Which of the following is not a Window Function in SQL?
Anonymous Quiz
5%
RANK()
19%
DENSE_RANK()
26%
LEAD()
50%
MEAN()
👍2🔥1
Which of the following window function is used to assign a unique number to each row, even if the values are the same?
Anonymous Quiz
18%
RANK()
50%
ROW_NUMBER()
4%
SUM()
28%
DENSE_RANK()
👍7
Data Analytics
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…
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
✔ Speeds up searches for users by email
3️⃣ Creating a Unique Index
🔹 Ensure that no two users have the same email
✔ Prevents duplicate emails from being inserted
4️⃣ Composite Index for Multiple Columns
🔹 Optimize queries that filter by first name and 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 a non-clustered index on the "email" column
✔ 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
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
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#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
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#sql
👍10❤6🎉1
What's the full form of DDL in SQL?
Anonymous Quiz
93%
Data definition language
5%
Database definition link
1%
Dataset download link
2%
Data download language
👍9
Data Analytics
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…
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
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#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
👍11❤10
Data Analytics
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…
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
In Python (Pandas):
dropna(): Removes rows with missing values
fillna(): Fills missing values with a specified value
interpolate(): Fills missing values using interpolation
2️⃣ Removing Duplicates
In SQL:
Remove duplicate rows using DISTINCT
Delete duplicates while keeping only one row
In Python (Pandas):
Remove duplicate rows
Keep only the first occurrence
3️⃣ Standardizing Formats (Data Normalization)
Standardizing Text Case:
SQL: Convert text to uppercase or lowercase
Python: Convert text to lowercase
Date Formatting:
SQL: Convert string to date format
Python: Convert string to datetime
4️⃣ ETL Process (Extract, Transform, Load)
Extract:
SQL: Retrieve data from databases
Python: Load data from CSV
Transform:
SQL: Modify data (cleaning, aggregations)
Python: Apply transformations
Load:
SQL: Insert cleaned data into a new table
Python: Save cleaned data to a new CSV file
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! ❤️
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#sql
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
👍13❤5
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 :)
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 :)
❤12👍4🥰1
Data Analytics
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.…
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):
Find Mode (Most Frequent Value)
Calculate Variance & Standard Deviation
In Python (Pandas):
Mean, Median, Mode
Variance & Standard Deviation
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)
In Python (Matplotlib & Seaborn):
Bar Chart (Category-Wise Sales)
Histogram (Salary Distribution)
Box Plot (Outliers in Sales Data)
Heatmap (Correlation Between Variables)
3️⃣ Detecting Anomalies & Outliers
Outliers can skew results and should be identified.
In SQL:
Find records with unusually high salaries
In Python (Pandas & NumPy):
Using Z-Score (Values Beyond 3 Standard Deviations)
Using IQR (Interquartile Range)
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
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
👍20❤9
Which of the following python library is not used for data visualization?
Anonymous Quiz
19%
Matplotlib
18%
Seaborn
64%
Scikit learn
👍3❤1🔥1
Data Analytics
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.…
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
Create a Year-over-Year Growth Rate
✔ 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
Sales Growth Percentage
✔ 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:
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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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: 👇
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❤10👍7
What's the full form of DAX in Power BI?
Anonymous Quiz
77%
Data Analysis Expressions
22%
Database Analytics Expressions
1%
Data Analyst Exams
👍8
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 :)
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 :)
❤24👍7
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 :)
🔹 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 :)
👍16❤12
Which of the following python library is used for numerical computation?
Anonymous Quiz
10%
Matplotlib
82%
Numpy
5%
Scikit learn
2%
Plotly
👍7❤4
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 :)
🔹 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 :)
👍10❤7
Data Analytics
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…
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 :)
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 :)
👍12❤4🔥2
👍7🔥2
Data Analytics
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…
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 :)
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
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Data Analytics
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…
Data-Driven Decision Making
Data-driven decision-making (DDDM) involves using data analytics to guide business strategies instead of relying on intuition. Key techniques include A/B testing, forecasting, trend analysis, and KPI evaluation.
