Statistics Globe’s Post

Sometimes you want to practice a method or create a teaching example, but it is difficult to find a dataset that truly fits your needs. Real data is often messy, restricted, or simply not aligned with what you want to demonstrate. That’s where drawing your own data becomes very useful. Instead of searching for the "perfect" dataset, you can create one that matches your exact requirements. A great tool for this is the drawdata library in Python. It allows you to visually sketch data points and convert them into structured datasets within seconds. The image below illustrates a typical workflow: You generate data in Python using drawdata and then apply a method to it, for example k-means clustering. What makes this even more interesting is the environment used here. The Positron IDE is a modern IDE by Posit, the company behind RStudio, and is designed for multi-language workflows. You can work with Python and R in the same environment, side by side. In this example, the data is created in Python and then directly analyzed in R without switching tools. This kind of setup can make your workflow more efficient, especially if you regularly move between languages. I’ve just published a new module in the Statistics Globe Hub on how to draw synthetic datasets using the drawdata Python library and analyze them afterward in R using k-means clustering. It includes a full video walkthrough, practical examples, and detailed exercises. Not part of the Statistics Globe Hub yet? The Hub is a continuous learning program with new modules released every week on topics such as statistics, data science, AI, R, and Python. More information about the Statistics Globe Hub: https://lnkd.in/exBRgHh2 #datascience #python #rstats #machinelearning #kmeans #statisticsglobehub

Creating example datasets should not be the hardest part of your workflow. Instead of searching for data that almost fits your needs, you can simply draw your own. With the drawdata library in Python, you can sketch data points and turn them into structured datasets within seconds. Here are some key advantages: ✔ Full control over your data ✔ Create exactly the patterns you want to demonstrate ✔ No dependency on external datasets ✔ Fast prototyping of ideas and methods ✔ Ideal for teaching and clear examples ✔ Saves time compared to searching for and cleaning data The visualization below shows the idea. Instead of generating data with formulas, you draw points on a canvas, create clusters, trends, and outliers, and then export the result as a dataset for analysis. This makes it easy to create realistic scenarios for testing, teaching, and debugging. I’ve just published a new module in the Statistics Globe Hub that shows how to draw synthetic datasets using the drawdata Python library and analyze them afterward in R with k-means clustering. It includes a full video walkthrough, practical examples, and detailed exercises. Not part of the Statistics Globe Hub yet? It is an ongoing learning program with new modules released every Monday, covering topics such as statistics, data science, AI, R, and Python. More information about the Statistics Globe Hub: https://lnkd.in/e5YB7k4d #datascience #python #machinelearning #datavisualization #syntheticdata #statisticsglobehub

Creating example datasets should not be the hardest part of your workflow. Instead of searching for data that almost fits your needs, you can simply draw your own. With the drawdata library in Python, you can sketch data points and turn them into structured datasets within seconds. Here are some key advantages: ✔ Full control over your data ✔ Create exactly the patterns you want to demonstrate ✔ No dependency on external datasets ✔ Fast prototyping of ideas and methods ✔ Ideal for teaching and clear examples ✔ Saves time compared to searching for and cleaning data The visualization below shows the idea. Instead of generating data with formulas, you draw points on a canvas, create clusters, trends, and outliers, and then export the result as a dataset for analysis. This makes it easy to create realistic scenarios for testing, teaching, and debugging. I’ve just published a new module in the Statistics Globe Hub that shows how to draw synthetic datasets using the drawdata Python library and analyze them afterward in R with k-means clustering. It includes a full video walkthrough, practical examples, and detailed exercises. Not part of the Statistics Globe Hub yet? It is an ongoing learning program with new modules released every Monday, covering topics such as statistics, data science, AI, R, and Python. More information about the Statistics Globe Hub: https://lnkd.in/exBRgHh2 #datascience #python #machinelearning #datavisualization #syntheticdata #statisticsglobehub

This is a great reminder that the hardest part of data science is often data preparation, not modeling. Being able to draw your own datasets with tools like drawdata is a game-changer—especially for teaching, prototyping, and testing ideas quickly. It gives full control to create patterns, clusters, and edge cases without relying on messy real-world data. Simple idea, but incredibly powerful. Looking forward to exploring this further. #datascience #python #machinelearning #syntheticdata #dataanalysis #analytics #datavisualization #datamodeling #featureengineering #deeplearning #artificialintelligence #ai #ml

Ever struggled to find the right dataset to test or explain a method? Instead of searching endlessly, you can simply create your own. With the drawdata library in Python, you can visually sketch data points and turn them into a usable dataset within seconds. This makes it much easier to demonstrate patterns exactly the way you need them. In the example below, the workflow is straightforward: Data is created in Python and then analyzed in R using k-means clustering. What makes this even more powerful is the setup: Using the Positron IDE, you can work with Python and R in the same environment. No switching tools, no interruptions, just a smooth multi-language workflow where data creation and analysis happen side by side. I’ve just published a new module in the Statistics Globe Hub that shows how to draw synthetic datasets using the drawdata Python library and analyze them afterward in R with k-means clustering. It includes a full video walkthrough, practical examples, and detailed exercises. Not part of the Statistics Globe Hub yet? The Hub is a continuous learning program with new modules released every week on topics such as statistics, data science, AI, R, and Python. More information about the Statistics Globe Hub: https://lnkd.in/exBRgHh2 #datascience #python #rstats #machinelearning #kmeans #statisticsglobehub

