SQL Fundamentals Day 1 Basics DDL DML DQL TCL

𝗗𝗮𝘆 𝟲𝟳/𝟳𝟱 | 𝗟𝗲𝗲𝘁𝗖𝗼𝗱𝗲 𝟳𝟱 𝗣𝗿𝗼𝗯𝗹𝗲𝗺: 338. Counting Bits 𝗗𝗶𝗳𝗳𝗶𝗰𝘂𝗹𝘁𝘆: Easy 𝗣𝗿𝗼𝗯𝗹𝗲𝗺 𝗦𝘂𝗺𝗺𝗮𝗿𝘆: Given an integer n, return an array where each index i (0 ≤ i ≤ n) contains the number of 1’s in the binary representation of i. 𝗠𝘆 𝗔𝗽𝗽𝗿𝗼𝗮𝗰𝗵: This problem is solved efficiently using Dynamic Programming with bit manipulation. Instead of converting each number to binary separately, we reuse previously computed results. • Key Observation: – Right shifting a number (i >> 1) removes the last bit – (i & 1) tells whether the last bit is 1 or 0 • Transition: – ans[i] = ans[i >> 1] + (i & 1) This means: Take the count of 1’s from i/2 and add 1 if the current number is odd. • Base Case: – ans[0] = 0 • Final answer: – Array ans of size n + 1 This avoids repeated binary conversions and builds the solution in a bottom-up manner. 𝗖𝗼𝗺𝗽𝗹𝗲𝘅𝗶𝘁𝘆 𝗔𝗻𝗮𝗹𝘆𝘀𝗶𝘀: • Time Complexity: O(n) • Space Complexity: O(n) 𝗞𝗲𝘆 𝗧𝗮𝗸𝗲𝗮𝘄𝗮𝘆: Problems involving binary representations often have patterns that can be reused. Bit manipulation + DP is a powerful combination for optimizing such computations. 𝗤𝘂𝗲𝘀𝘁𝗶𝗼𝗻 𝗟𝗶𝗻𝗸: https://lnkd.in/gydkB_rv #Day67of75 #LeetCode75 #DSA #Java #Python #DynamicProgramming #BitManipulation #MachineLearning #DataScience #ML #DataAnalyst #LearningInPublic #TechJourney #LeetCode

🚀 Day 14 – SQL Challenge “Find students who know C”… Simple? Not when your data looks like this: 👉 C, Python, C++ Now the real question: How do you match only ‘C’ and ignore C++ / C#? I explored multiple approaches 👇 🔹 1. LIKE-based logic (pattern matching) Works, but gets messy with multiple conditions. 🔹 2. Split-based approach using SUBSTRING_INDEX Simulates splitting and gives more control: 🔹 3. FIND_IN_SET (simple & effective) Sometimes the simplest solution wins: WHERE FIND_IN_SET('C', skills) 🔥 Key Insight: There’s no single “best” solution in SQL. It’s about choosing between readability, scalability, and simplicity based on the situation. Which approach would you pick? 👇 Thanks for the suggestion, Ratan Kumar jha! 🙌 Tried a split-based approach as well using SUBSTRING_INDEX to simulate splitting in MySQL. Really helped make the logic cleaner and more structured. #SQL #DataAnalytics #SQLChallenge #ProblemSolving #LearningInPublic

Day 04 #StudyClub SQL, we learned and implemented indexing strategies. The purpose of this indexing is to reduce the time it takes for the database to process our queries, due to the large volume of data. With 72 million records, processing a single query without an index would take an extremely long time. Mentor: Hilmi ‎. Team: Monika Hermiani Yolanda Simamora Irpan Pilihan Rambe nadhira M. Aprida Sapitri Br Saragih Dyah Ayu Goldy Cahyastuti Medina Uli Alba Somala Siti Komala Hafsaninda MR Rintaldi Ghazian Hindami Reihan Nanda R. #SQLDay #MySQL #EntityRelationshipDiagram #ManifoldDatafolk #DatabaseDesignPrinciple #StudyClubDay4 #Python #ETLProcess #IndexingStrategies