1️⃣ A/B Testing & Hypothesis Testing
A/B testing compares two versions of a product, marketing campaign, or website feature to determine which performs better.
✔ Key Metrics in A/B Testing:
Conversion Rate
Click-Through Rate (CTR)
Revenue per User
✔ Steps in A/B Testing:
1. Define the hypothesis (e.g., "Changing the CTA button color will increase clicks").
2. Split users into Group A (control) and Group B (test).
3. Analyze differences using statistical tests.
✔ SQL for A/B Testing:
Calculate average purchase per user in two test groups
Run a t-test to check statistical significance (Python)
🔹 P-value < 0.05 → Statistically significant difference.
🔹 P-value > 0.05 → No strong evidence of difference.
2️⃣ Forecasting & Trend Analysis
Forecasting predicts future trends based on historical data.
✔ Time Series Analysis Techniques:
Moving Averages (smooth trends)
Exponential Smoothing (weights recent data more)
ARIMA Models (AutoRegressive Integrated Moving Average)
✔ SQL for Moving Averages:
7-day moving average of sales
✔ Python for Forecasting (Using Prophet)
3️⃣ KPI & Metrics Analysis
KPIs (Key Performance Indicators) measure business performance.
✔ Common Business KPIs:
Revenue Growth Rate → (Current Revenue - Previous Revenue) / Previous Revenue
Customer Retention Rate → Customers at End / Customers at Start
Churn Rate → % of customers lost over time
Net Promoter Score (NPS) → Measures customer satisfaction
✔ SQL for KPI Analysis:
Calculate Monthly Revenue Growth
✔ Python for KPI Dashboard (Using Matplotlib)
4️⃣ Real-Life Use Cases of Data-Driven Decisions
📌 E-commerce: Optimize pricing based on customer demand trends.
📌 Finance: Predict stock prices using time series forecasting.
📌 Marketing: Improve email campaign conversion rates with A/B testing.
📌 Healthcare: Identify disease patterns using predictive analytics.
Mini Task for You: Write an SQL query to calculate the customer churn rate for a subnoscription-based company.
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
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Data-driven decision-making (DDDM) involves using data analytics to guide business strategies instead of relying on intuition. Key techniques include A/B testing, forecasting, trend analysis, and KPI evaluation.
1️⃣ A/B Testing & Hypothesis Testing
A/B testing compares two versions of a product, marketing campaign, or website feature to determine which performs better.
✔ Key Metrics in A/B Testing:
Conversion Rate
Click-Through Rate (CTR)
Revenue per User
✔ Steps in A/B Testing:
1. Define the hypothesis (e.g., "Changing the CTA button color will increase clicks").
2. Split users into Group A (control) and Group B (test).
3. Analyze differences using statistical tests.
✔ SQL for A/B Testing:
Calculate average purchase per user in two test groups
SELECT test_group, AVG(purchase_amount) AS avg_purchase
FROM ab_test_results
GROUP BY test_group;
Run a t-test to check statistical significance (Python)
from scipy.stats import ttest_ind
t_stat, p_value = ttest_ind(group_A['conversion_rate'], group_B['conversion_rate'])
print(f"T-statistic: {t_stat}, P-value: {p_value}")
🔹 P-value < 0.05 → Statistically significant difference.
🔹 P-value > 0.05 → No strong evidence of difference.
2️⃣ Forecasting & Trend Analysis
Forecasting predicts future trends based on historical data.
✔ Time Series Analysis Techniques:
Moving Averages (smooth trends)
Exponential Smoothing (weights recent data more)
ARIMA Models (AutoRegressive Integrated Moving Average)
✔ SQL for Moving Averages:
7-day moving average of sales
SELECT order_date,
sales,
AVG(sales) OVER (ORDER BY order_date ROWS BETWEEN 6 PRECEDING AND CURRENT ROW) AS moving_avg
FROM sales_data;
✔ Python for Forecasting (Using Prophet)
from fbprophet import Prophet
model = Prophet()
model.fit(df)
future = model.make_future_dataframe(periods=30)
forecast = model.predict(future)
model.plot(forecast)
3️⃣ KPI & Metrics Analysis
KPIs (Key Performance Indicators) measure business performance.
