SQL vs Python: Consecutive Steps in Data Analysis

For a long time, I believed Excel was all I needed. Become 2026 Data analysis Roadmap Free resources https://lnkd.in/dRJpwWvC If the analysis worked and the numbers looked right, I assumed my skills were complete. Then real data entered the picture. Large files, repeated tasks, and manual steps started slowing everything down. Many beginners face the same confusion: Should we master Excel completely, or move to Python early? Is Python replacing Excel? This image answers that clearly. Excel is where analytical thinking begins. It teaches logic, structure, and how to work with data step by step. Python does not discard those skills. It scales them. By showing the same tasks side by side, this comparison helps beginners see Python as a natural extension of Excel, not a sudden jump. When learning follows a clear progression, fear reduces and confidence grows. In 2026, analysts who understand this transition will work faster, cleaner, and with far less manual effort. Growth becomes simpler when the path is visible. — Shivam Saxena https://lnkd.in/dRJpwWvC #Excel #Python #DataAnalytics #Pandas #AnalyticsForBeginners #DataAnalystSkills #BusinessAnalytics #LearnData #AnalyticsCareer #FutureOfAnalytics

One common struggle when learning SQL is that SQL is not a procedural language. In Python, code runs top to bottom — the order you write is the order it executes. SQL works differently. It’s declarative: you describe what you want, not how to compute it. That means the syntax order and the logical execution order do not match. Beginners often read it line by line, just like Python. But databases actually think in this order: FROM + JOIN → build the base dataset ↓ WHERE → filter rows ↓ GROUP BY → form groups ↓ AGGREGATES → SUM / COUNT / AVG (written in SELECT) ↓ HAVING → filter groups ↓ SELECT → choose final columns ↓ ORDER BY → sort results 💡 Key insight: After GROUP BY, raw rows disappear. Only grouping columns and aggregated values can survive. This mismatch between how SQL is written and how it is executed is what makes SQL feel roundabout at first — but once this mental model clicks, SQL becomes much easier and more powerful. Always deconstruct a SQL query by mapping each clause to the logical execution step, not by reading it top to bottom. #SQL #QueryLanguages #DeclarativeProgramming #ProceduralProgramming #DatabaseSystems #DataEngineering #Analytics #FinTech #FinancialData #DataArchitecture

This Week in Python (Pandas): A Key Realisation Data analysis isn’t about building dashboards first. It starts with mastering the foundation — clean, structured data. This week, I focused on three critical areas: 🔎 Data Cleaning Removed duplicates, standardized column names, corrected data types, and ensured structural consistency. Because inaccurate data leads to inaccurate decisions. 🧩 Handling Missing Values Instead of automatically filling gaps with 0, I paused to understand the context. Was the data unavailable, or was the value genuinely zero? Thoughtful handling prevents distorted insights. 📊 Aggregation & Insight Generation Using groupby(), sum(), and mean(), I transformed raw transactional data into meaningful performance metrics. That’s when rows of numbers started telling a story. The biggest takeaway? Strong analysis isn’t defined by complex code. It’s defined by clear thinking and the right questions. Every dataset is an opportunity to uncover insight — if you approach it with structure and intention. Continuing to learn. Continuing to build. #Python #Pandas #DataAnalytics #DataAnalyst #SQL #AnalyticsJourney #OpenToWork

⛓️💥 #ADVANCE PYTHON #PANDAS LIBRARY 🔓 🚀 Mastering Pandas – The Backbone of Data Analysis in Python! 🐼 As part of my continuous learning journey, I explored the powerful Pandas library in Python — one of the most essential tools for Data Analysis and Data Science. 📌 What is Pandas? Pandas is an open-source Python library used for data manipulation, cleaning, and analysis. It provides powerful data structures like: 🔹 Series – 1D labeled array 🔹 DataFrame – 2D labeled data structure (like Excel table) 💡 Key Concepts I Practiced: ✅ Creating DataFrames ✅ Reading CSV files (read_csv()) ✅ Data cleaning (dropna(), fillna()) ✅ Filtering & indexing (loc[], iloc[]) ✅ GroupBy operations ✅ Sorting & aggregation ✅ Handling missing values ✅ Applying functions using apply() 🎯 Why Pandas is Important? ✔ Efficient data handling ✔ Essential for Data Science & ML ✔ Works smoothly with NumPy & Matplotlib ✔ Used widely in industry projects 🔓 Learning Pandas improved my understanding of real-world data processing and strengthened my problem-solving skills. #Python #Pandas #DataScience #DataAnalytics #MachineLearning #CodingJourney Ajay Miryala 10000 Coders #pythonpractice

📊 Pandas Basic Revision Codes — Python Data Analysis Cheat-Sheet I’ve created a structured set of basic Pandas revision codes to quickly review the core concepts of data analysis in Python. This resource is designed for students, beginners in Data Science, and anyone who wants a fast refresher before exams, projects, or interviews. 📚 Topics covered in this pack: 🔹 L1 — What is Pandas 🔹 L2 — Pandas Basics: Create DataFrame 🔹 L3 — Pandas Series and Columns 🔹 L4 — Pandas DataFrame Info 🔹 L5 — Selecting Rows and Columns 🔹 L6 — Add & Drop Columns 🔹 L7 — Reading CSV (Most Important) 🔹 L8 — Handling Missing Values 🔹 L9 — Basic Math Operations All examples are written in simple Python code for quick understanding and practical use. 📂 Download the revision pack here: 🔗 https://lnkd.in/gB8GKTXd If this helps you, feel free to share it with others who are learning Python and Data Science 🚀 🔥 Hashtags #Python #Pandas #DataScience #DataAnalysis #MachineLearning #Programming #Coding #PythonProgramming #ComputerScience #StudentDeveloper #LearningInPublic #AI #Tech #StudyResources #BeginnerFriendly #OpenSource #Developers #STEM

