𝗘𝘃𝗲𝗿𝘆 𝗱𝗮𝘁𝗮 𝘀𝗰𝗶𝗲𝗻𝘁𝗶𝘀𝘁 𝗸𝗻𝗼𝘄𝘀 𝘁𝗵𝗲 𝗳𝗲𝗲𝗹𝗶𝗻𝗴: the model is perfect, the data is loaded, but then... you hit run. And you wait. ☕️ My recent project was a Monte Carlo Stock Simulation, calculating 100,000 future price paths. It was a beautiful financial model, but it had a silent killer: the Python for loop. The loop was supposed to calculate 25.2 million daily returns. The Nightmare: I timed the initial run. The Python loop method took 1 minute and 13 seconds. Over a minute of wasted time, just watching the cursor spin, waiting for the interpreter to sequentially check 25.2 million individual steps. The Hero: I realized the answer wasn't better hardware; it was a better approach: NumPy Vectorization. I replaced the nested loops with a single line of code, using the power of Ufuncs (np.cumsum, np.exp) to process the entire array at once. The Victory: The optimized version took just 1.19 seconds. That's not just faster—it's 62x FASTER! We turned an agonizing minute of waiting into an instant result, all by shifting the work from slow Python to optimized C code. This carousel walks you through the entire story: from the slow code (the killer) to the single-line solution (the hero). Swipe through to see the exact code comparison and how we crushed that 62x speed barrier! 👇 #DataStorytelling #Python #NumPy #Vectorization #CodingTips #DataScience

Day 3/90 📅 Data Analysis with Pandas & Numpy Today’s session was all about getting hands-on with data using Python libraries chiefly Pandas and NumPy. Here’s what I covered: 1. Importing and exploring datasets using Pandas 2. Handling missing values and duplicates 3. Filtering and slicing dataframes 4. Applying functions and transformations 5. Working with groupby and aggregations 6. Basic statistics with NumPy (mean, median, std) 7. Combining dataframes with merge() and concat() To apply today’s learnings, I built a mini project: Sales Insights Dashboard Using a simple CSV of store transactions 1. Loaded and cleaned the data in Pandas 2. Aggregated total revenue by region, category, and month 3. Identified top-performing products 4. Exported a summary table as a clean report Stayed away from visuals today to prevent overwhelming myself with workload On to the next one! One step at a time ☑️ #AIEngineer #LearningInPublic #DataScienceJourney #Python #Pandas #NumPy #90DaysChallenge #MachineLearning #Consistency

When it comes to data transformation, Pandas and NumPy are two of the most important tools every data engineer should master. Together, they make the manipulation of data faster, cleaner, and more efficient. With NumPy, you are able to explore how n-dimensional arrays enable high-performance numerical computations. Tasks that would normally take multiple loops in pure Python can be done in just one line using vectorization and broadcasting. Then came Pandas, built on top of NumPy, which provides powerful tools for handling real-world datasets. Working on data often require us to Load and inspect data from CSV and JSON files, Handle missing values and duplicates, Perform transformations using groupby, merge, and pivot operations. Using Pandas and NumPy helps with faster computations and cleaner data pipelines. What really stood out is how these two libraries simplify the data preparation process, turning raw, messy data into something structured and ready for analysis or storage. Every dataset tells a story and today, I’m learning the language that lets me read it. #SamsonDataEngineeringJourneyWith10alytics #DataEngineeringWith10alytics #NumPy #Pandas #Python #DataTransformation #LearningInPublic #DataEngineering

I am excited to share that Production-Ready Data Science is now live on Leanpub 🎉 On Leanpub, you can choose your price and get updates as more examples and chapters roll out. This book dives into the real engineering skills behind dependable data systems, including: • Testing • CI and CD • Environments and packaging • Data validation and logging • Reproducible workflows If you want to take your data work beyond notebooks and into reliable production environments, this is for you. 📚 Link to the book: https://bit.ly/3LGjnOZ #DataScience #Python