✔ Common Business KPIs:
Revenue Growth Rate → (Current Revenue - Previous Revenue) / Previous Revenue
Customer Retention Rate → Customers at End / Customers at Start
Churn Rate → % of customers lost over time
Net Promoter Score (NPS) → Measures customer satisfaction
✔ SQL for KPI Analysis:
Calculate Monthly Revenue Growth
SELECT month,
revenue,
LAG(revenue) OVER (ORDER BY month) AS prev_month_revenue,
(revenue - prev_month_revenue) / prev_month_revenue * 100 AS growth_rate
FROM revenue_data;
✔ Python for KPI Dashboard (Using Matplotlib)
import matplotlib.pyplot as plt
plt.plot(df['month'], df['revenue_growth'], marker='o')
plt.noscript('Monthly Revenue Growth')
plt.xlabel('Month')
plt.ylabel('Growth Rate (%)')
plt.show()
4️⃣ Real-Life Use Cases of Data-Driven Decisions
📌 E-commerce: Optimize pricing based on customer demand trends.
📌 Finance: Predict stock prices using time series forecasting.
📌 Marketing: Improve email campaign conversion rates with A/B testing.
📌 Healthcare: Identify disease patterns using predictive analytics.
Mini Task for You: Write an SQL query to calculate the customer churn rate for a subnoscription-based company.
Data Analyst Roadmap: 👇
https://news.1rj.ru/str/sqlspecialist/1159
Like this post if you want me to continue covering all the topics! ❤️
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Quick recap of essential SQL basics 😄👇
SQL is a domain-specific language used for managing and querying relational databases. It's crucial for interacting with databases, retrieving, storing, updating, and deleting data. Here are some fundamental SQL concepts:
1. Database
- A database is a structured collection of data. It's organized into tables, and SQL is used to manage these tables.
2. Table
- Tables are the core of a database. They consist of rows and columns, and each row represents a record, while each column represents a data attribute.
3. Query
- A query is a request for data from a database. SQL queries are used to retrieve information from tables. The SELECT statement is commonly used for this purpose.
4. Data Types
- SQL supports various data types (e.g., INTEGER, TEXT, DATE) to specify the kind of data that can be stored in a column.
5. Primary Key
- A primary key is a unique identifier for each row in a table. It ensures that each row is distinct and can be used to establish relationships between tables.
6. Foreign Key
- A foreign key is a column in one table that links to the primary key in another table. It creates relationships between tables in a database.
7. CRUD Operations
- SQL provides four primary operations for data manipulation:
- Create (INSERT) - Add new records to a table.
- Read (SELECT) - Retrieve data from one or more tables.
- Update (UPDATE) - Modify existing data.
- Delete (DELETE) - Remove records from a table.
8. WHERE Clause
- The WHERE clause is used in SELECT, UPDATE, and DELETE statements to filter and conditionally manipulate data.
9. JOIN
- JOIN operations are used to combine data from two or more tables based on a related column. Common types include INNER JOIN, LEFT JOIN, and RIGHT JOIN.
10. Index
- An index is a database structure that improves the speed of data retrieval operations. It's created on one or more columns in a table.
11. Aggregate Functions
- SQL provides functions like SUM, AVG, COUNT, MAX, and MIN for performing calculations on groups of data.
12. Transactions
- Transactions are sequences of one or more SQL statements treated as a single unit. They ensure data consistency by either applying all changes or none.
13. Normalization
- Normalization is the process of organizing data in a database to minimize data redundancy and improve data integrity.
14. Constraints
- Constraints (e.g., NOT NULL, UNIQUE, CHECK) are rules that define what data is allowed in a table, ensuring data quality and consistency.
