DEV Community: Ashok Kumar

DCL & TCL in SQL

Ashok Kumar — Mon, 22 Dec 2025 09:33:00 +0000

-- whats the use of DCL and TCL commands in SQL? Provide examples for each.
DCL (Data Control Language) and TCL (Transaction Control Language) are two categories of SQL commands used to manage database access and transactions, respectively.
DCL (Data Control Language):
DCL commands are used to control access to data in the database. The two primary DCL commands are GRANT and REVOKE.

GRANT: This command is used to give users access privileges to the database. Example:

   GRANT SELECT, INSERT ON Employees TO user1;

This command grants user1 the privileges to SELECT and INSERT data into the Employees table.

REVOKE: This command is used to remove access privileges from users.
Example:
```
REVOKE INSERT ON Employees FROM user1;
```
This command revokes the INSERT privilege from user1 on the Employees table.
TCL (Transaction Control Language):
TCL commands are used to manage transactions in the database. The primary TCL commands are COMMIT, ROLLBACK, and SAVEPOINT.
COMMIT: This command is used to save all changes made during the current transaction.
Example:

   BEGIN TRANSACTION;
   UPDATE Employees SET Salary = Salary * 1.1 WHERE Department = 'Sales';
   COMMIT;

This sequence of commands starts a transaction, updates the salaries of employees in the Sales department, and then commits the changes to the database.

ROLLBACK: This command is used to undo changes made during the current transaction. Example:

   BEGIN TRANSACTION;
   UPDATE Employees SET Salary = Salary * 1.1 WHERE Department = 'Sales';
   ROLLBACK;

In this case, the salary update is undone, and no changes are made to the database.

SAVEPOINT: This command is used to set a point within a transaction to which you can later roll back.
Example:
```
BEGIN TRANSACTION;
UPDATE Employees SET Salary = Salary * 1.1 WHERE Department = 'Sales';
SAVEPOINT SalesUpdate;
UPDATE Employees SET Salary = Salary * 1.2 WHERE Department = 'Marketing';
ROLLBACK TO SalesUpdate;
COMMIT;
```
Here, a savepoint named SalesUpdate is created after updating the Sales department. If needed, you can roll back to this savepoint,
undoing only the changes made after it (the Marketing update), and then commit the remaining changes.

Pandas Task:

Ashok Kumar — Mon, 27 Oct 2025 06:17:45 +0000

💻 Completed Full Data Analysis Process Using Python (Pandas) 📊

I’m happy to share that I’ve successfully completed all 5 Phases of the Data Analysis workflow step by step:

🧩 Phase 1: Data Understanding
Loaded dataset, reviewed column types, and understood the meaning of each field.

🧹 Phase 2: Data Cleaning
Handled missing values, removed duplicates, corrected data types, and formatted dates properly.

⚙️ Phase 3: Data Preparation
Created new calculated columns such as Total Sales Value, Profit Margin %, and Month from Date to enrich the dataset.

🔍 Phase 4: Data Exploration
Explored key insights like total profit by region, top-performing salespersons, and product discounts.

📈 Phase 5: Aggregation & Insights
Compared sales and profits across regions, analyzed discount impact, and identified top 5 profitable products.

This hands-on exercise helped me understand how data cleaning and exploration lead to valuable business insights.
Excited to move towards Data Visualization next using Power BI / Tableau! 🚀

Git Hub Link - https://lnkd.in/eyUQrcgu

hashtag#DataAnalytics hashtag#Python hashtag#Pandas hashtag#DataCleaning hashtag#DataExploration hashtag#DataScience hashtag#LearningJourney

🐼 Pandas DataFrame Selection, Filtering & Cleaning — Hands-on Practice.

Ashok Kumar — Fri, 10 Oct 2025 18:46:16 +0000

As part of my Data Analytics learning journey, I created a small Pandas practice script that covers the most used operations in real-world data cleaning and analysis.

🔹 What I Practiced

Creating a DataFrame using Python dictionaries

Selecting columns and rows using loc[] and iloc[]

Row slicing and multiple row selection

Conditional filtering using operators and isin()

Handling missing data using:

isnull()

dropna()

fillna() with custom or mean values

🔹 Why This Matters

These are the foundation skills every Data Analyst needs before moving to advanced topics like merge, groupby, and visualization.
It helped me understand how to access, filter, and clean datasets efficiently.

🔹 Code & Repo

👉 Check out the complete code on GitHub:
🔗 GitHub Repository : https://github.com/Ashokkumarrk/Pandas-Practices/tree/main

Python #Pandas #DataAnalytics #DataCleaning #LearningJourney #Ashokkumar

Employee Data sales Dashboard

Ashok Kumar — Wed, 08 Oct 2025 05:56:20 +0000

💼 Employee Sales Dashboard using Power BI

(https://github.com/Ashokkumarrk/Employee_Data_Sales_Dashboard)

📊 Project Overview

This project is an Employee Sales Dashboard built using Power BI, designed to visualize employee performance and sales trends across departments.

