Statistics Globe’s Post

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

My Day 15 of 90 Days Growth Challenge AMDOR ANALYTICS Today, we will look into an important Python concept called library Python without libraries will be boring and would be only used by expert programmer, it would be non-beginner friendly for entrants to start learning code with it. Today we talk about Python for everything because of the rich libraries in it. Programmers can build their library into Python to make life easier for debutant programmers. I remember working on Machine Learning algorithm using Scikit-Learn library, how could I do such predictions on those projects in my LinkedIn without libraries. What of the panda’s library for data manipulation I used to clean my data or the NumPy for numerical calculation; You can do your matrix calculation which is the hallmark of multivariate analysis with the help of that powerful library in Python. You can’t do your powerful visualization without matplotlib or seaborn, although I use matplotlib a lot for my statistical visualization and all these were made possible for us because of libraries in Python. Now, you can build fast API and web development using Django, Flask, FastAPI  et cetera because of libraries. We can used Tensor-Flow and Py Torch to build advanced frameworks used for deep learning and building complex neural networks for tasks like image and speech recognition. See y’all tomorrow #Techjourney #90daysgrowthchallenge #consistency #growth #aiengineering #Amdoranalytics

Most data analysts know Python. But not everyone uses it effectively. This image covers some advanced Pandas techniques, and honestly, these are the kind of things that make a real difference in day-to-day work. Not because they’re “advanced", but because they make your code cleaner, faster, and easier to maintain What stood out to me is Instead of writing long, step-by-step transformations, you can chain operations for cleaner pipelines, use vectorized calculations instead of loops, and combine multiple aggregations in a single step. Also, small things matter more than we think: 🔺 selecting only required columns 🔺 handling missing data thoughtfully 🔺 using proper joins instead of manual merges These don’t sound fancy, but they save a lot of time in real projects. 𝐈'𝐦 𝐡𝐨𝐬𝐭𝐢𝐧𝐠 𝐚 𝐰𝐞𝐛𝐢𝐧𝐚𝐫 𝐨𝐧 𝐀𝐩𝐫𝐢𝐥 26. 𝐌𝐨𝐫𝐞 𝐝𝐞𝐭𝐚𝐢𝐥𝐬 𝐡𝐞𝐫𝐞: 👇 https://lnkd.in/gXQZCDV8 Visual Credits: Sohan Sethi 𝑾𝒂𝒏𝒕 𝒕𝒐 𝒄𝒐𝒏𝒏𝒆𝒄𝒕 𝒘𝒊𝒕𝒉 𝒎𝒆? 𝘍𝒊𝒏𝒅 𝒎𝒆 𝒉𝒆𝒓𝒆 --> https://lnkd.in/dTK-FtG3 Follow Shreya Khandelwal for more such content. ************************************************************************ #Python #DataScience #Pandas #Analytics

Excel is where many data journeys begin. Python is where they scale. The real challenge is not learning a new tool. It is understanding how the same logic translates across tools. Filtering rows, sorting data, creating columns, handling missing values, joining tables. These are not tool-specific skills. They are analytical thinking patterns. When you understand how Excel actions map to Python (Pandas), you stop memorizing syntax and start thinking like a data professional. For Excel users, this is the fastest path to transition into Python. For Python learners, this builds clarity on what is happening behind the code. For working analysts, this improves speed, flexibility, and problem-solving across tools. Same problem. Different tools. One mindset. The goal is not to replace Excel. It is to expand your capability. #DataAnalytics #Python #Excel #Pandas #DataScience #BusinessIntelligence #DataAnalyst #Analytics #DataSkills #LearnPython #ExcelTips #DataEngineering #ETL #DataTransformation

Started this journey feeling completely lost. Python didn’t make sense, SQL looked like a foreign language, and you kept questioning if you were cut out for this. I need you to know; you figure it out. Those same Python concepts will click. SQL will start to feel natural. And you will grow into data science, even understanding how machine learning models work. It’s consistency, patience, and small wins over time. So relax, keep going, and trust the process. And if you are just starting too, it’s okay to feel lost… just don’t stop. #RisewithTechCrush #Tech4Africans #LearningwithTechCrush

