Mohammedali Saiyed’s Post

Day 16/75 — An unpopular truth about learning Data Science 👇 You don’t need more courses. I used to think: 👉 “One more course and I’ll be ready” But the reality? I was just avoiding real work. 💡 The truth is: You learn more by: • Working on messy datasets • Making mistakes • Fixing errors than watching hours of tutorials. 🚨 Courses feel productive. 👉 But real progress comes from doing. Once I started: • Analyzing real data • Building small projects • Sharing my work Everything changed. Now I focus less on: ❌ Consuming And more on: 👉 Creating What do you think—courses or hands-on practice? 👇 #DataScience #LearningInPublic #Python #CareerGrowth #OpenToWork

🚀 Turning Data into Insights with Python! I’m excited to share my Student Result Analysis Project, where I applied data analysis and basic machine learning techniques to understand student performance. 📊 What I did: • Cleaned and processed raw data using Pandas & NumPy • Performed exploratory data analysis (EDA) to find patterns and trends • Created visualizations using Matplotlib & Seaborn • Applied basic models using Scikit-learn 💡 Key Learning: Data is powerful — with the right tools, we can uncover insights that help in better decision-making. This project enhanced my skills in: ✔ Python Programming ✔ Data Analysis & Visualization ✔ Problem Solving I’m actively looking to grow in Data Science and Machine Learning and open to learning opportunities! 🔗 Feel free to connect with me and share your feedback! #Python #DataScience #MachineLearning #DataAnalysis #EDA #NumPy #Pandas #Seaborn #Matplotlib #ScikitLearn

Day 14/75 — What learning Data Science actually looks like for me 👇 A lot of people think I spend hours building models every day. That’s not true. Here’s what my learning actually looks like: 🕒 2–3 hours a day: • 30% → Learning concepts (courses, docs) • 50% → Working on datasets (cleaning, analyzing) • 20% → Debugging errors 😅 Most of my time goes into: 👉 Understanding the data 👉 Fixing problems 👉 Trying things that don’t work 💡 And honestly… That’s where the real learning happens. 🚨 Biggest realization: You don’t need 10 hours a day. You need: 👉 Consistency 👉 Focus 👉 Real practice That’s why I started this 75-day challenge. Small steps. Every day. How much time do you spend learning daily? 👇 #DataScience #LearningInPublic #Consistency #Python #OpenToWork

Today I spent some time understanding how data flows in real systems and how different tools are used in the data industry.   While learning, I realized how Python makes working with data very simple and flexible. It helps in handling and processing data in a clear step-by-step way.   What I understood today is that in the data industry, Python is widely used because it is easy to write, easy to understand, and very powerful when dealing with large amounts of data.   I also explored how combining Python with SQL becomes very powerful. SQL helps in extracting and organizing data from databases, and Python helps in further processing, transforming, and preparing that data for analysis or reporting.   Key takeaway: Modern data systems are built on simple but powerful tools working together. Understanding how data flows from one step to another is more important than just learning individual tools. Still learning and building my understanding step by step.   #Python #SQL #DataEngineering #DataAnalytics #DataFlow #LearningInPublic #OpenToWork

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

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

Most Python beginners learn lists but not how to actually use them effectively. 🐍 If you’re preparing for roles in Python Programming, Data Analytics, or Data Science, understanding Python list methods is a must. Because in real-world coding, it’s not just about creating lists, it’s about manipulating data efficiently. Here are some essential Python list methods you should know: 🔹 append() – Add a single element to the end of the list 🔹 extend() – Add multiple elements to a list 🔹 insert() – Insert an element at a specific position 🔹 remove() – Remove a specific element 🔹 pop() – Remove element by index (or last by default) 🔹 sort() – Sort the list in ascending/descending order 🔹 reverse() – Reverse the order of elements 🔹 index() – Find the position of an element 🔹 count() – Count occurrences of a value 💡 Why this matters: Efficient use of list methods helps you write cleaner code, process data faster, and solve problems effectively. These fundamentals are heavily used in data cleaning, automation, scripting, and algorithm-based problem solving. 🌐 Visit our website: infinitylearning.online Follow us for more insights on Python, AI, and Tech Careers: Facebook: @infinitylearningmumbai Instagram: @infinitylearningmumbai X: @InfinityLearnMu #Python #PythonProgramming #DataStructures #Coding #DataAnalytics #MachineLearning #ProgrammingBasics #TechSkills #Upskill 

🚀 Why do customers leave a company? I recently worked on a Customer Churn Prediction Project to find out—and the results were surprising. 🔧 Tech Stack: Python | Pandas | NumPy | Scikit-learn | Matplotlib 📊 What I did: Cleaned and analyzed customer data Built ML models (Logistic Regression, KNN) Tuned hyperparameters using GridSearchCV 💡 Key Insight: Customers with month-to-month contracts were significantly more likely to churn compared to long-term contract users. 📈 The model achieved ~85% accuracy in predicting churn. 🔗 I’ve shared the full project on GitHub (link in comments). Would love your feedback! 🙌 #MachineLearning #DataScience #Python #Projects #OpenToWork

A few months ago, I thought learning Data Analytics was all about tools. Python, SQL, Power BI… I believed mastering them was enough. But working on projects slowly changed that thinking. I started realizing that: Data is messy Problems are not clearly defined And the “right answer” is not always obvious That’s when things became interesting. Instead of just learning tools, I started trying to understand: 👉 What problem am I actually solving? 👉 Why does this analysis matter? 👉 How would this help in real decisions? 💡 Biggest shift for me: From learning tools → to thinking like an analyst Still learning. Still improving. 💬 What was the biggest mindset shift in your learning journey? #DataAnalytics #Learning #CareerGrowth #Python #SQL #DataScience #Projects #OpenToWork