Learning Pandas: Reading & Importing Data in Python 🐼📊 Currently exploring Pandas DataFrames as part of my Python for Data Science journey. This session focused on how real-world data is read, structured, and prepared for analysis. 🔹 Key takeaways from this module: Understanding file formats used in data science (.csv, .xlsx, .txt, JSON, HTML, etc.) Basics of Comma Separated Values (CSV) files Importing data into Spyder using Pandas Using essential libraries: Python import os import pandas as pd Changing the working directory with os.chdir() Reading datasets using: Copy code Python pd.read_csv() Handling missing values (NaN) in datasets Removing unwanted index/ID columns while importing data Introduction to DataFrames – 2D, size-mutable, powerful data structures 📌 Pandas makes data handling faster, cleaner, and more efficient—an essential tool for anyone stepping into Data Science, Machine Learning, or Analytics. Excited to apply these concepts to real datasets and move deeper into data analysis! 💡📈 #Python #DataScience #Pandas #LearningJourney #SpyderIDE #CSV #MachineLearning #Analytics #Programming #StudentLife

🚀 𝐌𝐚𝐬𝐭𝐞𝐫𝐢𝐧𝐠 𝐕𝐚𝐫𝐢𝐚𝐛𝐥𝐞𝐬 𝐢𝐧 𝐏𝐲𝐭𝐡𝐨𝐧 – 𝐓𝐡𝐞 𝐅𝐨𝐮𝐧𝐝𝐚𝐭𝐢𝐨𝐧 𝐨𝐟 𝐄𝐯𝐞𝐫𝐲 𝐃𝐚𝐭𝐚 𝐄𝐧𝐠𝐢𝐧𝐞𝐞𝐫! Variables are the building blocks of Python programming. Whether you're working on ETL pipelines, PySpark transformations, or analytics dashboards, understanding variables is essential. In this blog by us, you’ll learn: ✅ What is a Variable in Python ✅ Dynamic Typing Explained Simply ✅ Multiple Assignments ✅ Variable Re-initialization ✅ Naming Rules (Very Important for Beginners) ✅ Constants in Python ✅ Real Code Examples If you are starting your journey in Python / Data Engineering, this blog will give you a strong foundation. 🔗 Read here: https://lnkd.in/gX568i2V Keep learning. Keep building. Keep growing. 💡 #Python #CorePython #Programming #DataEngineering #ETL #Learning #TechCareer #KSRDataVizon #CodingJourney #Beginners Santosh J. | Mahesh | KONDA REDDY | Magudeswaran | Satya | Ajay | Basha | Gopi E | Sekhar | Gopi Krishna | Prasanna | Sourav | Shaik Arshad | Kamalaker | Indrajeet | Arvind | Harikrishna | Maureen | Ravindra Reddy | Manikanta Reddy | Niharika | RAMA | Sreethar M B | 𝐀𝐛𝐝𝐮𝐥 Khuddus | Mallikarjuna R

🚀 Is Python really required for Data Analysis? Short answer: Not mandatory — but highly valuable. You can start with Excel, SQL, and Power BI. But when datasets grow larger and problems become complex, Python makes a big difference. Basic understanding of: ✅ Variables & functions ✅ Lists & dictionaries ✅ NumPy for numerical operations ✅ Pandas for data cleaning & manipulation can make your analysis faster, cleaner, and more scalable. I personally realized that learning Python strengthened my confidence as a Data Analyst. Grateful to Codebasics, Dhaval Patel, and Hemanand Vadivel for simplifying the journey 🙏 Still learning. Still growing. #DataAnalytics #Python #LearningJourney #Codebasics

🐍 Learning Pandas has completely changed how I work with data in Python. Instead of feeling overwhelmed by large datasets, a few simple functions now help me quickly understand, clean, and analyze information. 💡 Here are some Pandas functions I use the most in my daily practice 👇 👀 head() & tail() To quickly preview the structure of a dataset and understand what I’m working with. 📊 describe() To get instant insights into key statistics like averages, ranges, and distributions. 🧹 dropna() & fillna() To handle missing values and prepare clean data for analysis. 🧠 Simple workflow I follow: Preview → Understand → Clean → Analyze ⭐ TAKEAWAY You don’t need complex code to start doing meaningful analysis. Mastering a few core Pandas functions can already turn raw data into useful insights. These basics have helped me feel more confident working with Python as I transition into Data Analytics. 🌱 If you use Pandas: 👉 Which function do you find yourself using the most in real projects? Let’s learn from each other! 💡 #Pandas #PythonForDataAnalytics #SkillSharing #LearningPython #DataAnalyticsJourney #CareerTransition #LearningInPublic #AspiringDataAnalyst

🐍 Python in Data Analytics Python is widely used in Data Analytics to clean, analyze, and automate data tasks efficiently. It enables analysts to work with large datasets and perform complex calculations with ease. Libraries such as Pandas and NumPy simplify data manipulation, while tools like Matplotlib and Seaborn help visualize insights clearly. Python also allows analysts to automate repetitive tasks, saving time and improving productivity. Its ability to integrate seamlessly with SQL, Excel, and BI tools makes Python a powerful addition to any data analyst’s skill set. 🚀 That’s why Python is a valuable skill for growing Data Analysts. 👉 Start with basics first — learn Python when you’re ready to level up your analytics skills. #Python #DataAnalytics #PythonForDataAnalysis #AnalyticsSkills #CareerGrowth #NattonTechnology #NattonSkillX #NattonAI #NattonDigital