🌟 Today’s Learning: Clearing Nulls & Replacing Missing Values in Python 🐍📊 Data cleaning is one of the most important steps in data analysis. Today I learned how to handle null values and missing data using Python Pandas. ✅ Key Learnings: 🔹 Detect null values using isnull() 🔹 Count missing values with sum() 🔹 Remove null rows using dropna() 🔹 Replace missing values using fillna() 🔹 Use mean / median / mode for smart replacements 💻 Example Code: Python import pandas as pd # Load dataset df = pd.read_excel("data.xlsx") # Check null values df.isnull() # Count null values in each column df.isnull().sum() # Remove rows with null values df.dropna() # Fill missing values with 0 df.fillna(0) # Fill missing values with mean df["Sales"] = df["Sales"].fillna(df["Sales"].mean()) # Fill missing values with median df["Age"] = df["Age"].fillna(df["Age"].median()) # Drop rows with null values df.dropna(inplace=True) 📈 Clean data = Better insights = Better decisions! #Python #Pandas #DataCleaning #DataAnalysis #LinkedInLearning #MachineLearning #CodingJourney #DataScience

🚀 Most beginners make this mistake in Data Science… They jump into Machine Learning without mastering the most important foundation: Python. Why Python matters? Python is not just a programming language — it is the foundation of modern Data Science workflows. * Simple and readable syntax * Powerful data science libraries * Industry standard across companies Core libraries you will use: * NumPy → numerical computing * Pandas → data analysis * Matplotlib / Seaborn → visualization * Scikit-learn → machine learning Simple example: data = [10, 20, 30, 40] avg = sum(data) / len(data) print(avg) Where Python is used: * Data analysis * Machine learning models * Recommendation systems * AI-based applications Key insight: In Data Science, tools do not make you powerful. Your understanding of how to use them does. Python just makes that journey smoother. #DataScience #Python #MachineLearning #AI #LearningInPublic

A beginner mindset shift I’m learning in Python for data science: think in arrays, not loops. I used to believe that better performance meant writing more efficient 'for loops'. However, I’m starting to realize that in data science, the key question is: do I need the loop at all? When I loop through large data in Python, it processes values one by one. In contrast, using NumPy or Pandas operations allows the work to shift into optimized low-level code designed to handle arrays much more efficiently. This realization has transformed my approach to writing code for data work. It’s not solely about speed; it’s about adopting the right mental model for the problem. One beginner habit I’m working to break is reaching for a loop every time I want to transform data. Instead, I’m cultivating a better habit: if the data is array-shaped, I’ll try thinking in array operations first. #Python #DataScience #NumPy #Pandas #MachineLearning #CodingJourney

Most of us don’t struggle with learning Python. We struggle with connecting the dots. You watch tutorials. You try small examples. But when it comes to actually working with data… everything feels scattered. That’s exactly where structured notes make a difference. I’ve been going through this Python for Data Science cheat sheet and it quietly covers what we actually use day-to-day: • Basic Python operations (because fundamentals still matter) • NumPy for handling arrays and computations • Pandas for real-world data manipulation • Visualization with Matplotlib & Seaborn • Machine learning basics with scikit-learn Not in isolation ~ but as a flow. And that’s the shift. From learning topics To understanding how things connect Because in real projects, you don’t use just Pandas or just NumPy. You use everything together. One thing I’ve realised while revisiting these concepts: Clarity doesn’t come from more content. It comes from structured understanding. So if you’re learning data analytics or data science right now don’t just collect resources. Spend time with fewer things, but understand them deeply. Pdf Credits : DataCamp If you’re looking for structured guidance, notes, or want to discuss your learning path: https://lnkd.in/gasgBQ6k #DataScience #Python #DataAnalyst #Numpy #Pandas #Matplotlib #Seaborn #Scipy #DataCareers #AI #Jobs

Python becomes powerful not when you learn more syntax, but when you stop writing unnecessary code. In real data analysis and data science work, speed, clarity and reliability matter far more than clever one-liners. The difference often comes down to choosing the right built-in function at the right moment. Over time, I noticed the same pattern: a small group of Python functions keeps appearing across data cleaning, transformation, validation, debugging and everyday analysis tasks. Mastering these functions changes how confidently and efficiently you work with data. That’s why I put together a practical reference focused on Python functions that are genuinely useful in real workflows, not academic examples. The goal is simple: help analysts and data scientists write cleaner logic, reduce complexity and build code they can actually maintain. If Python is part of your daily work, this kind of reference saves time repeatedly. Follow for more practical content on Python, data analysis and applied data science. #python #pythonprogramming #dataanalysis #datascience #dataanalytics #analytics #machinelearning #coding #programming #learnpython #pythondeveloper #datacleaning #pandas #numpy #ai