Prashant Uswadkar’s Post

I recently had to practice python on a datasets. The datasets contains just 891 rows and 12 columns. At the beginning, I thought it would be easy since the datasets aren't many, but as time goes, I realised, there isn't small data. Every data requires patience and good skills, and your thinking brain. I will share the process soon, it's not something loud but it's growth. I am getting better at this thing called Data Analysis #growthsometimesdoesnotlookit. #proudself #futureselfisregistering

A small realization from my Data Analytics learning journey. When I started learning tools like SQL and Python, my focus was mostly on getting the right answer. If the query ran successfully or the code worked, I felt like I had solved the problem. But while practicing more datasets recently, I noticed something interesting. The first solution is rarely the best one. Sometimes a query works but: • it’s inefficient • it’s hard to read • or it doesn’t scale well with larger data That’s when I realized something important. Good data analysis isn’t just about making things work. It’s about writing queries and code that are clear, efficient, and easy to understand for others. #DataAnalytics #SQL #Python #LearningInPublic

✅Day 3 – Variables & Data Types in Python Today I learned the foundation of Python: **Variables and Data Types. --A variable stores data. --Data types define what kind of data it is. ✅Example: * String → "Name" * Integer → 10 * Float → 99.5 * Boolean → True/False In data analytics, understanding data types is very important. If we don’t know the type of data, we cannot analyze it correctly. Strong basics = Strong future skills. Step by step, improving every day. #Python #DataAnalytics #LearningJourney #Consistency

Learning every tool will not make you better. Clarity will. Start with the type and scale of your data, then align tools to your goal. Excel and SQL for summaries and dashboards. Python for deeper analysis and predictions. Focus beats overload. 📕 https://lnkd.in/d42rindX #DataAnalytics #DataScience #SQL #Python #Excel

A small but powerful data lesson I’ve been revisiting lately: SQL helps you ask the right questions. Python helps you explore the answers. SQL is incredible for: filtering large datasets aggregating data efficiently understanding what is happening Python shines when you want to: clean and transform messy data explore patterns and outliers visualise trends and test assumptions What I’m learning is that the real strength isn’t choosing one over the other — it’s knowing when to use each and how they work together in a data workflow. Strong data analysis isn’t about tools alone; it’s about clarity of thinking. #Python #SQL #DataAnalytics #OpenData #LearningInPublic #DataSkills #MScJourney

This morning we are starting a new session introducing some of my clients to SQL and Python. For many professionals and business teams, data often sits quietly inside databases and spreadsheets. The real power begins when you learn how to ask the right questions and extract insights from it. Today we begin with the foundations: How to query data using SQL How Python can help analyze and work with data more efficiently. My goal in sessions like this is simple — help people move from seeing data to understanding data. Because when people understand their data, they make better business decisions. Looking forward to a great learning session today and an impactful month ahead. #DataAnalytics #SQL #Python #BusinessIntelligence #DataEducation #NaijaDataProfessor

How to export and import files between Python and Excel? Stop manual work. Use these two snippets to automate your data workflow with pandas: Import: Read Excel files into Python for analysis. Export: Save results back to Excel (use index=False for a clean file). Simple, fast, and error-free. #Python #Excel #Pandas #Automation #DataAnalysis

Today I explored 3 key concepts: 🐍 Python — String Methods Practiced 10 built-in string functions: .upper(), .lower(), .strip(), .split(), .replace(), .find(), .count(), .startswith(), .endswith(), .join() 📊 Power BI — DAX Functions Took a test on DAX aggregation functions — SUM, AVERAGE, COUNT & CALCULATE. Revision is the best way to solidify concepts! 🧭 Aptitude — Logical Reasoning Worked on directional sense problems — N, S, E, W and left/right turns. Consistency is the key. #Python #PowerBI #DAX #Aptitude #LearningInPublic #DataAnalytics #100DaysOfLearning

📊 Excel, SQL & Python Formulas — Side‑by‑Side Guide This guide brings together some useful Excel formulas and shows their direct equivalents in SQL and Python. It’s a practical way to understand how the same logic translates across tools, perfect for anyone working across multiple data environments. A great reference to keep close when you want to move faster, switch between languages, and deepen your understanding of syntax differences. #Excel #SQL #Python #DataAnalytics #DataAnalysis

🚀 Day 27/100 – Python, Data Analytics & Machine Learning Journey 📊 Started SQL – The Backbone of Data Analytics Today I learned: 7. Aggregation Functions (COUNT(), SUM(), AVG(), MIN(), MAX()) 8. Joins (INNER JOIN, LEFT JOIN, RIGHT JOIN, FULL JOIN) 📌 Code & notes :- https://lnkd.in/dmFHqCrK #100DaysOfPython #MachineLearning #AIML #Python #LearningInPublic