Data Science Journey: Data Collection Techniques

🚀 Day 71 – Operations in Pandas Today’s focus was on mastering Pandas Operations — an essential step toward handling real-world datasets effectively! 📊 🔹 Data Processing with Pandas Learned how to clean and prepare raw data for analysis by handling missing values, filtering data, and structuring datasets properly. 🔹 Data Normalization in Pandas Explored techniques to scale data into a common range, making it easier to compare and analyze different features. 🔹 Data Manipulation in Pandas Worked with powerful operations like: Filtering and sorting data Grouping using groupby() Aggregating data with functions like sum(), mean(), etc. 💡 Key Takeaway: Efficient data operations = Better insights. The ability to process, normalize, and manipulate data is what turns raw data into meaningful information. 📈 Step by step, building strong foundations in Data Analytics! #Day71 #DataScience #Pandas #Python #DataAnalytics #DataProcessing

I started practicing pandas question from Leetcode by following the explanation from a Youtube video which started with some basic questions related to "SELECT" and "BASIC JOINS". YouTube link - https://lnkd.in/eHVbH2Jh https://lnkd.in/dkKvmEim I vibe coded a portfolio project that shows the skills of a Data Engineer at a Financial services / investment research firm and turned it into a streamline dashboard. It is RAG pipeline that makes 30 years of financial research queryable in plain English — combining vector search, BM25, metadata filtering, and LLM-generated answers to answer questions like "show me emerging market buy upgrades from senior analysts in the last 6 months. Check it out - https://lnkd.in/eMXWgYqB #DataScience #Python #Pandas #Day3 #66DaysOfData

#SQL vs #Pandas is not a battle. It’s the same brain… in two different worlds. Think like this: • SQL → Talking to a database • Pandas → Talking to data in your notebook Same logic, different language: • SELECT → Choosing columns • WHERE → Filtering rows • GROUP BY → Summarizing data • JOIN → Combining datasets Real analogy: SQL → Ordering food from a restaurant Pandas → Cooking it yourself at home Both get the job done. But the environment changes everything. Lesson: Don’t learn tools separately. Learn the pattern once → apply everywhere. #PySpark #Python #DataEngineering #BigData #ApacheSpark #CodingTips #TechLearning #DataScience #DevCommunity

📊 What I Learned Today — Percentiles & Quantiles (Pandas) Today I fixed a confusion I had for a long time: 👉 Percentiles are NOT based on total sum — they’re based on position in sorted data. Key takeaways: 🔹 Quantile → value below which a % of data lies 🔹 Position formula: (n − 1) × q 🔹 Decimal position → interpolation 🔹 Result may not exist in dataset (and that’s okay) 💡 Example: Data → [10, 20, 30, 40] 75th percentile → position = (4−1)×0.75 = 2.25 So pandas doesn’t pick a value directly — it interpolates between 30 and 40 → 32.5 💡 Big insight: Even if the 75th percentile isn’t directly present, pandas computes it using values in between — not by summing anything. This cleared a major confusion: ❌ Percentage = sum-based ✅ Percentile = position-based Small concept, but a big clarity boost. Consistency > Perfection 🚀 #DataAnalytics #Pandas #Python #LearningJourney #InterviewPrep

📅 Day 13 of My Data Analytics Journey 🚀 Today I focused on understanding one of the most important concepts in data analysis — Pandas DataFrames. 🔍 What I learned: • Introduction to Pandas DataFrames • Creating DataFrames from data • Understanding rows and columns • Viewing and exploring data 🧠 Concepts covered: • DataFrame structure (rows & columns) • Column selection and basic operations • Viewing data using ".head()" and ".tail()" • Understanding dataset shape and size 💡 Key Learning: DataFrames provide a structured and efficient way to store and analyze data, making it easier to work with real-world datasets. 📈 Building confidence in handling structured data step by step. 🚀 Next step: Applying filtering and analysis on real datasets. #DataAnalytics #Python #Pandas #LearningInPublic #Consistency #CareerGrowth

