Shivam Saxena’s Post

Day #01 🚀 Today’s Learning Milestone in Python & Data Science Foundations Today I strengthened my understanding of Python’s core data structures — the building blocks of Data Science and problem solving. Here’s a structured summary of what I learned: 🔹 List Mutable (can add, update, delete elements) Ordered Indexed Allows duplicate values Best for ordered, changeable collections 🔹 Tuple Immutable (cannot modify after creation) Ordered Indexed Allows duplicate values Hashable (if elements inside are immutable) Useful when data should remain constant 🔹 Set Mutable (but elements must be immutable) Unordered Not indexed Does not allow duplicates Uses hashing internally Very fast membership testing (O(1)) 🔹 Dictionary Mutable Key–value pair structure Keys must be immutable & unique Values can be duplicated Uses hashing for fast lookups (O(1)) 💡 Key Concepts I Understood Deeply: Difference between mutable vs immutable What “hashable” means Why dictionary keys must be immutable Why sets are faster than lists for membership testing Internal working of hashing in sets and dictionaries Understanding these fundamentals strengthens problem-solving ability and prepares the foundation for advanced topics in Data Science and Machine Learning. Step by step, building strong basics. 💪 #Python #DataScienceJourney #MachineLearning #Programming #ComputerScience #PythonBasics #LearningInPublic #Hashing #DataStructures #TechGrowth

🚀 Day 5 – Python for Data Analytics Today I stepped deeper into the world of data with Python. I realized one thing — If Excel is the foundation, Python is the superpower. 💻⚡ 🔹 Why Python is important in Data Analytics? ✔ Easy to learn and versatile ✔ Handles large datasets efficiently ✔ Automates repetitive tasks ✔ Widely used in industry And the real power comes from its libraries 👇 📊 Pandas – Makes data cleaning and manipulation simple. (Filtering, grouping, transforming data easily) 🔢 NumPy – Performs fast numerical computations. Essential for calculations and mathematical operations. 📈 Matplotlib – Helps turn data into visual stories using charts and graphs. The more I learn Python, the more I understand — Data analytics is not just about analyzing data… It’s about solving real-world problems efficiently. Consistency > Motivation. Day by day, skill by skill. 🚀 💬 What was your first Python project? Tajwar Khan Ethical Learner Dr. Nitesh Saxena Dr. Rajeev Singh Bhandari @ #Day5 #Python #DataAnalytics #Pandas #NumPy #Matplotlib #LearningJourney #DataScience

Module 2 of My Python for Data Science Journey Ngao Labs This week was both challenging and exciting as I deepened my understanding of Python for Data Science. Here’s what I learned: v Python fundamentals – variables, control flow (if statements & loops), and writing reusable functions v Core data structures – Lists, Tuples, and Dictionaries v NumPy – working with arrays and performing fast numerical operations v Pandas – loading CSV files, cleaning data, handling missing values, filtering, and grouping data v Matplotlib – creating visualisations like line plots, bar charts, scatter plots, and histograms One key takeaway? Data is powerful — but knowing how to clean, analyse, and visualise it makes it meaningful. I also learned that debugging, patience, and consistent practice are just as important as the code itself. Looking forward to applying these skills to real-world datasets and continuing to grow 📈 💻 . #Python #DataScience #LearningJourney #NumPy #Pandas #Matplotlib

