Python Functions and SQL Joins for Data Analysis

🚀 Day 6 of my 30-Day Data Analytics Challenge Today I started working with Python for Data Analytics — focusing on logic before libraries. What I practiced today: Understanding variables and data types Using if–else conditions to apply business rules Working with lists and dictionaries to represent data Writing simple functions to classify data (e.g., fast vs delayed delivery) Most importantly, learning to read Python code and predict output 🐍 Key realization: Python in data analytics is not about complex syntax — it’s about applying clear logic to data. This session helped me see how Python supports data cleaning, classification, and analysis, which will be essential as I move toward Pandas and real datasets. Step by step, building strong foundations 💪📊 #DataAnalytics #PythonForDataAnalytics #LearningInPublic #30DayChallenge #AnalyticsJourney #Day6

Day 8 / 90 – Data Science Learning Update 🚀 Today I focused on improving my understanding of Python data structures and practicing SQL aggregate functions for data summarization. What I worked on: • Python – working with lists and dictionaries for storing and accessing data • Practicing list operations and dictionary methods • SQL – using COUNT(), SUM(), AVG(), MIN(), and MAX() for analyzing data Key takeaway: Strong knowledge of data structures in Python helps in efficient data handling, while aggregate functions in SQL are essential for extracting meaningful insights from datasets. Consistent learning, one step at a time. #DataScience #Python #SQL #LearningJourney #Day8

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

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

🚀 Starting My Pandas Learning Journey I’m excited to share that I have started learning the Pandas library in Python, which is one of the most powerful tools for data analysis. Pandas helps in: • Data cleaning • Data manipulation • Data analysis • Working with large datasets I am currently learning Pandas from the WsCube Tech YouTube tutorial and practicing the concepts step by step. You can check the tutorial here: https://lnkd.in/gZ3yhEvE I believe sharing my learning journey will help me stay consistent and connect with others in the data community. #Python #Pandas #DataAnalytics #LearningJourney #DataScience

🚨𝗜 𝗧𝗿𝗶𝗲𝗱 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀 𝘄𝗶𝘁𝗵 𝗣𝘆𝘁𝗵𝗼𝗻 — 𝗛𝗲𝗿𝗲’𝘀 𝗪𝗵𝗮𝘁 𝗔𝗰𝘁𝘂𝗮𝗹𝗹𝘆 𝗛𝗲𝗹𝗽𝗲𝗱 Most beginners jump into libraries. I first learned how data actually thinks. That changed everything. Here’s the beginner-friendly roadmap that made Python analytics finally click 👇 🐍📊 Python for Data Analytics — Hands-On Guide ✨ What this guide walks you through: 1️⃣ What data analytics really means (not just tools) 2️⃣ Python fundamentals that matter for analysts 3️⃣ Pandas & NumPy for real data manipulation 4️⃣ Matplotlib for turning numbers into insights 💡 Why it works: → Simple, step-by-step flow → Practical examples (not theory dumps) → Built for beginners who want confidence, not confusion 🔁 Repost to help a beginner in your network #Python #DataAnalytics #Pandas #NumPy #Matplotlib #LearningInPublic #DataScience #TechCareers

🚨𝗜 𝗧𝗿𝗶𝗲𝗱 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀 𝘄𝗶𝘁𝗵 𝗣𝘆𝘁𝗵𝗼𝗻 — 𝗛𝗲𝗿𝗲’𝘀 𝗪𝗵𝗮𝘁 𝗔𝗰𝘁𝘂𝗮𝗹𝗹𝘆 𝗛𝗲𝗹𝗽𝗲𝗱 Most beginners jump into libraries. I first learned how data actually thinks. That changed everything. Here’s the beginner-friendly roadmap that made Python analytics finally click 👇 🐍📊 Python for Data Analytics — Hands-On Guide ✨ What this guide walks you through: 1️⃣ What data analytics really means (not just tools) 2️⃣ Python fundamentals that matter for analysts 3️⃣ Pandas & NumPy for real data manipulation 4️⃣ Matplotlib for turning numbers into insights 💡 Why it works: → Simple, step-by-step flow → Practical examples (not theory dumps) → Built for beginners who want confidence, not confusion 🔁 Repost to help a beginner in your network #Python #DataAnalytics #Pandas #NumPy #Matplotlib #LearningInPublic #DataScience #TechCareers

Day 9 / 90 – Data Science Learning Update 🚀 Today I focused on strengthening my understanding of Python OOPS concepts and practicing SQL subqueries for deeper data analysis. What I worked on: • Python – understanding inheritance and method overriding • Exploring how OOPS improves code reusability and structure • SQL – writing subqueries to filter and retrieve specific data Key takeaway: Object-oriented concepts help in building scalable and maintainable Python programs, while subqueries allow more flexible and powerful data retrieval in SQL. Consistent learning, one step at a time. #DataScience #Python #SQL #LearningJourney #Day9

Day 04 As part of my 30-Day Data Science learning journey, today I focused on strengthening core Python concepts. 📌 Key Areas Covered: • Writing reusable and modular functions • Problem-solving using Lists (Arrays) • Implementing Dictionary-based logic • Solving intermediate-level coding challenges Building strong fundamentals is essential before moving into advanced analytics and machine learning. Consistency and structured practice remain my priority. https://lnkd.in/gMCwkFT2 #Python #DataScience #dataanalytics #LearningJourney