One of the most important steps in Data Analysis is Exploratory Data Analysis (EDA). Before building dashboards or models, I always spend time understanding the dataset. Here’s what I usually focus on: 🔍 Checking missing values 📊 Understanding distributions 🔗 Finding relationships between variables Using Python libraries like Pandas and Matplotlib makes this process much easier and more insightful. Sometimes, a simple visualization can reveal patterns that are not obvious in raw data. 💡 In my experience, strong EDA leads to better decisions and more accurate insights. 👉 What’s your favorite library for data analysis and why? #Python #EDA #DataScience #Analytics #Learning

🚀 Top 5 Pandas Codes Every Data Scientist Should Know From loading datasets to performing powerful aggregations, these essential Pandas commands form the backbone of real-world data analysis. Whether you're a beginner or sharpening your skills, mastering these basics can significantly boost your productivity and confidence in handling data. 📌 Key Highlights: • Efficient data loading • Quick data insights & summary • Smart filtering techniques • Handling missing values • Grouping & aggregating like a pro 💡 Small commands, big impact — this is where every Data Science journey begins. If you're learning Data Science, don’t just read—practice daily. #DataScience #Python #Pandas #MachineLearning #DataAnalytics #Coding #LearnToCode #CareerGrowth

📊 Turn data into decisions that matter Data is everywhere but the real power lies in understanding it. This guide walks you through the core of Data Science & Analytics: 🐼 Pandas 🔢 NumPy 📈 Visualization 🗄️ SQL 🧹 Data Cleaning 🤖 Machine Learning 💡 Learn how to analyze, visualize, and extract insights that drive real impact. 🚀 Start your data journey today, one step at a time. 💬 Comment “DATA” for a beginner roadmap! 🔗 Register now at https://vilabsacademy.uk 📞 Contact us: ‪+44 7853 753852‬ | ‪‪info@vilabsacademy.uk #DataScience #DataAnalytics #LearnData #Python #MachineLearning #CareerGrowth #TechSkills

Most beginners learn how to clean data… but very few learn how to extract insights from it. That’s where real data analysis begins. In my latest video, I dive into one of the most important concepts in Pandas — GroupBy and aggregation — and show how raw, unstructured data can be transformed into meaningful insights. With just a few lines of code, you can: • Identify top-performing products • Analyze trends across categories • Summarize large datasets efficiently This is a fundamental skill for anyone entering data science, analytics, or machine learning. What I focused on in this video: Practical examples instead of theory Real-world style dataset Common mistakes beginners make Clean and structured explanations If you're building your foundation in Python and data analysis, this will help you move from data cleaning → actual analysis. 🎥 Watch the full video here: https://lnkd.in/gzRnwCvY 🔗 What’s next? I’ll be covering joining and merging DataFrames next — a crucial step in working with real-world datasets. 🤝 Let’s connect If you're also learning or working in data science, I’d love to connect and exchange ideas. #DataScience #Python #Pandas #DataAnalytics #MachineLearning

🚀 Pandas Series – Day 3 | Understanding loc vs iloc In today’s session of my Pandas series, I focused on one of the most important and commonly asked concepts in data analysis — the difference between loc and iloc. 📌 Here’s a quick breakdown: 🔹 loc (Label-based indexing) Used when you want to access data using row/column labels. Example: df.loc[0:3, "Name"] 🔹iloc (Position-based indexing) Used when you want to access data using index positions (integers) Example: df.iloc[0:3, 0:2] 💡 key Insight: * loc works with labels * iloc works with positions To make things clear, I explained this with a practical example so beginners can easily understand how and when to use each. 🎯 Mastering these fundamentals is essential for anyone aiming to become a Data Analyst,as data selection and filtering are everyday tasks in real-world projects. #Pandas #Python #DataAnalytics #DataScience #LearningInPublic #LinkedInLearning #CodingJourney #DataAnalyst #PythonProgramming Bhavesh Arora Shakra Shamim Ayushi Jain Gitanjali Pekamwar Abhisheak Gangwar