Here is an amazing resources to learn & practice SQL: https://bit.ly/3FxxKPz
Share with credits: https://news.1rj.ru/str/sqlspecialist
Hope it helps :)
SQL is a domain-specific language used for managing and querying relational databases. It's crucial for interacting with databases, retrieving, storing, updating, and deleting data. Here are some fundamental SQL concepts:
1. Database
- A database is a structured collection of data. It's organized into tables, and SQL is used to manage these tables.
2. Table
- Tables are the core of a database. They consist of rows and columns, and each row represents a record, while each column represents a data attribute.
3. Query
- A query is a request for data from a database. SQL queries are used to retrieve information from tables. The SELECT statement is commonly used for this purpose.
4. Data Types
- SQL supports various data types (e.g., INTEGER, TEXT, DATE) to specify the kind of data that can be stored in a column.
5. Primary Key
- A primary key is a unique identifier for each row in a table. It ensures that each row is distinct and can be used to establish relationships between tables.
6. Foreign Key
- A foreign key is a column in one table that links to the primary key in another table. It creates relationships between tables in a database.
7. CRUD Operations
- SQL provides four primary operations for data manipulation:
- Create (INSERT) - Add new records to a table.
- Read (SELECT) - Retrieve data from one or more tables.
- Update (UPDATE) - Modify existing data.
- Delete (DELETE) - Remove records from a table.
8. WHERE Clause
- The WHERE clause is used in SELECT, UPDATE, and DELETE statements to filter and conditionally manipulate data.
9. JOIN
- JOIN operations are used to combine data from two or more tables based on a related column. Common types include INNER JOIN, LEFT JOIN, and RIGHT JOIN.
10. Index
- An index is a database structure that improves the speed of data retrieval operations. It's created on one or more columns in a table.
11. Aggregate Functions
- SQL provides functions like SUM, AVG, COUNT, MAX, and MIN for performing calculations on groups of data.
12. Transactions
- Transactions are sequences of one or more SQL statements treated as a single unit. They ensure data consistency by either applying all changes or none.
13. Normalization
- Normalization is the process of organizing data in a database to minimize data redundancy and improve data integrity.
14. Constraints
- Constraints (e.g., NOT NULL, UNIQUE, CHECK) are rules that define what data is allowed in a table, ensuring data quality and consistency.
Here is an amazing resources to learn & practice SQL: https://bit.ly/3FxxKPz
Share with credits: https://news.1rj.ru/str/sqlspecialist
Hope it helps :)
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Data Analytics
Data-Driven Decision Making Data-driven decision-making (DDDM) involves using data analytics to guide business strategies instead of relying on intuition. Key techniques include A/B testing, forecasting, trend analysis, and KPI evaluation. 1️⃣ A/B Testing…
Data Storytelling & Communication
Data storytelling is the art of transforming data insights into compelling narratives that help stakeholders make informed decisions. It involves visualization, presentation skills, and dashboard design.
1️⃣ Why Data Storytelling Matters
🚀 Bridges the Gap → Translates complex data into actionable insights.
🚀 Engages Stakeholders → Helps non-technical audiences understand key takeaways.
🚀 Drives Decisions → Turns raw numbers into meaningful business strategies.
✔ Example: Instead of saying "Sales dropped by 15% last quarter",
→ Tell a story: "Due to a seasonal decline and increased competition, our sales dipped 15% in Q4. However, targeting high-performing regions with a discount campaign increased customer retention by 10%."
2️⃣ The 3 Key Elements of Data Storytelling
🔹 1. Data → Accurate, well-processed information.
🔹 2. Narrative → A logical flow that explains why the data matters.
🔹 3. Visuals → Graphs, charts, and dashboards that enhance understanding.
✔ Example:
BAD: A dashboard cluttered with too many numbers and graphs.
GOOD: A simple, focused visualization that highlights the most important KPI.
3️⃣ How to Structure a Data Story
✔ 1. Set the Context – What problem are we solving?
✔ 2. Present the Data – Use relevant visuals (bar charts, line graphs, heatmaps).
✔ 3. Explain the Insights – What trends, patterns, or outliers do we see?