It helps businesses make data-driven decisions by identifying top performers and analyzing sales growth patterns.

⚙ Tools & Technologies Used

Power BI – for dashboard design and DAX measures
Excel / CSV dataset – for employee sales data
Power Query – for data transformation and cleaning
DAX – for calculated columns and KPIs

🧩 Dashboard Features

👨‍💼 Employee performance by department
💰 Sales revenue trends (monthly/quarterly/yearly)
🏆 Top-performing employees
📈 Department-wise total sales comparison
📊 Filters for department, region, and performance level

🧮 Key DAX Measures


DAX
Total Sales = SUM(Sales[Amount])
Average Sales = AVERAGE(Sales[Amount])
Total Employees = DISTINCTCOUNT(Employee[EmployeeID])
Performance Score = DIVIDE(SUM(Sales[Amount]), [Average Sales])

IPL Data Analysis Dashboard

Ashok Kumar — Wed, 08 Oct 2025 05:50:44 +0000

🏏 IPL Data Analysis Dashboard using Power BI

Project LINK:(https://github.com/Ashokkumarrk/IPL-Data-Analysis-Dashboard)

📊 Project Overview

This project focuses on analyzing IPL (Indian Premier League) data using Power BI to uncover key insights about players, teams, and match performance.

The dashboard provides a comprehensive visual analysis of IPL data including:

Top run scorers & wicket takers
Team performance trends
Win percentages by toss decisions
Player comparisons
Year-wise analysis

⚙ Tools & Technologies Used

Power BI for data visualization
Excel / CSV dataset for IPL data
DAX for calculated columns and measures
Power Query for data cleaning and transformation

🧩 Dashboard Features

🎯 Player-wise Performance Analysis
🏆 Team Stats & Yearly Trends
📅 Match Analysis (Home/Away)
📈 Interactive filters for teams, venues, and seasons

🧮 Key DAX Measures Used


DAX
Total Runs = SUM(Batting[Runs])
Total Wickets = SUM(Bowling[Wickets])
Strike Rate = DIVIDE(SUM(Batting[Runs]), SUM(Batting[Balls Faced])) * 100
Win Percentage = DIVIDE([Total Wins], [Total Matches]) * 100

Build2Learn Event

Ashok Kumar — Mon, 29 Sep 2025 07:57:05 +0000

✨ My First Project Presentation Journey! ✨

I had the wonderful opportunity to present my IPL Data Analysis Dashboard (2008–2025) using Python & Power BI 📊.
This was my very first project presentation, and the experience was both challenging and exciting!

Through this project, I:
✅ Cleaned and processed raw IPL datasets
✅ Built interactive dashboards in Power BI
✅ Analyzed top players, venues, and match-winning patterns

It was also an amazing experience to collaborate with my peers and learn from each other. 🙌
Grateful to my mentors and teammates for the constant support.

This is just the beginning of my Data Analytics journey, and I’m looking forward to working on more real-world projects. 🚀

PowerBI #Python #DataAnalytics #IPL #LearningJourney #FirstPresentation

SQL Database Concepts:

Ashok Kumar — Mon, 15 Sep 2025 09:35:58 +0000

💡 Sharing my key takeaways from today’s study session:

1️⃣ SQL vs NoSQL

SQL → Structured data, fixed schema, relational tables.

NoSQL → Unstructured/semi-structured data, flexible schema, handles big & dynamic data.

Use SQL when data is consistent & relational, NoSQL when data is large, scalable & diverse.

2️⃣ SQL vs MySQL

SQL → Language (Structured Query Language) used to manage & query relational databases.

MySQL → A Database Management System (RDBMS) that implements SQL.
👉 In short: SQL = Language | MySQL = Software using that language.

3️⃣ DBMS vs RDBMS

DBMS → Database system (stores data, no strict relationships). Example: MS Access.

RDBMS → DBMS + relationships between tables (uses keys, constraints). Example: MySQL, Oracle.
👉 RDBMS is an advanced form of DBMS.

4️⃣ Stored Procedures vs Functions

Stored Procedure → Set of SQL statements stored in DB, can return multiple values, used for business logic.

Function → Must return a single value, mainly used for computations.
👉 Procedure = multi-purpose, Function = calculation focused.

✅ Excited to keep learning & sharing more database concepts.