Standard classification models tell you if a customer will leave, but Survival Analysis tells you <<when>>. I just published a new deep dive into Survival Analysis using Python and the lifelines library. Using telco churn data, I explore: ✅ The Kaplan-Meier Estimator: Visualizing the "survival" journey of a subscriber. ✅ Cox Proportional Hazards: Identifying exactly which behaviors (like high charges or complaints) accelerate the risk of churn. ✅ Censoring: How to handle customers who haven't churned yet without biasing your data. Treating churn like a timeline. Check out the full article and breakdown at Towards Data Science: https://lnkd.in/evH9Fk2R #DataScience #MachineLearning #SurvivalAnalysis #Python #ChurnPrediction #Analytics

Ever struggled to find the right dataset to test or explain a method? Instead of searching endlessly, you can simply create your own. With the drawdata library in Python, you can visually sketch data points and turn them into a usable dataset within seconds. This makes it much easier to demonstrate patterns exactly the way you need them. In the example below, the workflow is straightforward: Data is created in Python and then analyzed in R using k-means clustering. What makes this even more powerful is the setup: Using the Positron IDE, you can work with Python and R in the same environment. No switching tools, no interruptions, just a smooth multi-language workflow where data creation and analysis happen side by side. I’ve just published a new module in the Statistics Globe Hub that shows how to draw synthetic datasets using the drawdata Python library and analyze them afterward in R with k-means clustering. It includes a full video walkthrough, practical examples, and detailed exercises. Not part of the Statistics Globe Hub yet? The Hub is a continuous learning program with new modules released every week on topics such as statistics, data science, AI, R, and Python. More information about the Statistics Globe Hub: https://lnkd.in/e5YB7k4d #datascience #python #rstats #machinelearning #kmeans #statisticsglobehub

This Python tool just made vector databases optional for RAG. It's called PageIndex. It reads documents the way you do. No embeddings. No chunking. No vector database needed. # Here's the problem with normal RAG: It takes your document, cuts it into tiny pieces, turns those pieces into numbers, and searches for the closest match. But closest match doesn't mean best answer. # PageIndex works completely different. → It reads your full document → Builds a tree structure like a table of contents → When you ask a question, the AI walks through that tree → It thinks step by step until it finds the exact right section Same way you'd find an answer in a textbook. You don't read every page. You check the chapters, pick the right one, and go straight to the answer. That's exactly what PageIndex teaches AI to do. Here's the wildest part: It scored 98.7% accuracy on FinanceBench. That's a test where AI answers real questions from SEC filings and earnings reports. Most traditional RAG systems can't touch that number. Works with PDFs, markdown, and even raw page images without OCR. 100% Open Source. MIT License.

🔍 Exploratory Data Analysis (EDA) with Python Before building any model, you need to understand your data. That's exactly what EDA is about. EDA is the process of investigating datasets to discover patterns, spot anomalies, test hypotheses, and check assumptions — using visual and statistical methods. Here's how I approach it with Python: 1. Load & Inspect the Data python import pandas as pd df = pd.read_csv("data.csv") df.head() df.info() df.describe() → Understand shape, dtypes, null values, and basic statistics right away. 2. Handle Missing Values python df.isnull().sum() df.fillna(df.median(), inplace=True) → Never ignore nulls — they skew your results silently. 3. Univariate Analysis python import seaborn as sns sns.histplot(df['age'], kde=True) → Understand the distribution of each feature individually. 4. Bivariate & Multivariate Analysis python sns.heatmap(df.corr(), annot=True, cmap='coolwarm') sns.pairplot(df, hue='target') → Find correlations and relationships between features. 5. Detect Outliers python sns.boxplot(x=df['salary']) → Outliers can destroy model performance if ignored. 6. Feature Distribution by Class python sns.violinplot(x='target', y='feature', data=df) → See how features behave across different classes. 💡 EDA is not optional — it's the foundation of every reliable ML pipeline. The better you understand your data, the better your model will be. What's your go-to EDA library? Drop it in the comments 👇 #DataScience #Python #EDA #MachineLearning #Pandas #Seaborn #Analytics #DataAnalysis #AI

𝗣𝘆𝘁𝗵𝗼𝗻'𝘀 𝗗𝗮𝘁𝗮 𝗠𝗼𝗱𝗲𝗹 𝗜𝘀 𝗔𝗻 𝗔𝗣𝗜 Dunder methods let your objects work with the language. They are not features. They are protocols. The interpreter calls these methods. It looks at the class. It does not look at the instance. Dunders on instances do not work. Truthiness and Equality: - Python uses __bool__ for truth. - If __bool__ is missing, it uses __len__. - Length zero is False. - __eq__ handles equality. - Equal objects must have the same hash. - If you define __eq__, Python sets __hash__ to None. Comparisons and Math: - Python tries the left object first. - If it returns NotImplemented, Python tries the right object. - This lets your types work with built-in types. - Use __iadd__ for in-place changes to save memory. Attributes and Memory: - Use __getattr__ for lazy loading. - Use __slots__ to stop the creation of __dict__. - This saves memory for millions of objects. Avoid bugs by following the contract. Read the protocol docs. The data model is the most reliable part of Python. Source: https://lnkd.in/gx4m2id7