GROUP BY & HAVING in SQL

The GROUP BY clause is used to group rows that have the same values in specified columns. It’s commonly used with aggregation functions (SUM(), AVG(), COUNT(), etc.) to perform calculations on each group.
The HAVING clause filters groups after aggregation, similar to how WHERE filters individual rows.

1️⃣ Basic GROUP BY Usage
🔹 Find the total number of employees in each department

SELECT department, COUNT(*) FROM employees GROUP BY department; 

This groups employees by department and counts the number of employees in each department.

🔹 Find the total salary per department

SELECT department, SUM(salary) FROM employees GROUP BY department; 

2️⃣ GROUP BY with Multiple Columns
You can group by multiple columns to analyze data more deeply.

🔹 Find the total salary for each job noscript within each department

SELECT department, job_noscript, SUM(salary) FROM employees GROUP BY department, job_noscript;

3️⃣ Using HAVING to Filter Groups
Unlike WHERE, which filters before aggregation, HAVING filters after aggregation.

🔹 Find departments with more than 5 employees

SELECT department, COUNT(*) AS employee_count FROM employees GROUP BY department HAVING COUNT(*) > 5; 

🔹 Find departments where the total salary is greater than $500,000

SELECT department, SUM(salary) AS total_salary FROM employees GROUP BY department HAVING SUM(salary) > 500000;

🔹 Find job noscripts where the average salary is above $70,000

SELECT job_noscript, AVG(salary) AS avg_salary FROM employees GROUP BY job_noscript HAVING AVG(salary) > 70000; 

4️⃣ GROUP BY with ORDER BY
To sort grouped results, use ORDER BY.

🔹 Find the total salary per department, sorted in descending order

SELECT department, SUM(salary) AS total_salary FROM employees GROUP BY department ORDER BY total_salary DESC;

Mini Task for You:

Write an SQL query to find departments where the average salary is more than $80,000.
Let me know when you’re ready to move to the next topic! 🚀

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JOINS in SQL

Joins allow you to combine data from multiple tables based on related columns. They are essential for working with relational databases.

1️⃣ Types of JOINS

INNER JOIN → Returns only matching rows from both tables
LEFT JOIN → Returns all rows from the left table + matching rows from the right table
RIGHT JOIN → Returns all rows from the right table + matching rows from the left table
FULL JOIN → Returns all rows from both tables (matching + non-matching)
SELF JOIN → Joins a table with itself
CROSS JOIN → Returns all possible combinations of rows

2️⃣ INNER JOIN (Most Common Join)

🔹 Find employees and their department names

SELECT employees.name, employees.salary, departments.department_name FROM employees INNER JOIN departments ON employees.department_id = departments.department_id; 

✔ Returns only employees who have a matching department.

3️⃣ LEFT JOIN (Includes Unmatched Rows from Left Table)

🔹 Find all employees, including those without a department

SELECT employees.name, employees.salary, departments.department_name FROM employees LEFT JOIN departments ON employees.department_id = departments.department_id; 

✔ Includes employees even if they don’t have a department (NULL if no match).

4️⃣ RIGHT JOIN (Includes Unmatched Rows from Right Table)

🔹 Find all departments, including those without employees

SELECT employees.name, employees.salary, departments.department_name FROM employees RIGHT JOIN departments ON employees.department_id = departments.department_id; 

✔ Includes all departments, even if no employees are assigned.

5️⃣ FULL JOIN (Includes Unmatched Rows from Both Tables)

🔹 Get a complete list of employees and departments (matched + unmatched rows)

SELECT employees.name, employees.salary, departments.department_name FROM employees FULL JOIN departments ON employees.department_id = departments.department_id; 

✔ Includes all employees and departments even if there’s no match.


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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.

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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().

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#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.

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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.

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#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.

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#sql

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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.

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#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

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#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.

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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 :)