Prajwal Marthur’s Post

Most people jump straight into building models. I’m learning to fix the data first. Today’s focus: Data Cleaning in Python 🧹 Here’s the reality — even the best algorithms fail with messy data. So I worked on: ✔️ Handling missing numeric values using mean ✔️ Filling categorical gaps with mode ✔️ Verifying data integrity before moving forward Simple steps… but they make a massive difference. What stood out to me: 👉 Data cleaning isn’t “boring prep work” — it’s where real analysis begins 👉 Small improvements in data quality can outperform complex models 👉 Clean data = reliable insights I’m starting to see that data science is less about fancy models and more about asking: “Can I trust this data?” 📊 This is part of my hands-on journey into data analysis and machine learning 📈 Focus: Building strong fundamentals, one step at a time If you’re in data or learning it — what’s one cleaning step you never skip? #DataScience #Python #DataCleaning #MachineLearning #Analytics #LearningInPublic #DataAnalytics #TechJourney #Unlox #GirishKumar

📊 What is Data Science? A Beginner-Friendly View 🚀 Data Science is the art of turning raw data into meaningful insights that drive decisions. Here’s how it all connects: 📥 Data – The foundation of everything 🗄️ Database – Where data is stored and managed 📊 Analytics – Extracting insights from data 💻 Programming (Python, SQL) – Tools to work with data 🤖 Machine Learning – Building intelligent models 📈 Visualization – Communicating insights clearly 💡 Key Insight: Data Science isn’t just about coding it’s about solving real-world problems using data. 🔥 Whether you're starting your journey or upskilling, mastering these components is essential in today’s data-driven world. #DataScience #DataAnalytics #MachineLearning #Python #DataVisualization #AI #BigData #Learning #TechCareers #DataDriven #Analytics #CareerGrowth

