Jaswanth Thathireddy’s Post

Learning never stops. Over the last weeks we’ve been diving deep into Python, SQL, and NoSQL – building small projects, breaking things on purpose, and then fixing them again. It’s a great way to understand not only how to write queries and scripts, but also how data actually flows through real applications. Step by step, it’s starting to connect: Python for logic and automation, SQL for structured data, and NoSQL for flexible, modern workloads. Looking forward to turning this practice into real‑world projects soon. https://lnkd.in/dcPkK-hX #sql #nosql #python

Knowing Python isn't enough... You need to know how to work with real data. That's where Pandas comes in. Day 5 of my 30-day Data Science challenge Here's what I simplified into this cheat sheet 👇 Data Loading → read_csv, read_excel, read_json Data Inspection → head(), info(), describe() Data Cleaning → dropna(), fillna(), rename() Data Selection → loc, iloc, df['col'] Data Manipulation → groupby(), merge(), sort_values() Filtering → df[df['col'] > value], query() This is something I keep coming back to every single day. Save this — you'll need it Which Pandas function do you use the most? 👇 #Pandas #Python #DataScience #LearningInPublic #DataScienceFresher

🔢 Why NumPy Matters in Data Science (More Than I Thought) Hi everyone! 👋 While learning Python for data work, I came across NumPy — and initially, it just looked like another library. But after spending some time with it, I realized why it’s so widely used. At its core, NumPy is about working efficiently with numbers and arrays. A few things that stood out to me: ✔️ Faster computations compared to regular Python lists ✔️ Ability to perform operations on entire datasets at once (no loops needed) ✔️ Foundation for libraries like Pandas, Scikit-learn For example, instead of looping through values one by one, NumPy lets you do operations in a single line — which is both cleaner and faster. This made me think about real-world scenarios: When dealing with large datasets, performance really matters. Even small optimizations can save a lot of time. Coming from SQL and ETL, this feels similar to optimizing queries — but now at a programming level. Still exploring more, but it’s clear that understanding NumPy well can make a big difference in data processing and model performance. Have you used NumPy in your work? Or do you rely more on Pandas/SQL? #DataScience #Python #NumPy #MachineLearning #LearningInPublic

🔍 Most beginners fail in data science before even starting… 🕵️ Imagine entering a room full of clues — names, numbers, categories — but you don’t know what they represent. That’s exactly how raw data looks. In Data Detective, I call this: 👉 The Sorting Hat Problem Before analysis, you must ask: 👉 “What type of data am I looking at?” 💡 If you skip this step: ❌ You apply wrong techniques ❌ You misinterpret patterns ❌ Your conclusions become unreliable ✔ But if you classify data correctly: ✔ Everything becomes structured ✔ Analysis becomes logical ✔ Insights become meaningful 🚀 Want to identify data types using Python? 👉 Code: https://lnkd.in/g2HENF5M 📖 Book (DOI): https://lnkd.in/gQ2Af9uz #DataScience #Python #EDA #LearningByDoing #TeachingInnovation

🚀 Day 12/20 — Python for Data Engineering Filtering & Selecting Data (Pandas) Now that we know what a DataFrame is… 👉 The real work starts here: getting only the data you need 🔹 Selecting Columns df["name"] 👉 Select a single column df[["name", "salary"]] 👉 Select multiple columns 🔹 Filtering Rows df[df["salary"] > 50000] 👉 Get rows based on condition 🔹 Multiple Conditions df[(df["salary"] > 50000) & (df["age"] < 30)] 👉 Combine conditions 🔹 Why This Matters Reduce unnecessary data Focus on relevant records Improve performance 🔹 Real-World Use 👉 Raw Data → Filter → Useful Data 💡 Quick Summary Selecting = columns Filtering = rows 💡 Something to remember You don’t need all the data… You need the right data. #Python #DataEngineering #DataAnalytics #LearningInPublic #TechLearning #Databricks

🚀 Day 1/20 — Python for Data Engineering From SQL to Python: The Next Step After spending time with SQL, I realized something: 👉 SQL helps us query data 👉 But real-world data engineering needs more than that. We need to: process data transform data move data across systems That’s where Python comes in. 🔹 Why Python? Python helps us go beyond querying: ✅ Process data from multiple sources ✅ Build data pipelines ✅ Automate workflows ✅ Handle large datasets efficiently 🔹 Simple Example import pandas as pd df = pd.read_csv("data.csv") print(df.head()) 👉 From raw file → usable data in seconds 🔹 SQL vs Python (Simple View) SQL → Get the data Python → Work with the data Together, they form the foundation of data engineering. 💡 Quick Summary SQL is where data access begins. Python is where data engineering truly starts. 💡 Something to remember SQL gets the data. Python makes the data useful. #Python #DataEngineering #DataAnalytics #LearningInPublic #TechLearning #Databricks

🚀 Day 67 – Project Work | Pandas for Data Handling Today I worked with Pandas, one of the most important Python libraries for data manipulation in Machine Learning projects 📊🐼 🔹 What I worked on today: ✔️ Loaded dataset using Pandas ✔️ Cleaned missing values ✔️ Handled duplicates & inconsistencies ✔️ Performed basic data analysis ✔️ Converted data into model-ready format 🔹 Key Concepts I used: 👉 DataFrames & Series 👉 Data cleaning techniques 👉 Filtering & selecting data 👉 Feature preparation 🔹 How it helped my project: 🎯 Improved data quality before prediction 🎯 Made preprocessing pipeline more efficient 🎯 Better understanding of real-world messy data 🔹 Challenges: ⚡ Handling null values correctly ⚡ Choosing the right preprocessing steps ⚡ Managing large datasets 🔹 What I learned: 💡 Good data = Good model performance 💡 Pandas is the backbone of data preprocessing 💡 Small cleaning steps make a big difference 📌 Next Step: Integrate Pandas preprocessing directly into my FastAPI pipeline 🚀 #Day67 #Pandas #DataScience #MachineLearning #FastAPI #Python #ProjectWork

Started learning Pandas — and now data actually makes sense After working with NumPy, I realized something: Handling real-world data (like CSV files) still felt a bit messy. That’s where Pandas comes in. It’s a Python library designed to make working with structured data simple and efficient. 📊 What’s happening here: • read_csv() loads data into a table-like structure • head() shows the first few rows • info() gives a summary of the dataset 💡 What I understood today: – Pandas organizes data in a structured format (DataFrame) – It makes reading and exploring data very easy – This is exactly how real datasets are handled in Data Science This feels like a big step from writing basic programs to actually understanding data. Next: Selecting specific columns and filtering data in Pandas #Python #Pandas #DataAnalysis #MachineLearning #LearningInPublic #DataScience Here is the code: