Descriptive Statistics with Python

🐍 Day 74 — Mode Day 74 of #python365ai 🔁 The mode is the most frequent value in a dataset. Example: from scipy import stats stats.mode(data) 📌 Why this matters: Mode is useful for categorical data analysis. 📘 Practice task: Find the most common value in a list. #python365ai #Mode #Statistics #Python

Monday Data Thought One thing I’m learning while building analytics projects: Data rarely answers the question immediately. You explore it. Clean it. Question it. Then explore it again. Sometimes what you expect to find isn’t there. Sometimes the real insight appears in a completely different metric. That’s the interesting part of analytics, the process of discovery. Every dataset teaches something new. What’s the most surprising insight you’ve discovered while analyzing data? #DataAnalytics #SQL #Python #BusinessIntelligence #LearningInPublic

📊 Completed Task-04: Sentiment Analysis & Visualization Performed sentiment analysis on social media data to understand public opinion and attitudes towards a topic. Key Steps: •Processed text data • Analyzed sentiment (positive, negative, neutral) •Visualized sentiment distribution Key Insights: • Identified overall public sentiment trends • Observed variation in opinions across data 🛠️ Tools Used: Python, Pandas, TextBlob, Matplotlib This task helped me understand how text data can be analyzed to extract meaningful insights. #DataScience #SentimentAnalysis #Python #EDA #LearningJourney Github link : https://lnkd.in/gujAADPg Prodigy InfoTech

Wednesday Data Thought One thing I’m learning while working on analytics projects: The first result you get from a dataset is rarely the final answer. You explore the data. You question assumptions. You test different metrics. Sometimes a small change in how you calculate something can completely change the insight. That’s why good analysts don’t just trust the first number they see, they investigate the story behind it. Still learning. Still building. #DataAnalytics #SQL #Python #BusinessIntelligence #LearningInPublic

Understanding inverse relationships in data 📊 This visualization demonstrates a negative correlation — as one variable increases, the other decreases. Recognizing such patterns is essential for building accurate predictive models and making data-driven decisions. #Python #DataScience #Statistics #DataVisualization #Analytics

Handling Missing Data Missing data is common. Techniques: • Drop records • Mean/median imputation • Forward fill • Predictive filling • Business-rule handling Context matters. #Python #DataCleaning #Analytics #DataScience #TechSkills

One thing I’ve learned while working with data.. “Data doesn’t lie… but it can definitely mislead.” The real skill is not just creating charts, but asking the right questions. Good analysis = Good questions + Clean data + Clear thinking Still exploring, still learning 📊 #DataAnalytics #Python #BusinessAnalysis #LearningJourney

🧠 Data Analytics Quiz Which chart is best for showing the relationship between two variables? A) Pie Chart B) Bar Chart C) Scatter Plot D) Histogram 💬 Comment your answer below 👉 Swipe to see the correct answer. #DataAnalytics #Python #DataScience #Learning

📊 Mean–Variance–Standard Deviation Analysis | Python Statistics I built a statistical calculator to analyze datasets using core descriptive statistics. 🔹 Calculated mean, variance, and standard deviation across matrix dimensions 🔹 Converted raw data into structured NumPy arrays 🔹 Visualized data distribution using histograms and statistical markers Implemented in Python using NumPy and Matplotlib. 🔗 GitHub repository https://lnkd.in/dQqNTari I’m continuing to strengthen my skills in Applied Statistics and Data Analysis. #Python #DataScience #Statistics #DataAnalysis #NumPy

🐍 Day 72 — Mean (Average) Day 72 of #python365ai ➗ The mean is the average value of a dataset. Example: import numpy as np data = [10, 20, 30] print(np.mean(data)) 📌 Why this matters: The mean helps describe the central tendency of data. 📘 Practice task: Calculate the mean of five numbers. #python365ai #Mean #Statistics #Python