𝐏𝐂𝐀 (𝐏𝐫𝐢𝐧𝐜𝐢𝐩𝐚𝐥 𝐂𝐨𝐦𝐩𝐨𝐧𝐞𝐧𝐭 𝐀𝐧𝐚𝐥𝐲𝐬𝐢𝐬)- 𝐖𝐡𝐞𝐧 𝐭𝐨𝐨 𝐦𝐚𝐧𝐲 𝐟𝐞𝐚𝐭𝐮𝐫𝐞𝐬 𝐬𝐭𝐚𝐫𝐭 𝐛𝐞𝐜𝐨𝐦𝐢𝐧𝐠 𝐚 𝐩𝐫𝐨𝐛𝐥𝐞𝐦… While working on datasets with a large number of features, I realized something important: 𝐌𝐨𝐫𝐞 𝐟𝐞𝐚𝐭𝐮𝐫𝐞𝐬 ≠ 𝐛𝐞𝐭𝐭𝐞𝐫 𝐦𝐨𝐝𝐞𝐥 In fact, too many features can lead to a problem called: - Curse of Dimensionality - Models become slow - Computation increases - Noise increases - Visualization becomes difficult 𝐒𝐨𝐥𝐮𝐭𝐢𝐨𝐧 → 𝐃𝐢𝐦𝐞𝐧𝐬𝐢𝐨𝐧𝐚𝐥𝐢𝐭𝐲 𝐑𝐞𝐝𝐮𝐜𝐭𝐢𝐨𝐧 𝐏𝐂𝐀 is an 𝐮𝐧𝐬𝐮𝐩𝐞𝐫𝐯𝐢𝐬𝐞𝐝 𝐥𝐞𝐚𝐫𝐧𝐢𝐧𝐠  technique used when we only have input features (no target/output). It is a 𝐟𝐞𝐚𝐭𝐮𝐫𝐞 𝐞𝐱𝐭𝐫𝐚𝐜𝐭𝐢𝐨𝐧 𝐭𝐞𝐜𝐡𝐧𝐢𝐪𝐮𝐞 𝐭𝐡𝐚𝐭 𝐭𝐫𝐚𝐧𝐬𝐟𝐨𝐫𝐦𝐬 𝐡𝐢𝐠𝐡-𝐝𝐢𝐦𝐞𝐧𝐬𝐢𝐨𝐧𝐚𝐥 𝐝𝐚𝐭𝐚 𝐢𝐧𝐭𝐨 𝐥𝐨𝐰𝐞𝐫 𝐝𝐢𝐦𝐞𝐧𝐬𝐢𝐨𝐧𝐬  while preserving most of the important information. " In simple words: It keeps the essence of data but reduces complexity." 𝐔𝐬𝐢𝐧𝐠 𝐏𝐂𝐀 𝐡𝐞𝐥𝐩𝐬:- Reduce number of features - Improve model performance - Reduce computation cost - Speed up training - Make data easier to visualize 𝐇𝐨𝐰 𝐏𝐂𝐀 𝐖𝐨𝐫𝐤𝐬 (𝐒𝐭𝐞𝐩𝐬 𝐈 𝐏𝐫𝐚𝐜𝐭𝐢𝐜𝐞𝐝) 𝐒𝐭𝐞𝐩 1️⃣: 𝐒𝐭𝐚𝐧𝐝𝐚𝐫𝐝𝐢𝐳𝐞 𝐭𝐡𝐞 𝐝𝐚𝐭𝐚 Because PCA is scale-sensitive 𝐒𝐭𝐞𝐩 2️⃣: 𝐂𝐨𝐦𝐩𝐮𝐭𝐞 𝐂𝐨𝐯𝐚𝐫𝐢𝐚𝐧𝐜𝐞 𝐌𝐚𝐭𝐫𝐢𝐱 To understand relationships between features 𝐒𝐭𝐞𝐩 3️⃣: 𝐅𝐢𝐧𝐝 𝐄𝐢𝐠𝐞𝐧𝐯𝐚𝐥𝐮𝐞𝐬 & 𝐄𝐢𝐠𝐞𝐧𝐯𝐞𝐜𝐭𝐨𝐫𝐬 import numpy as np eigen_values, eigen_vectors=np.linalg.eig(cov_matrix) 𝐒𝐭𝐞𝐩 4️⃣: 𝐒𝐞𝐥𝐞𝐜𝐭 𝐏𝐫𝐢𝐧𝐜𝐢𝐩𝐚𝐥 𝐂𝐨𝐦𝐩𝐨𝐧𝐞𝐧𝐭𝐬 Choose top components with highest variance 𝘗𝘊𝘈 𝘪𝘴 𝘯𝘰𝘵 𝘫𝘶𝘴𝘵 𝘳𝘦𝘥𝘶𝘤𝘪𝘯𝘨 𝘤𝘰𝘭𝘶𝘮𝘯𝘴… 𝘐𝘵’𝘴 𝘢𝘣𝘰𝘶𝘵 𝘬𝘦𝘦𝘱𝘪𝘯𝘨 𝘵𝘩𝘦 𝘮𝘰𝘴𝘵 𝘪𝘮𝘱𝘰𝘳𝘵𝘢𝘯𝘵 𝘪𝘯𝘧𝘰𝘳𝘮𝘢𝘵𝘪𝘰𝘯 𝘸𝘩𝘪𝘭𝘦 𝘳𝘦𝘮𝘰𝘷𝘪𝘯𝘨 𝘳𝘦𝘥𝘶𝘯𝘥𝘢𝘯𝘤𝘺 #Datascience #Dataanalyst #Machinelearning #curseofdimensionality #featureextraction #python #numpy

Data analytics is often seen as learning a few tools like Excel, SQL, or Python. But in reality, it’s much broader than that. This roadmap of 78 topics highlights how data analytics is built step by step: • Understanding data and business problems • Collecting and preparing data • Cleaning and transforming datasets • Exploring patterns and trends • Applying statistics for insight • Communicating results through visualization • Using tools and programming effectively • Advancing into predictive and machine learning techniques Each stage plays an important role, and skipping one can make the next more challenging. For anyone learning or transitioning into data analytics, having a structured path like this can make the journey more clear and manageable. Consistency matters more than speed. Which area are you currently focusing on? #DataAnalytics #DataScience #LearningJourney #BusinessIntelligence #Python #SQL

Start your journey in Data Science with practical, industry-focused training. Learn how to: • Collect and clean data • Perform exploratory data analysis (EDA) • Build machine learning models • Generate insights for real business decisions Gain hands-on experience in Python, SQL, Data Analytics, and Machine Learning with expert guidance. If you're serious about building a career in data, this is where you start. 📞 9884678282 | 9884678383 🌐 www.itechpanda.com #DataScience #DataAnalytics #MachineLearning #Python #CareerGrowth

