Milan Janosov’s Post

If you use data to communicate with others and want to improve your ability to effectively and succinctly share insights and convey meaning, the Data Visualization Academy is essential. Having access to the Academy tutorials, office hours, and LinkedIn group will shift how you approach communicating with data, while also providing step-by-step guides to crafting impressive visualizations! You will not be disappointed.

Pandas Series Part 3: Pandas Data Cleaning Functions Never underestimate Pandas when it comes to data cleaning. With: 1. Df.isnull() - You can tell the missing values within each column or row 2. df.dropna() - you can quickly eliminate null cells 3. df.fillna() - you can replace missing values with any values of your choice 4. df.duplicated - you can tell how many duplicates exist within an attribute 5. df.drop_duplicates() - you can eliminate duplicate values 6. df.rename() - you can rename a column/attribute 7. df.replace() - you can replace/rename values within a cell. #dataanalytics, #data, #Pandas, #dataengineering, #analytics, #businessintelligence

Ever wonder how some companies handle massive datasets with lightning speed? ⚡️ The secret often lies in their file format choice, and Parquet is a game-changer! Its columnar storage isn't just a fancy term; it means better compression and incredibly fast query performance, saving you both time and money. If you're working with big data, understanding Parquet is a must for optimizing your data pipelines. What's been your experience with Parquet files, or what other data formats have you found beneficial? Let me know below! 👇 #BigData #DataEngineering #ApacheParquet #DataAnalytics #DataStorage #Performance

We’ve all written those cryptic groupby + aggregation lines that only make sense to the person who wrote them. But Pandas has a better way: named aggregations. They let you define your summary stats in a way that reads like the report itself. No more guessing what “mean_1” or “agg_2” means. Once I switched to named aggregations, my data summaries stopped feeling like code and began to feel like analytics. Have you ever gone back to a messy groupby block and wondered what on earth you were thinking?

Kicking off Graphtober! 🎃📊 Day 1 Bar Graphs https://lnkd.in/eFAwViws For the month of October, I'm sharing short daily videos on different graph types and how to create them in #DataGraph. Starting with the basics. Bar Graphs - one of the most widely used chart types for visualizing data Follow along all month for tips on data visualization! #Graphtober #DataVisualization #macOS Visual Data Tools, Inc.

This week’s article dives into the visualization layer — exploring Apache Superset and other open-source BI tools like Metabase and Redash. You’ll see how Superset connects to Trino and the Hive Metastore to turn dbt-transformed data into interactive dashboards. It’s been a great experience trying to publish a new post every Sunday — a way to stay consistent, learn deeply, and share the progress of this open-source data platform project as it evolves week by week. We’re getting close to the end of the series (only one part left — scaling, orchestration, and production hardening)… but there’s still a lot to explore 🚀 Read it here: https://lnkd.in/e6sTbxvz

This week isn’t all about building because, let’s be real, it never is. But somewhere in here, I’ll be adding more InfoLobby examples to show how SQL can make data a little smarter (and life a little easier). I’ve been thinking about: • Using SQL to do more than just store data • Automating the boring stuff that eats up hours • Making results clear enough that even us non-tech folks can use them confidently 💬 I’d love to hear from you: What are you using your SQL database for? And if your system could do one more thing for you, what would it be? Now, back to juggling spreadsheets, code, and caffeine.

Clean up titration artifacts before you publish. In DatLab 8, Hide/Show points lets you temporarily exclude affected data segments to display a crisp, trace—without altering your raw data. Learn how: https://buff.ly/NGtkqmr #OroborosInstruments #DatLab #Respirometry #DataAnalysis

#MoreThanBarCharts — Week 1: Bar-in-Bar Still using grouped bars for Actual vs Target? Nest them. Why Bar-in-Bar is better → one glance shows over/under-performance. Made by DataCher (my series on data viz for learners in SG). #DataCher #DataStorytelling #SGDataViz

🗂️ Boost your data lake game! Organize by domain, use Parquet for performance, automate with Alice, and load smart with COPY INTO 🚀📊📁 Build scalable, reliable analytics from day one! #DataEngineering 💡Read the latest blog by Michael Dominick 👉 https://t.ly/6ZL3u