Day 03 #StudyClub SQL, we performed data transformation and loaded the data into the database we created on Day 02. This time, we used Python for the transformation and loading process into MySQL. Stay tuned for the next episode to find out the results and the ultimate goal of this #StudyClub SQL episode. Fyi, we successfully load 72 million records of data. We encountered many errors and issues during this process. I've already written down all the issues and their solutions in the report. Mentor: Hilmi ‎. Team: Monika Hermiani Yolanda Simamora Irpan Pilihan Rambe nadhira M. Aprida Sapitri Br Saragih Dyah Ayu Goldy Cahyastuti Medina Uli Alba Somala Siti Komala Hafsaninda MR Rintaldi Ghazian Hindami Reihan Nanda R. #SQLDay #MySQL #EntityRelationshipDiagram #ManifoldDatafolk #DatabaseDesignPrinciple #StudyClubDay3 #Python #ETLProcess

Just finished building an ETL pipeline project in Python — reading, cleaning, and analyzing data from a multi-table bookstore dataset. The pipeline handles real-world data quality issues (missing values, invalid entries, orphan records), loads everything into SQLite, and runs reports using joins, CTEs, and window functions. Built with Python, pandas, and SQL as part of my data engineering learning path. UPDATED LINK here: https://lnkd.in/eBtEtXi3 #DataEngineering #Python #SQL #ETL

The most underrated skill in data isn’t Python or SQL. It’s patience. Most people see the final dashboard. They don’t see: - The 2 hours spent fixing a broken JOIN. - The hunt for the person who actually knows what the column names mean. - The "clean" dataset that was actually 40% null values. The truth? The "messy" part isn't a distraction from the work. It is the work. If your morning was more troubleshooting than "analyzing," you’re exactly where you need to be. The value is in the investigation, not just the chart. #DataAnalytics #SQL #DataLife #RealTalk

Currently revisiting SQL alongside Python 👨💻 I had learned SQL earlier, but like most people, I forgot many concepts. Now focusing on: SELECT, WHERE, ORDER BY GROUP BY Basic queries I can already see that SQL + Python together will be very powerful for Data Analytics. #SQL #DataAnalytics #LearningInPublic

Every new skill starts with confusion, errors, and a lot of debugging. Working with SQL connections and PostgreSQL through Python has been exactly that—figuring out errors, understanding why queries fail, and learning how databases actually function. But that’s also where the real learning happens. Step by step, I’m becoming more comfortable with querying data, managing connections, and thinking more logically about how information is stored and retrieved. It’s a process, but one that’s building a solid foundation for data analytics and future work in finance. #SQL #PostgreSQL #Python #DataAnalytics #DataScience #Finance #LearningByDoing #MittalschoolofBusiness #lpu

I’ve been developing prototype Python Pandas codes to transform observational data housed on an OMOP schema into a SDTM schema containing the tables, DM, and MH, in particular. My in-house setup is with MySQL on a bare metal Ubuntu Server. I realize that there are already R solutions to do this and I personally worked on a SAS solution, but it’s my my belief Python may be the best programming approach. Also I don’t have SAS, thus my interest in developing a set of codes using free tools, but it may have greater potential, overall, and it’s definitely worth the development time.

🎯 Tech Learning Journey - Day 09: SQLite Databases - Your Personal Data Vault! SQLite is like having a mini database built right into Python. It's a lightweight database that stores data in tables you can search, filter, and update - perfect for apps that need to remember information between runs. import sqlite3 # Create database and table conn = sqlite3.connect\('mydata.db'\) cursor = conn.cursor\(\) cursor.execute\('CREATE TABLE IF NOT EXISTS users \(id INTEGER PRIMARY KEY, name TEXT\)'\) # Insert and query data cursor.execute\("INSERT INTO users \(name\) VALUES \('Dhanush'\)"\) cursor.execute\("SELECT \* FROM users"\) print\(cursor.fetchall\(\)\) # \[\(1, 'Dhanush'\)\] conn.close\(\) Where I use this: Building apps that need persistent storage, logging user activity, and managing application data locally. #Python #Coding #Programming #Database #SQLite