Applying Python to Real Data with Data Science Challenge

REMINDER: Join us for another FREE webinar in April! 🐍Working with data in Python: Data import, export, and cleaning Date/Time: April 29th, 2026 | 1200 Hrs Eastern Time Speaker: Ruslan Klymentiev, Ghent University Cost: FREE for all attendees Register now: https://lnkd.in/eUk9sMXv This practical webinar introduces how to work with common data formats in Python, including text files, CSV, Excel spreadsheets, and JSON. Participants will learn how to import data from local files as well as from online sources using API requests (e.g., publicly available crime datasets). In addition to data import and export, the session covers essential data manipulation techniques using tabular data. This includes renaming columns, adding or removing variables, filtering datasets, replacing values, and merging datasets. By the end of the session, participants will be able to take raw, messy data from multiple sources and transform it into a clean, structured dataset ready for analysis or visualization. No advanced Python experience is required, although familiarity with basic Python concepts will be helpful #crimeanalyst #crimeanalysis #python

🚀 Day 7: Understanding Loops in Python (for & while) 🐍📊 As I continue strengthening my foundation in Python for Data Science, today I explored Loops, an important concept that allows programs to repeat tasks efficiently. Loops are extremely useful when working with large datasets, where performing the same operation repeatedly would otherwise require writing the same code multiple times. 🔹 1️⃣ for Loop A for loop is used when we want to iterate over a sequence, such as a list, range of numbers, or dataset. Example: for i in range(5):  print(i) This loop prints numbers from 0 to 4, executing the code block five times. 🔹 2️⃣ while Loop A while loop runs as long as a condition remains True. Example: count = 0 while count < 5:  print(count)  count += 1 This loop keeps running until the condition becomes False. 🔹 Why Loops Matter in Data Science Loops are widely used for: ✔ Iterating through datasets ✔ Automating repetitive calculations ✔ Data preprocessing and cleaning ✔ Applying transformations to multiple records 📌 Today's Key Takeaway Loops help automate repetitive tasks, making Python programs more efficient and scalable, especially when working with large amounts of data. 🙏 Special thanks to my mentor Nallagoni Omkar Sir 🙏 for guiding me and helping me build a strong foundation in Python for Data Science. 🔜 Next Topic: Working with Lists and List Comprehensions in Python #Python #DataScience #Programming #LearningInPublic #CodingJourney #MachineLearning #StudentOfDataScience #NeverStopLearning #OmkarNallagoni

Make Python Your Best Friend in Data 📊 I’ve been building my skills step by step — from reading datasets to transforming, analyzing, and visualizing data. And one thing I’ve learned is this: 👉 You don’t need to memorize everything. You need to understand and practice consistently. So this is one of the cheat sheet l use. Here’s something I believe: We grow faster when we learn with others, not alone. 💬 Drop a function you recognize from the cheat sheet 💬 Tell me what it does (in your own words) 💬 Or add one function you think every data analyst should know Let’s learn from each other and build stronger foundations together. Because the goal isn’t just to write code It’s to think with data #Python #DataAnalysis #DataEngineering #LearningInPublic #DataScience #TechJourney #Coding

Turn messy data into actionable business insights with Python. Learn how to clean, analyse, visualise and model data using Python in this hands-on course designed for real-world business problems. Ideal for business and data analysts, programmers and executives looking to strengthen their data capabilities. Sign up now to build practical, in-demand Python data skills: https://lnkd.in/e7nFctEZ NUS Computing #LearnPython #PythonTraining #dataanalytics #businessanalytics #machinelearning #datascience

Python for Business Analytics 🧠📊 From raw data to meaningful insights — Python plays a powerful role in transforming complex and unstructured data into clear, actionable information. With its wide range of libraries and tools, Python enables data cleaning, analysis, visualization, and modeling, making it an essential skill in today’s data-driven business world. This mindmap represents how Python connects different aspects of business analytics — from collecting and processing data to generating insights that support smarter decision-making. It highlights how businesses can move from confusion and scattered data to structured analysis and strategic outcomes. Continuously learning and applying Python is not just about coding — it’s about developing the ability to think analytically, solve real-world problems, and create value through data. 📈💻 #python #pythonforbusinessanalytics #businessanalytics

🐍 Top 5 Python List Codes Every Data Scientist Should Know Lists are one of the most commonly used data structures in Python. Simple, flexible, and powerful—they are the foundation of many data operations in real-world projects. If you're learning Data Science, mastering lists is a must. 📌 What you’ll learn: • Creating lists • Accessing elements (indexing) • Adding new items • Removing items • Performing common operations 💡 Strong fundamentals in lists make data handling faster and more efficient. Start with basics, practice consistently, and build real projects. 📌 Save this post for quick revision! #Python #DataScience #Coding #Programming #LearnToCode #DataAnalytics #PythonLists

Hands-on practice in Python Data Analysis using Pandas and NumPy I have been actively practicing Python Data Analysis using Pandas and NumPy to strengthen my foundation in data handling and analysis. 💡 What I learned & practiced: ✔ Creating and structuring datasets using Pandas DataFrames ✔ Exploring data using key Pandas functions (.head(), .tail(), .describe()) ✔ Working with NumPy arrays and Pandas Series for numerical analysis ✔ Data manipulation, transformation, and cleaning basics ✔ Converting data between structured (DataFrame) and numerical (NumPy) formats 🚀 This helped me understand how raw data is processed and analyzed using Python. #Python #Pandas #NumPy #DataAnalysis #MachineLearning #DataScience #Coding

🚀 Project Spotlight: Data Analysis with Python I recently worked on a data analysis project where I explored data using Python libraries. 🧰 Tools I used: ✔ Pandas ✔ NumPy ✔ Matplotlib ✔ Seaborn 📊 Key Highlights: ✅ Cleaned and processed raw data ✅ Performed statistical analysis ✅ Created meaningful visualizations ✅ Identified patterns and trends 💡 This project helped me understand how data can be transformed into insights. 🔗 More projects coming soon on my GitHub! #DataScience #Python #DataAnalysis #Projects #Learning

I used to feel confused about where to start in Python for Data Analytics… 😵💫 So today, I created a clear roadmap for myself 👇 🚀 Day 2 of my Data Analytics Journey Here’s the Python syllabus I’ll be following: 📌 Basics • Variables & Data Types • Loops & Conditions 📌 Data Analysis • NumPy • Pandas (Data Cleaning, EDA) 📌 Visualization • Matplotlib • Seaborn 📌 Advanced (Optional) • Basic Machine Learning 👉 My focus is simple: Learn → Practice → Build Projects No more random tutorials ❌ I’ll be sharing my progress daily here. 💬 If you’re learning Python, what topic are you currently on? #Python #DataAnalytics #LearningInPublic #DataScience #BeginnerJourney

Working on Real World Data Problems Using Pure Python Recently worked on a project focused on handling and analyzing structured data using core Python without relying on libraries like NumPy or Pandas. The goal was to understand the logic from the ground up. Cleaned and structured raw JSON data Built logic for “People You May Know” (mutual connections) Implemented “Pages You Might Like” recommendations Focused on problem-solving using basic data structures This approach helped me strengthen my core data handling and logical thinking, rather than depending on pre-built tools. Late nights after work, but worth it for the growth. #Python #DataProcessing #DataScience #ProblemSolving #CorePython #Algorithms #NumPy #pandas