Day[4] of Data Engineering Series : Today, I focused on strengthening my core data skills: 🔹 SQL: Learned about Window Frames in SQL. Explored how to use ROWS BETWEEN and RANGE BETWEEN for precise data analysis. Understood how window frames refine analytical queries and help in calculating moving averages, running totals, and rankings effectively. 🔹 Python (NumPy Library): Completed full understanding of the NumPy library. Practiced array creation, reshaping, indexing, and slicing. Explored vectorized operations, broadcasting, and performance optimization. Realized how NumPy forms the foundation for data analysis and numerical computation in Python. #SQL #Python #NumPy #DataEngineering #DataAnalytics #LearningJourney #TechGrowth #ContinuousLearning

🐍 𝐏𝐲𝐭𝐡𝐨𝐧 𝐟𝐨𝐫 𝐃𝐚𝐭𝐚 𝐀𝐧𝐚𝐥𝐲𝐭𝐢𝐜𝐬 — 𝐌𝐲 𝐎𝐧𝐠𝐨𝐢𝐧𝐠 𝐋𝐞𝐚𝐫𝐧𝐢𝐧𝐠 𝐉𝐨𝐮𝐫𝐧𝐞𝐲 📊 As I dive deeper into the world of data analytics, 𝐏𝐲𝐭𝐡𝐨𝐧 has become one of my most powerful tools. From 𝐝𝐚𝐭𝐚 𝐜𝐥𝐞𝐚𝐧𝐢𝐧𝐠 with pandas, to 𝐯𝐢𝐬𝐮𝐚𝐥𝐢𝐳𝐢𝐧𝐠 𝐭𝐫𝐞𝐧𝐝𝐬 with matplotlib and seaborn, and exploring 𝐝𝐚𝐭𝐚 𝐩𝐚𝐭𝐭𝐞𝐫𝐧𝐬 using numpy — every new concept is helping me understand how data truly works. Here are a few key things I’ve learned recently: ✅ DataFrames make complex data easy to handle. ✅ A few lines of Python can automate hours of manual work. ✅ Visualization libraries turn numbers into insights. Learning Python is teaching me that it’s not just about code — it’s about clarity, creativity, and curiosity. #Python #DataAnalytics #Pandas #NumPy #Matplotlib #DataVisualization #LearningJourney #BusinessIntelligence #Analytics #DataScience

🧠 Why do we still perform manual EDA when Pandas Profiling can do it in one line? Recently, I explored Pandas Profiling, and I was amazed — it automatically generates a complete Exploratory Data Analysis report in just one command! 📊 You get: ✅ Summary statistics for all features ✅ Correlation heatmaps ✅ Missing value reports ✅ Outlier detection ✅ Distribution plots So… why do we still do EDA manually? 🤔 Because manual EDA gives deeper control and contextual understanding — we can focus on what matters most to the problem instead of just what’s visible in the data. In short: 🧩 Pandas Profiling = Quick overview 🔍 Manual EDA = Detailed insight Both go hand-in-hand for a strong data analysis workflow 💪 #DataAnalysis #EDA #Python #MachineLearning #DataScience #Pandas #PandasProfiling

📘 Experiment 1: Data Acquisition Using Pandas As part of my Data Science and Statistics lab, I explored the fundamentals of data acquisition and loading using the Pandas library in Python. This experiment focused on efficiently importing and managing datasets from different file formats such as CSV, Excel, and JSON. Key learning outcomes included:  • Utilizing Pandas functions for reading and exploring datasets  • Performing initial data inspection using .head(), .tail(), .info(), and .describe()  • Understanding dataset structure, size, and dimensions for better preprocessing This experiment provided a solid foundation in data handling and preparation — essential skills for performing effective data analysis and building reliable machine learning workflows. 📁 Explore the repository here:  👉https://lnkd.in/epWys7e7 #DataScience  #MachineLearning  #Python  #Pandas  #Statistics  #JupyterNotebook  #DataAnalysis  #GitHub  #LearningByDoing Ashish Sawant Sir