Data Visualization Notes for Beginners

Excited to share my latest Data Science project — Expense Tracker App using Python 📊 This project focuses on analyzing spending patterns, tracking expenses across categories, and generating insights through data visualization. Special thanks to Umesh Yadav for guidance and motivation throughout the process 🙌 🔹 Built using: Python, Pandas, NumPy, Matplotlib 🔹 Features: • Category-wise expense analysis • Monthly spending trends • Data visualization (Pie, Bar, Line charts) • Insight generation for better financial decisions This project helped me strengthen my understanding of data analysis, visualization, and real-world problem solving. 🔗 GitHub Repository: https://lnkd.in/gD3fCgDF #DataScience #Python #DataAnalytics #StudentProject #MachineLearning #FinanceAnalytics #GitHubProjects #EDCIITDelhi

Day 25 – Introduction to Data Visualization with Matplotlib Continuing my data analysis journey Today’s session focused on understanding how to visualize data effectively using Matplotlib, which plays a key role in transforming data into meaningful insights. Topics Covered Introduction to Data Visualization Learned the importance of visualizing data to identify patterns, trends, and insights that are not easily visible in raw data. Matplotlib Basics Got introduced to one of the most widely used Python libraries for creating visualizations. Charts Practiced Line Plot Used to represent trends over time or continuous data. Bar Plot Helpful for comparing different categories and values. Scatter Plot Used to understand relationships and correlations between variables. This session helped me understand how visualization makes data more intuitive and impactful for decision-making. Grateful for the practical learning and continuous support from our mentor Praveen Kalimuthu through the Data Tech Community (TDC). #Day25 #DataVisualization #Matplotlib #Python #DataAnalysis #DataScience #LearningJourney #DataTechCommunity #TDC #FutureDataAnalyst #Charts #HandsOnLearning

Everyone wants to learn data analytics, but most people get stuck before they even start. Too many questions: • Which tool should I learn first? • Do I need coding? • What course is worth it? So nothing happens. Here’s the truth. You don’t need the perfect roadmap. You need a starting point. Start with one tool. Practice with real data. Stay consistent. Clarity comes after action, not before. Most analysts didn’t have everything figured out. They simply started. If you’re stuck, this is your sign. Start today. #DataAnalytics #LearnData #CareerGrowth #Upskill #DataAnalyst #AnalyticsJourney #ExcelTips #SQL #Python #DataScience

One small habit that improved my Data Analytics skills a lot: Working with real datasets instead of only tutorials. Tutorials teach how tools work. Projects teach how problems work. When you work on real data you start facing: • 𝐌𝐢𝐬𝐬𝐢𝐧𝐠 𝐯𝐚𝐥𝐮𝐞𝐬 • 𝐃𝐮𝐩𝐥𝐢𝐜𝐚𝐭𝐞 𝐫𝐨𝐰𝐬 • 𝐂𝐨𝐧𝐟𝐮𝐬𝐢𝐧𝐠 𝐜𝐨𝐥𝐮𝐦𝐧𝐬 • 𝐑𝐞𝐚𝐥 𝐛𝐮𝐬𝐢𝐧𝐞𝐬𝐬 𝐪𝐮𝐞𝐬𝐭𝐢𝐨𝐧𝐬 And that’s where real learning happens. If you’re learning Data Analytics, start building projects early. #dataanalytics #learninginpublic #sql #python #powerbi

*Stop Googling the same syntax every 5 minutes. 🛑 *Transitioning between Excel, Python, and SQL is a daily reality for most Data Analysts. But switching your brain from =VLOOKUP to pd.merge() or JOIN can cause some serious mental lag. I found/created this "Rosetta Stone" for data tasks to keep the workflow seamless. Key takeaways from the guide: ✅ Cleaning: How to handle nulls and duplicates across all three platforms. ✅ Aggregations: Pivot Tables (Excel) vs. GroupBy (Pandas) vs. Group By (SQL). ✅ Time-Savers: Quick date extraction and top N row filtering. If you are constantly switching between spreadsheets and code, bookmark this for your next project. 📌 #DataAnalytics #Python #SQL #Excel #DataScience #DataCleaning #CareerGrowth

🔄 Every real Data Science project follows a lifecycle — not just a Jupyter notebook. From defining business goals → acquiring data → EDA → modeling → evaluation → deployment & monitoring. The part most beginners skip? Business Understanding and MLOps — the two ends that actually determine if your model creates value in production. Which stage do you find most challenging? Drop it in the comments 👇 #DataScience #MachineLearning #MLOps #DataEngineering #Python

📅 Day 73 of #100DaysOfCode — and today the data told a story I didn't expect! Today's focus: data visualization with Matplotlib using real StackOverflow data on programming language popularity from 2008 to 2020. Here's what I worked through today: 🔧 Renamed DataFrame columns using the names parameter in read_csv() for cleaner, more readable data 📅 Converted messy datetime strings into proper pandas datetime objects — a crucial data cleaning step before any time series analysis 🔍 Used groupby() + sum() + idxmax() to identify the most popular programming language of all time by total posts (spoiler: JavaScript 👑) 📊 Filtered DataFrames using boolean indexing to isolate specific languages for visualization 📈 Plotted time series data with Matplotlib — first a single language, then overlaid two languages on the same chart The most compelling insight? The chart says it all: 🔵 Java peaked around 2013-2014 and has been declining ever since 🟠 Python has been on a relentless rise — and by 2020, it's not even close The numbers don't lie. If you're wondering whether to learn Python, the StackOverflow community already voted with their questions. Onward to Day 74! 💪 #Python #Pandas #Matplotlib #DataVisualization #100DaysOfCode #DataScience #ContinuousLearning #MicrosoftFabric

Data analysis is a popular and growing field in the tech world. And this 19-hour course takes you on an in-depth journey, whether you're a beginner or more advanced in your skills. You'll learn about Python, Excel, SQL, Tableau and Power BI & much more. https://lnkd.in/gWNSChwT

If you’re working with Snowflake and want a faster path to analysis, this guide walks through connecting it directly to Plotly Studio. ❄️ It covers: • Establishing a Snowflake connection using account + credential configuration • Using Plotly Studio’s AI agent to generate the underlying SQL + Python • Querying tables and returning results as Pandas DataFrames • Building visualizations on top of live warehouse data No manual pipeline setup is required; the connection layer handles authentication and query execution, so you can focus on exploring datasets and iterating on analysis. 🔗 Full walkthrough: https://lnkd.in/gi8tdYAM 📊Try Plotly Studio for free: https://lnkd.in/gY3E_r3B

Most beginners don’t struggle with Pandas… They struggle with messy data. I recently worked on a simple dataset and noticed: - Column names had extra spaces - Inconsistent formatting - Numbers stored as text And this is where things go wrong. Your analysis is only as good as your data. So I created a short video where I walk through: ✔️ Renaming columns properly ✔️ Standardizing column names (the smart way) ✔️ Fixing incorrect data types ✔️ Converting text into numbers and dates These are small steps, but they make a huge difference in real-world data analysis. If you're learning Python or Data Science, this is something you shouldn’t skip. 📌 Watch the video here: https://lnkd.in/gH5k7VJ4 I’d love to know — What’s one data cleaning problem you’ve faced recently? #Python #Pandas #DataScience #DataAnalysis #MachineLearning #Programming #Analytics