✔ 4. Recommend an Action – What should the business do next?
✔ Example:
Scenario: A retail company sees a drop in sales.
Context: "Over the last 3 months, sales have declined by 12%."
Data Insight: "Our analysis shows that this is due to lower engagement in younger age groups."
Actionable Insight: "Introducing a new loyalty program for customers under 30 could increase retention by 20%."
4️⃣ Best Practices for Dashboard Design
✔ Keep It Simple: Show only essential KPIs.
✔ Use Consistent Colors & Formatting: Make it visually appealing.
✔ Prioritize Interactivity: Enable filters and drill-downs.
✔ Highlight Key Metrics: Use callouts for important numbers.
✔ Example:
📊 A Sales Performance Dashboard should include:
Total Revenue (KPI Card)
Sales Trend (Line Chart)
Top-Selling Products (Bar Chart)
Region-wise Performance (Map Visualization)
5️⃣ Tools for Data Storytelling
📌 Power BI & Tableau → Create interactive dashboards.
📌 Google Data Studio → Great for real-time reporting.
📌 Python (Matplotlib, Seaborn, Plotly) → Advanced data visualization.
📌 Excel → Quick visual summaries using Pivot Charts.
Mini Task for You: Create a Power BI or Tableau dashboard that tells a story about sales performance over the last 6 months.
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 :)
Data storytelling is the art of transforming data insights into compelling narratives that help stakeholders make informed decisions. It involves visualization, presentation skills, and dashboard design.
1️⃣ Why Data Storytelling Matters
🚀 Bridges the Gap → Translates complex data into actionable insights.
🚀 Engages Stakeholders → Helps non-technical audiences understand key takeaways.
🚀 Drives Decisions → Turns raw numbers into meaningful business strategies.
✔ Example: Instead of saying "Sales dropped by 15% last quarter",
→ Tell a story: "Due to a seasonal decline and increased competition, our sales dipped 15% in Q4. However, targeting high-performing regions with a discount campaign increased customer retention by 10%."
2️⃣ The 3 Key Elements of Data Storytelling
🔹 1. Data → Accurate, well-processed information.
🔹 2. Narrative → A logical flow that explains why the data matters.
🔹 3. Visuals → Graphs, charts, and dashboards that enhance understanding.
✔ Example:
BAD: A dashboard cluttered with too many numbers and graphs.
GOOD: A simple, focused visualization that highlights the most important KPI.
3️⃣ How to Structure a Data Story
✔ 1. Set the Context – What problem are we solving?
✔ 2. Present the Data – Use relevant visuals (bar charts, line graphs, heatmaps).
✔ 3. Explain the Insights – What trends, patterns, or outliers do we see?
✔ 4. Recommend an Action – What should the business do next?
✔ Example:
Scenario: A retail company sees a drop in sales.
Context: "Over the last 3 months, sales have declined by 12%."
Data Insight: "Our analysis shows that this is due to lower engagement in younger age groups."
Actionable Insight: "Introducing a new loyalty program for customers under 30 could increase retention by 20%."
4️⃣ Best Practices for Dashboard Design
✔ Keep It Simple: Show only essential KPIs.
✔ Use Consistent Colors & Formatting: Make it visually appealing.
✔ Prioritize Interactivity: Enable filters and drill-downs.
✔ Highlight Key Metrics: Use callouts for important numbers.
✔ Example:
📊 A Sales Performance Dashboard should include:
Total Revenue (KPI Card)
Sales Trend (Line Chart)
Top-Selling Products (Bar Chart)
Region-wise Performance (Map Visualization)
5️⃣ Tools for Data Storytelling
📌 Power BI & Tableau → Create interactive dashboards.
📌 Google Data Studio → Great for real-time reporting.
📌 Python (Matplotlib, Seaborn, Plotly) → Advanced data visualization.
📌 Excel → Quick visual summaries using Pivot Charts.
Mini Task for You: Create a Power BI or Tableau dashboard that tells a story about sales performance over the last 6 months.
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 :)
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