SQL #NoSQL #DBMS #RDBMS

DataAnalytics #LearningJourney #Database

concat() vs Group_concat()

Ashok Kumar — Sun, 14 Sep 2025 11:57:43 +0000

🚀 SQL – CONCAT() vs GROUP_CONCAT()

When I started learning SQL, I got confused between CONCAT and GROUP_CONCAT. Both look similar, but their usage is very different. Here’s a simple breakdown with examples 👇

🔹 CONCAT()

Combines column values within the same row.

Example: Joining first_name + last_name → NICK WAHLBERG

Works only at row-level.
📸 (Refer first image)

🔹 GROUP_CONCAT()

Combines values from multiple rows into a single string.

Example: Grouping all cate_id values for each pub_id.

Usually used with GROUP BY.
📸 (Refer second image)

⚡ Key Difference:

Use CONCAT → row-wise string merge.

Use GROUP_CONCAT → row aggregation into one string.

💡 This is a common interview question. Beginners in SQL often confuse these two, so keeping this clear helps in both projects + interviews.

👉 If you’re learning SQL, what other functions confuse you? Let’s discuss!

SQL #Database #DataAnalytics #Learning

SQL Mini project :WC24 T20 Data Analytics

Ashok Kumar — Wed, 03 Sep 2025 17:51:06 +0000

🏏 World Cup 2024 Stats Analysis with MySQL:

I recently built a small side project to analyze the ICC Cricket World Cup 2024 using MySQL. The goal was to write efficient queries to explore player performances, team stats, match results, and more — all using raw SQL.

💻 What I Did

Designed a simple relational schema for teams, players, matches, and performance stats

Wrote MySQL queries to:

Find top run-scorers & wicket-takers

Get win/loss summaries for each team

Analyze match results

Extract player-wise contributions

Practiced JOIN, GROUP BY, SUM, and ORDER BY for real-world analysis

🔗 GitHub Repo

You can check out the project and the SQL queries here:
👉 GitHub - World Cup 2024 Stats (SQL)

(https://github.com/Ashokkumarrk/SQL-Practices.git)

⚙️ Tools Used

MySQL for all queries

Sample data manually created (can be extended using real datasets later)

🧠 Why I Built This

I wanted to combine my love for cricket with learning SQL in a more practical, fun way. Sports analytics is a great playground for data lovers, and this project helped me get more confident with MySQL.

sql #mysql #cricket #worldcup #dataanalytics #devproject

Day 5 : Learned about Python Algorithms..

Ashok Kumar — Fri, 29 Aug 2025 07:30:13 +0000

📌 Today After learned about lists & slicing i'll come to this:

I started learning Python Algorithms and covered:

Cards (Arrays/Lists): storing data in order

Matrix: working with data in rows & columns

Tree: understanding parent–child data structure

Pseudocode: writing logic in plain English before coding

👉 Step by step I’m building my DSA foundation 💻✨
Tommorrow I will start PYTHON Dictionaries....

DAY: 5 - In Python all list method operators and slicing:

Ashok Kumar — Fri, 29 Aug 2025 06:25:32 +0000

Today, I completed learning List Methods and Slicing in Python.
Both are very important fundamentals in Python programming.

🔹 List Methods

Python provides several built-in methods to work with lists effectively:

append → adds an item at the end of the list

extend → adds multiple items to the list

insert → inserts an item at a specific position

remove → removes the first matching element

pop → removes an element by index (default is the last element)

clear → empties the list completely

sort → arranges elements in ascending order

reverse → reverses the order of elements in the list

🔹 Slicing

Slicing is the process of extracting a portion of a list (or string) using index values.

[:] → returns the full list

[start:end] → extracts elements from start to end-1

[::step] → extracts with a step size (skips elements)

Negative indexes allow slicing from the end of the list.

✨ Conclusion

I successfully understood List Methods and Slicing today ✅
This strengthens my Python fundamentals, and tomorrow I will continue with Dictionaries.

🚀 My Python Practice – Day 1

Ashok Kumar — Tue, 26 Aug 2025 10:11:10 +0000

✅ Topics Covered Today

🔹 1. Fibonacci Sequence

Using while loop

Using for + range

Recursion method

Memoization (efficient recursion)

🔹 2. Palindrome Programs

String palindrome check (using slicing)

Number palindrome check (without converting to string)

🔹 3. Multiplication Tables

Single table (for loop)

Nested loop → 1 to 10 tables

Side-by-side matrix style (like a grid)

🔹 4. Star Patterns (nested loops)

Right angled triangle

Inverted triangle

Pyramid & Diamond

Hollow square ---

✨ Key Learnings Today

Difference between for and while

Usage of range(start, stop, step)

Nested if for multiple conditions

Nested loops for grids and star patterns