Joachim Schork’s Post

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

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

📘 Today’s Learning: Data Inspection Steps in Python 🐍📊 Exploring data is the first step before analysis. Today I learned some important data inspection steps using Python and Pandas: Before analysis, it’s important to inspect and clean the dataset for better accuracy and insights. 🔍 Data Inspection Steps: ✅ "df.head()" – View first rows ✅ "df.tail()" – View last rows ✅ "df.shape" – Check rows & columns ✅ "df.info()" – Data types & null values ✅ "df.describe()" – Statistical summary 🧹 Data Cleaning Steps: ✅ "df.isnull().sum()" – Find missing values ✅ "df.dropna()" – Remove null values ✅ "df.fillna()" – Fill missing values ✅ "df.duplicated().sum()" – Check duplicates ✅ "df.drop_duplicates()" – Remove duplicates Clean data = Better analysis 📈 #Python #Pandas #DataCleaning #DataInspection #DataAnalysis #DataScience #LearningJourney #Coding

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/e5YB7k4d #datascience #python #rstats #machinelearning #kmeans #statisticsglobehub

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

Recently, I’ve been improving how I format and present my plots in Python 📊 At first, I focused mainly on generating graphs. But I’ve learned that presentation plays a huge role in how insights are understood. In the plot below, I experimented with: - Different markers and colors to distinguish data trends - Combining multiple relationships in a single figure - Improving clarity so patterns are easier to interpret This helped me realise that: • A well-formatted plot communicates faster than raw numbers • Visual clarity makes trends (like growth patterns) obvious. • Small changes in styling can completely change how your data is perceived Data visualization isn’t just about plotting — it’s about telling a clear and compelling story with data. Still learning, but definitely improving with each project 💡 #DataScience #Python #DataVisualization #LearningJourney #Analytics

🚀 Want to Master NumPy the Smart Way? If you're learning Python for Data Science, this resource is GOLD! 👇 🔗 https://lnkd.in/gaWMcuYP 💡 This platform covers everything from basics to advanced — all in a simple, practical way. ✨ What you’ll learn: ✔ Arrays & matrix operations ✔ Real-world NumPy functions ✔ Data handling techniques ✔ Performance optimization tips ✔ Use-cases in AI & Machine Learning NumPy is the backbone of data science — it powers fast numerical computing with multidimensional arrays and high-level mathematical functions. (Vision Institute Of Technology) 🔥 Instead of random tutorials, follow a structured learning path that actually builds your skills step by step. 👉 Perfect for beginners + developers upgrading to Data Science! #NumPy #Python #DataScience #MachineLearning #AI #LearnPython #Coding #Developers #Tech

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

🐍 Day 17–20 of My 30-Day Python Learning Challenge 🚀 Over the last few days, I focused on improving my Mini Project: Log File Analyzer by making it more practical and closer to real-world usage. 📌 What I Improved: ✅ Removed Stopwords Ignored common words like "the", "is", "and" to focus on meaningful data. stopwords = {"the", "is", "and", "in", "to", "of"} filtered_words = [w for w in words if w not in stopwords] --- ✅ Data Cleaning (Punctuation Removal) Handled messy real-world text by removing special characters. import string for p in string.punctuation: content = content.replace(p, "") --- ✅ Better Word Frequency Analysis Used efficient logic to count words. word_count[word] = word_count.get(word, 0) + 1 --- ✅ Top Frequent Words Extraction top_words = sorted(word_count.items(), key=lambda x: x[1], reverse=True)[:3] --- 📊 Key Learning: Small improvements like cleaning and filtering data significantly improve accuracy. 📈 Next Steps: • Visualize results using graphs • Add user input support • Build a simple UI using Streamlit 💡 This project helped me understand how Python is used in: • Data analysis • Text processing • Real-world problem solving #Python #MiniProject #DataCleaning #LearningInPublic #SoftwareDeveloper #ProjectBuilding

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