𝙔𝙤𝙪𝙧 𝙋𝙮𝙩𝙝𝙤𝙣 𝘾𝙤𝙙𝙚 𝙄𝙨 𝙒𝙖𝙨𝙩𝙞𝙣𝙜 𝙏𝙞𝙢𝙚, 𝙃𝙚𝙧𝙚’𝙨 𝙃𝙤𝙬 𝙩𝙤 𝙁𝙞𝙭 𝙄𝙩 Most Python scripts work fine… But fine isn’t fast. And slow code costs you time, memory, and sometimes even money. The good news? Just a few smart tweaks can make your scripts run fast. Here are 8 easy ways to speed up your Python code: ☉ 𝗨𝘀𝗲 𝘁𝗵𝗲 𝗿𝗶𝗴𝗵𝘁 𝗱𝗮𝘁𝗮 𝘁𝘆𝗽𝗲 → set() is way faster than list() for lookups. ☉ 𝗨𝘀𝗲 𝘃𝗲𝗰𝘁𝗼𝗿𝗶𝘇𝗲𝗱 𝗼𝗽𝗲𝗿𝗮𝘁𝗶𝗼𝗻𝘀 → NumPy & Pandas process data in bulk, avoiding slow Python loops. ☉ 𝗨𝘀𝗲 𝗴𝗲𝗻𝗲𝗿𝗮𝘁𝗼𝗿𝘀 → Process big data without eating up memory. ☉ 𝗥𝘂𝗻 𝘁𝗮𝘀𝗸𝘀 𝗶𝗻 𝗽𝗮𝗿𝗮𝗹𝗹𝗲𝗹 → Threads for I/O, processes for heavy CPU work. ☉ 𝗙𝗶𝗻𝗱 𝗯𝗼𝘁𝘁𝗹𝗲𝗻𝗲𝗰𝗸𝘀 𝗳𝗶𝗿𝘀𝘁 → Use cProfile before guessing what’s slow. ☉ 𝗖𝘂𝘁 𝘂𝗻𝗻𝗲𝗰𝗲𝘀𝘀𝗮𝗿𝘆 𝗹𝗼𝗼𝗽𝘀 → List comprehensions are faster and cleaner. ☉ 𝗨𝘀𝗲 𝗯𝘂𝗶𝗹𝘁-𝗶𝗻 𝘁𝗼𝗼𝗹𝘀 → Python’s standard library is already optimized. ☉ 𝗖𝗮𝗰𝗵𝗲 𝗿𝗲𝘀𝘂𝗹𝘁𝘀 → Don’t repeat expensive work, store it once. Doc Credits - Abhishek Agrawal ♻️ Repost if you found this useful 🤝 Follow me for more  👨💻 For 1:1 guidance → https://topmate.io/sateesh #python #pyspark #pysparklearning #dataengineering #azuredataengineer #bigdata #spark #datalearning #datacareer #azuredataengineering #dataengineeringjobs #linkedinlearning

Day 38 of my Data Engineering journey 🚀 Today I learned how to work with APIs in Python pulling live data from external systems. 📘 What I learned today (APIs in Python): • What an API is and how it works • Understanding HTTP methods (GET, POST) • Making API requests using requests • Handling JSON responses • Checking status codes • Managing API keys securely • Handling request errors and timeouts • Thinking about data ingestion from external sources APIs are how modern systems talk to each other. In data engineering, APIs are pipelines for live data. This is where Python connects databases to the outside world. Why I’m learning in public: • To stay consistent • To build accountability • To improve daily Day 38 done ✅ Next up: data manipulation with Pandas 💪 #DataEngineering #Python #APIs #LearningInPublic #BigData #CareerGrowth #Consistency

Data Insights: The Essential NumPy Toolkit 📊 Struggling with data manipulation in Python? Look no further than the powerful NumPy library! It's the foundation of data science and machine learning, and mastering these key functions is a game-changer. Here are 7 fundamental NumPy functions every data professional should have checked off their list: np.array(): The cornerstone for creating arrays from Python lists or tuples, enabling efficient numerical operations. np.arange(): Perfect for generating arrays with evenly spaced values within a defined interval (step size matters here!). np.linspace(): Ideal for scientific calculations, creating arrays with a specified number of linearly spaced values between a start and stop point (endpoints included). np.mean(): Quickly calculates the average of array elements, a crucial statistical function for initial analysis. np.sum(): Easily determines the total sum of array elements, whether for an entire array or specific axes. np.reshape(): A powerful function for changing the dimensions (shape) of an array without altering the data itself. np.random(): Essential for generating random numbers and data, vital for simulations, testing, and initializing machine learning models. These functions help you write faster, more memory-efficient code and effectively handle large datasets. #DataScience #Python #NumPy #DataAnalytics #MachineLearning #CodingTips #DataAnalysis #Programming# Abhishek kumar # Harsh Chalisgaonkar # SkillCircle™

