge datasets.


3. Create new KPIs and aggregate data.


4. Output to Power BI for visualization.


Like this post if you want me to post remaining answers in the next post 👍❤️

Share with credits: https://news.1rj.ru/str/sqlspecialist

Hope it helps :)

Business and Analytical Questions

Q10: How do you define KPIs for a business process?
KPIs (Key Performance Indicators) should be SMART (Specific, Measurable, Achievable, Relevant, Time-bound).
Example:
Operational KPI: Average processing time per request.
Financial KPI: Monthly revenue growth rate.

Q11: Give an example of how you used data to drive a business decision.
At my previous job, I analyzed customer churn rates using SQL and Power BI. I found that customers leaving had lower engagement rates. We introduced loyalty programs, reducing churn by 15%.


Scenario-Based & Behavioral Questions

Q12: How do you handle a situation where different business units have conflicting reporting requirements?
Understand each team's objectives.
Standardize KPI definitions.
Create customized dashboards based on common metrics.
Align with senior stakeholders to prioritize key metrics.

Q13: What would you do if your report is showing incorrect numbers?
Check source data (duplicates, missing values).
Validate ETL transformations.
Review calculations in Power BI/Tableau.
Compare outputs against historical trends.
Industry-Specific Questions (Credit Reporting & Financial Services)

Q14: What are some key credit risk metrics used in financial services?
Credit Utilization Ratio = (Credit Used / Credit Limit) * 100
Debt-to-Income (DTI) Ratio = (Total Debt / Total Income)
Delinquency Rate = % of accounts overdue

Q15: How do you ensure compliance and data security in reporting?
Follow GDPR, CCPA, and PCI-DSS regulations.
Use role-based access control (RBAC).
Encrypt sensitive data and restrict PII (Personally Identifiable Information) exposure.

General HR Questions
Q16: Why do you want to work at XYZ company?
XYZ is a leader in data analytics and financial insights. I’m excited about leveraging my BI expertise to contribute to global delivery operations and improve KPI reporting.

Q17: Tell me about a challenging project and how you handled it.
I once worked on a real-time dashboard project with inconsistent data sources. I collaborated with IT to automate data ingestion and improved accuracy by 30%.

Q18: Where do you see yourself in five years?
I see myself growing into a leadership role in business intelligence, contributing to strategic decision-making through advanced data insights.

Final Tips for Interview Preparation:

✅ Practice SQL queries and Power BI dashboards
✅ Review credit reporting metrics and industry knowledge
✅ Be ready with real-world case studies from your past experience

React with ♥️ if you want me to post mock interview questions or scenario-based Interview Questions related to data analytics

Share with credits: https://news.1rj.ru/str/sqlspecialist

Hope it helps :)