Dharsan C’s Post

As part of my journey into Data Analytics, I continued strengthening my SQL skills today by practicing data filtering techniques in MySQL Workbench. Today’s focus was on: • Using the WHERE clause to filter specific records • Applying logical operators like AND, OR, and NOT • Working with the LIKE statement to search for patterns within datasets Practicing these queries is helping me better understand how analysts extract meaningful information from raw data. Going back to the fundamentals is improving my confidence and accuracy when working with databases. Sharing a snapshot of my practice session as I continue learning and growing step by step. Consistency is key, and I’m committed to becoming better every day. #SQL #DataAnalytics #LearningJourney #WomenInTech #MySQL

Behind every clean dataset, there’s a lot of unseen work. While working with MySQL Workbench, I explored how raw user data is actually managed before any analysis begins. From inserting records to updating incorrect entries and removing unwanted data, every step required precision. I worked on a simple users dataset and focused on: • Structuring data into meaningful tables • Handling updates without disturbing existing records • Cleaning data by removing inconsistencies • Running queries to verify accuracy What stood out to me was how small query mistakes can completely change the dataset — and how important it is to understand data at a deeper level, not just surface-level queries. This experience gave me a clearer perspective on how data is prepared, maintained, and trusted before it’s ever used for insights or visualization. #SQL #MySQL #DataAnalytics #DataHandling #LearningJourney

Explored data aggregation techniques using SQL, focusing on functions like SUM, COUNT, AVG, MIN, and MAX to extract meaningful insights from datasets. Worked with grouping and filtering using GROUP BY and HAVING clauses, strengthening my understanding of how structured data can be analyzed efficiently using MySQL. This helped bridge the gap between raw data and actionable insights. #SQL #DataAnalysis #Databases

#DAY 12 I recently worked on importing CSV data into an SQL database—an essential skill for anyone dealing with real-world datasets. Here’s a quick overview of the process I followed: 1. **Understanding the Data** – Reviewed the CSV file structure, checked for missing values, and ensured consistency. 2. **Creating the Database Table** – Designed the SQL table with appropriate data types to match the dataset. 3. **Importing the CSV File** – Used SQL commands like `LOAD DATA INFILE` (MySQL) or `BULK INSERT` (SQL Server) to efficiently transfer data. 4. **Data Validation** – Verified successful import and performed basic queries to ensure accuracy. This hands-on experience helped me strengthen my understanding of data handling, database management, and practical SQL applications. Looking forward to exploring more data-driven projects and enhancing my technical skill set! #SQL #DataManagement #LearningJourney #Data Analytics

📊 SQL Data Types: The Foundation Every Data Analyst Must Get Right When I started working with SQL, I used to think data types were just a formality. But I quickly realized choosing the right data type can make or break your database performance. Here’s a simple breakdown 👇 🔹 Numeric Data Types Used for numbers INT → whole number DECIMAL → precise values FLOAT → approximate values 🔹 String Data Types Used for text VARCHAR → most commonly used CHAR → fixed length values TEXT → large content 🔹 Date & Time Data Types Used for tracking events DATE → only date TIME → only time TIMESTAMP → date + time 🔹 Boolean Data Type Used for true/false Example: IsActive, IsPaid 🔹 Binary Data Types Used for storing files Example: images, documents 💡 What I learned the hard way: Using the wrong data type can slow down queries and waste storage. Now I always ask: “Do I really need this much space and precision?” That one question improved my SQL design a lot. If you're learning SQL, don’t skip this topic, it’s more important than it looks. #SQL #DataAnalytics #DataScience #LearningSQL #Database #TechSkills

Today I learned how to import a database in MySQL Workbench and start working with real data. Before this, I was mostly creating small tables manually. But importing an existing database makes it easier to practice on structured, real-world datasets. This step is important because in data science, you rarely start from scratch. Most of the time, you work with already available data and focus on cleaning, analyzing, and extracting insights from it. Right now, I’m learning SQL for data science and building my skills step by step. #MySQL #SQL #DataScience #DataAnalytics #Database #LearningJourney #Beginner

SQL CHEAT SHEET 📝👩💻 SQL is a language used to communicate with databases it stands for Structured Query Language and is used by database administrators and developers alike to write queries that are used to interact with the database. Here is a quick cheat sheet of some of the most essential SQL commands: SELECT - Retrieves data from a database UPDATE - Updates existing data in a database DELETE - Removes data from a database INSERT - Adds data to a database CREATE - Creates an object such as a database or table ALTER - Modifies an existing object in a database DROP -Deletes an entire table or database ORDER BY - Sorts the selected data in an ascending or descending order WHERE – Condition used to filter a specific set of records from the database GROUP BY - Groups a set of data by a common parameter HAVING - Allows the use of aggregate functions within the query JOIN - Joins two or more tables together to retrieve data INDEX - Creates an index on a table, to speed up search times. #SQL #dataAnalytics #Data #Analysis #database

🚀 Day 9 of My SQL Learning Journey — Understanding the HAVING Clause 📊 Today I learned a small SQL concept that makes a big difference in real-world data analysis — HAVING. Most people stop at WHERE. But real insights start after grouping data. 🔹 WHERE filters rows 🔹 HAVING filters grouped results 👉 That means HAVING helps answer questions like: ✔ Which departments have more employees? ✔ Which customers appear multiple times? ✔ Which groups cross a certain threshold? 💡 Key Realisation: Data analysis isn’t just about retrieving rows — it’s about filtering meaningful summaries. Learning SQL this way is changing how I think about data, not just how I write queries. 📈 One concept at a time. Done right. On to Day 10… 🚀 #SQL #DataAnalytics #LearningInPublic #MySQL #HAVING #Consistency #CareerGrowth #Placements #CSE

🚀 MySQL: Moving Beyond Beginner Level :- Ready to level up your SQL skills? Focus on these powerful MySQL concepts to handle real-world data problems more effectively: 🔹 JOINs – Combine data from multiple tables 🔹 Subqueries – Simplify complex queries step by step 🔹 GROUP BY + HAVING – Perform advanced data aggregation 🔹 CASE – Apply conditional logic within queries 🔹 Window Functions – Analyze trends without losing row-level data 🔹 CTEs – Write clean, structured, and readable queries 💡 Mastering these will elevate your data analysis skills and prepare you for real industry challenges. #MySQL #SQL #DataAnalytics #CareerGrowth #Learning

🚀 Just practiced importing CSV data into MySQL using SQL! In this task, I: ✅ Created a database and table ✅ Designed a structured schema for customer data ✅ Used LOAD DATA LOCAL INFILE to efficiently import CSV data into MySQL ✅ Handled delimiters, line breaks, and ignored headers properly This is a powerful technique for working with real-world datasets and is widely used in data engineering and analytics workflows. 💡 Learning how to move data from files into databases is a key step toward becoming a Data Engineer. #SQL #MySQL #DataEngineering #Database #Learning #TechSkills #CSV #DataScienceJourney