𝐌𝐮𝐥𝐭𝐢𝐭𝐡𝐫𝐞𝐚𝐝𝐢𝐧𝐠 𝐢𝐧 𝐏𝐲𝐭𝐡𝐨𝐧 I recently learned Multithreading in Python, and it helped me understand one of the biggest performance problems in Data Science: Waiting. When working with data, a lot of time is spent on: • Loading datasets • Reading files • Calling APIs • Querying databases • Preprocessing data Most of these are 𝗜/𝗢-𝗯𝗼𝘂𝗻𝗱 𝘁𝗮𝘀𝗸𝘀, meaning the program spends more time waiting than actually computing. That’s where Multithreading becomes powerful. Instead of running tasks one by one, multithreading allows multiple tasks to run concurrently, reducing overall execution time. For example, I explored how two tasks running sequentially took 20 seconds, but with multithreading, the same tasks completed in 10 seconds by running simultaneously. This has huge applications in Data Science: → Faster data loading → Concurrent API calls → Parallel data preprocessing → Efficient pipeline execution → Improved performance for I/O-heavy workflows Learning this made me realize that Data Science is not just about models, it's also about performance and efficiency. To reinforce my learning, I created my own structured notes, and I’m sharing them as a PDF in this post. Step by step, building stronger foundations in Data Science & AI #Python #DataScience #Multithreading #AI #MachineLearning #Performance #LearningInPublic #TechJourney

📊 Pandas in Python – Making Data Simple & Powerfu Working with data doesn’t have to be complicated. With Pandas, we can easily clean, analyze, and manipulate data in just a few lines of code. From handling missing values to performing quick analysis, Pandas is an essential tool for anyone stepping into data science and machine learning. 🔹 Key Takeaways: • Two powerful structures: Series & DataFrame • Easy data handling (CSV, Excel, JSON) • Fast filtering, sorting, and analysis • Perfect for real-world datasets 💡 Whether you're a student or an aspiring data scientist, mastering Pandas can significantly boost your productivity and problem-solving skills. 🚀 Learning step by step and sharing the journey! #Python #Pandas #DataScience #MachineLearning #AI #Programming #Learning #Tech #StudentLife

AI for Python Analytics in Power BI Power BI becomes even more powerful when you combine it with Python + AI. Most dashboards stop at visualization. But real value comes from deeper analysis. Many advanced analytics tasks need more than charts. With AI, analysts can now generate Python scripts inside Power BI for: • Predictive models • Forecasting analysis • Advanced statistical insights • Machine learning visualizations This removes a major barrier. You don’t need to be an expert in Python to start using it. AI can write the code, explain it, and help you apply it correctly. That means faster analysis, better insights, and more informed decisions. Power BI is no longer just a reporting tool. It becomes a full analytics engine when combined with Python and AI. Have you ever used Python inside Power BI for analytics? #powerbi #pythonanalytics #dataanalytics #businessintelligence #machinelearning #datavisualization #aiforanalytics #datascience #analyticscommunity

SQL and SQLite with Python Data is useless if you can't store it properly. This week, I learned SQL and SQLite with Python, and it changed how I think about handling data in real-world applications. Before this, I was mostly working with data in memory. Now, I can store, manage, and retrieve data efficiently — just like real Data Science and production systems. Here’s what I explored: • Creating databases using SQLite • Storing structured data using SQL tables • Writing queries to retrieve specific insights • Updating and deleting records efficiently • Connecting Python with SQLite for automation • Managing datasets in a scalable and organized way What I found most interesting is how Python + SQL creates a powerful combination: Python → Data processing & analysis SQL → Data storage & retrieval Together, they form the backbone of many Data Science and AI systems. To reinforce my learning, I created my own structured notes and I’m sharing them as a PDF in this post. Hopefully, it helps others who are building their Data Science foundation. Step by step, building towards Data Science & AI #DataScience #SQL #SQLite #Python #Database #AI #MachineLearning #LearningInPublic #TechJourney

🚀 Exploratory Data Analysis (EDA) Using Python I’m excited to share my recent project where I performed Exploratory Data Analysis (EDA) on a publicly available dataset to uncover meaningful insights and patterns. 🔍 What I Did: Collected and explored a real-world dataset (Iris/Titanic/Kaggle) Cleaned the data by handling missing values, duplicates, and inconsistent formats Performed statistical analysis to understand distributions and key metrics Built visualizations using Matplotlib and Seaborn to identify trends and relationships 📊 Key Visualizations: Distribution plots to understand data spread Correlation heatmaps to identify relationships between variables Box plots to detect outliers Scatter plots for pattern analysis 💡 Key Learnings: Importance of data preprocessing before analysis How visualization helps in uncovering hidden insights Strengthened my analytical thinking and storytelling with data 🛠 Tools & Technologies: Python | Pandas | NumPy | Matplotlib | Seaborn | Jupyter Notebook 🎯 This project enhanced my ability to transform raw data into actionable insights and strengthened my foundation in Data Analysis & Data Science. I would appreciate your feedback and suggestions! #DataScience #Python #EDA #DataAnalysis #MachineLearning #LearningJourney