🌱 Day 1 of My Python for Data Science Journey 🌱 Today marks my first step into Python for Data Science, and I’m genuinely excited to share what I learned! 🚀 I started with one of the most important foundations of programming — Python Data Types. Understanding how data is stored and handled is the backbone of writing meaningful code. I learned that Python data types are broadly divided into two categories: 🔹 Primitive Data Types These store single values and are simple, fast, and efficient. 🔢 Numeric Used to work with numbers: Example: x = 10 # Integer y = 3.5 # Float z = 2 + 3j # Complex ✅ Boolean Used for decision-making, representing only two values: Example: a=1 b=2 result =a>b is True ✅Booleans help programs decide what to do next. 🔹 Non-Primitive Data Types These store multiple values and help organize data effectively. 📌 Sequence Types Used to store ordered collections Example: name = "Python" # String numbers = [1, 2, 3, 4] # List points = (10, 20) # Tuple 🧩 Set Stores unique values only: Example: unique_numbers = {1, 2, 3} 🗂 Dictionary Stores data in key–value pairs, making it powerful and readable Example: student = {"name": "Alex", "age": 20} This is just the beginning, but every line of code learned today is a step closer to mastering data-driven thinking 📊🐍 Excited to keep learning, exploring, and growing — one concept at a time! 🚀 #Python #DataScience #Day1Learning #ProgrammingBasics #Learningjourney

Most data analysts don’t quit. They hit a ceiling. SQL gets you comfortable. You can query, aggregate, and visualize. You start feeling capable. Then you realize: You can’t automate. You can’t build tools. You can’t turn insights into systems. That’s not a failure. It’s the signal. Python isn’t replacing SQL. It’s what comes after. SQL teaches you how to ask questions. Python teaches you how to build answers that run on their own. Pipelines. APIs. Data products. That’s the difference between analysis and ownership. Our Python 2.0 Cohort starts March 2026. 20% off for the first 20 learners. 13 slots left. If SQL helped you enter the room, Python helps you build the room. Register here: https://lnkd.in/e3kKWpjd #DataAnalytics #Python #SQL #BlockchainAnalytics #DataCareer #LearnPython #Web3Data

Day 4 was where Python stopped being just “code” and started feeling real.Become 2026 Data analysis Roadmap Free resources https://lnkd.in/dRJpwWvC Before this stage, most beginners only work with values inside the program. But real-world data does not live in variables. It lives in text files, CSVs, and logs. That gap confuses many learners. They know syntax, but they do not know how data actually enters and leaves a program. This is why Strings and Files matter early. String methods teach you how to clean, split, and search messy text. File handling shows you how Python reads and writes real information safely. CSV handling introduces structured data, which is the foundation of analytics, automation, and reporting. This image is part of my step-by-step Python learning series, designed for beginners who want clarity, not shortcuts. Each day focuses on one layer of understanding, so learning feels controlled and practical. In 2026, Python skills are valuable only when they connect to real data workflows. Strong basics make advanced work easier later. — Shivam Saxena https://lnkd.in/dRJpwWvC #Python #PythonLearningSeries #StringsInPython #FileHandling #PythonForBeginners #DataAnalytics #ProgrammingBasics #2026Skills #CareerInData

📌 TOOL 3: Python (Main Tool for Data Science) 🚀 Day 25: Full Data Cleaning Project Today I completed a complete Data Cleaning project using Python 🐍 and it was a real hands-on experience. In real life, data is never clean. It is messy, incomplete, and sometimes totally confusing 😅 That’s why data cleaning is one of the most important steps in Data Science. 🔎 In this project, I worked on: ✅ Handling missing values (fill or drop) ✅ Removing duplicate records ✅ Fixing incorrect data types ✅ Detecting and treating outliers ✅ Renaming and standardizing column names ✅ Formatting date columns properly ✅ Making the dataset ready for analysis I used Pandas for most of the cleaning process and applied logical thinking at every step 🧠 💡 Biggest lesson today: Clean data directly improves accuracy of analysis and machine learning models. Garbage data = Garbage output. This project helped me understand how important preprocessing is before visualization or model building. Small steps daily. Big growth yearly 📈 #Python #DataScience #Pandas #DataCleaning #LearningJourney #Day25 #Consistency Ulhas Narwade (Cloud Messenger☁️📨)