From Code to Insights: Asking Better Questions with Data

Today I learned that small functions can answer big questions fast. A simple groupby() + mean() helped me quickly compare performance across categories instead of scanning rows manually. Lesson: you don’t need complex models to create value — just the right question and a clean dataset. Still building fundamentals, one function at a time. #Python #SQL #DataAnalytics #LearningInPublic #BeginnerDataAnalyst

Today I worked on skewness in data analysis and explored: ➕ Positively skewed data ➖ Negatively skewed data 🔔 Normal distribution Along with this, I implemented Mean, Median, and Mode using Python to understand how these measures behave under different distributions. This practice helped me clearly see the relationship between data shape and statistical measures. Learning by doing, one concept at a time 🚀 #DataScience #Statistics #Skewness #Python #DataAnalysis #LearningJourney #Analytics

𝗧𝗵𝗶𝘀 𝗦𝗶𝗺𝗽𝗹𝗲 𝗗𝗮𝘁𝗮 𝗦𝗰𝗶𝗲𝗻𝗰𝗲 𝗖𝗵𝗲𝗰𝗸 𝗖𝗮𝗻 𝗦𝗮𝘃𝗲 𝗬𝗼𝘂 𝗛𝗼𝘂𝗿𝘀 Before blaming the model, check the data types. Numbers stored as text dates stored as strings categories treated as numbers Small datatype issues silently break analysis. Many “model problems” are actually data problems. Two minutes of checking can prevent hours of debugging later. #DataScience #MachineLearning #DataAnalytics #Python #AI #LearningInPublic

Headline: Stop guessing, start modeling: Using NumPy for Polynomial Regression 📈 Linear regression is great, but real-world data is rarely a straight line. When your data curves, Least Squares Polynomial Fit is your best friend. By minimizing the squared distance between your data points and the functional curve, you can uncover patterns that a simple linear model would miss. Here’s how I streamline the process using Python: The Discovery: Use np.polyfit(x, y, deg) to determine the optimal parameters for your independent and dependent variables. The Evaluation: Pass those coefficients into np.polyval() to generate your estimation. The Validation: Always plot your polyval results against your raw data. If the "residuals" (the gap between the dot and the line) are too large, it’s time to adjust your degree. Pro-tip: Be careful with the deg (degree) parameter. A degree too high leads to overfitting—where you're modeling the noise, not the signal! #DataScience #Python #Numpy #QuantitativeAnalysis #MachineLearning

Pandas Advanced – Part 7 🐼📊 This video focuses on how analysts think, not just syntax. Instead of jumping into code, we learn how to: Clean data correctly Avoid misleading insights Ask better analytical questions If you’re learning Pandas for real-world data analysis, this part is important. ▶️ Watch: https://lnkd.in/gT2xC4EE 📁 GitHub: https://lnkd.in/gdzNcMaT #Pandas #DataAnalysis #Python #Analytics #LearningInPublic #PyAIHub

Today I explored some common NumPy operations in Python 🐍 NumPy makes working with numerical data fast and efficient. Understanding its core operations is essential for data analysis and machine learning. Some important operations I learned: 🔹 Reshape – change array dimensions 🔹 Transpose – swap rows and columns 🔹 Sum – calculate total values 🔹 Mean – find average 🔹 Sort – arrange data 🔹 Max / Min – find extreme values These operations help transform raw data into meaningful insights. Still learning step by step, but enjoying the process of building strong foundations in data science 🚀 #Python #NumPy #DataScience #MachineLearning #LearningInPublic #100DaysOfCode #CareerSwitch

📊 Day 13/90 — Creating Powerful Visuals with Seaborn Yesterday we learned basic visualization. Today we level up using Seaborn, a Python library that helps create more professional and insightful charts. ✅ Today’s Focus: • What is Seaborn & why analysts use it • Creating attractive statistical charts • Visualizing relationships between variables • Understanding distributions & trends 🎯 Why this matters: Seaborn makes it easier to discover patterns and present insights in a professional, presentation-ready format. 📌 Practice Tip: Try this in Python: import seaborn as sns import matplotlib.pyplot as plt data = [12, 15, 14, 10, 18, 20, 17] sns.histplot(data) plt.show() Better visuals → clearer insights → stronger impact. 💬 Comment “DAY 13” if you’re learning with me. #DataAnalytics #Seaborn #DataVisualization #Python #LearningInPublic #90DaysChallenge

📊 Data analysis isn’t always about charts and visuals. Sometimes, it can feel a bit less exciting than graphs and dashboards. Today, I started working on text analysis, focusing on quick and practical methods to move efficiently through the process. Simple exercises like this help build strong foundations and keep progress steady—step by step. Here I shaw step by step the path to make correctly the process, and the correspondent code. The most important thing is to understand what the business is asking, and translate to python language. #DataAnalysis #Python #TextAnalysis #LearningByDoing #ContinuousImprovement

𝗧𝗵𝗶𝘀 𝗦𝗶𝗺𝗽𝗹𝗲 𝗗𝗮𝘁𝗮 𝗦𝗰𝗶𝗲𝗻𝗰𝗲 𝗛𝗮𝗯𝗶𝘁 𝗣𝗿𝗲𝘃𝗲𝗻𝘁𝘀 𝗠𝗮𝗻𝘆 𝗠𝗶𝘀𝘁𝗮𝗸𝗲𝘀 Before training any model, always look at a few rows of your data. df.head() You immediately notice: wrong formats unexpected values columns that don’t make sense Many problems are visible in seconds if you simply look at the data first. Two minutes of checking can save hours of confusion later. #DataScience #MachineLearning #DataAnalytics #Python #AI #LearningInPublic

Pandas 3.0 is here! 🎉https://lnkd.in/dfAUP2bH - Copy-on-Write (CoW) fully implemented: SettingWithCopyWarning is gone ✅. No more debugging mysterious copies - chained assignments just work - pd.col() syntax: Clean column references in assign() and loc() without messy lambdas. E.g., df.assign(c=pd.col('a') + pd.col('b')) - Faster UDFs 🚀: No more "slow as molasses" user-defined functions - major perf boosts via better optimization (full Arrow backend didn't land, but it's solid) I made a Kaggle notebook to try https://lnkd.in/d-SsfryV #Pandas #DataScience #Python #DataAnalysis #MachineLearning