๐๐ #DataScienceTip for today: When creating histograms, experiment with different bin widths. Too narrow, and you might miss trends; too wide, and details get lost. Find the sweet spot that uncovers meaningful patterns in your data. ๐๐๏ธโ๐จ๏ธ #DataVisualization#DataAnalysis
6๐ Keep in mind, data cleaning isn't a one-time task. It's an iterative process, so stay patient and persistent. You're on your way to becoming a data wizard! โจ #DataScienceBeginner#DataCleaning101
Welcome to #DataScience, beginners! ๐ Let's start with a fundamental concept: Data Cleaning. ๐งน๐
1 ๐ง Data Cleaning is like tidying up your room before starting a project. Remove duplicates, handle missing values, and fix errors. Clean data is crucial for accurate analysis.
๐ Just starting your journey in #DataScience? ๐ Don't be afraid to dive in! Embrace the data, learn, and explore. ๐๐ก Mistakes are your stepping stones to mastery. ๐ช Keep that curiosity alive! ๐ #DataScienceBeginner#LearningDataScience#DataAnalytics
The #1 tip any successful Kaggler will give you, is to study winning solutions of past competitions.
Thatโs we will analyze and summarize winning approaches for a lessons we can apply to our own projects in my new article series.
Introducing โthe Kaggle Blueprintsโ series:
Hiring core of ml/data/engineering team for our new co (w/ @antgoldbloom). DM if interested
Goal: Apply AI to build high quality data repo of world's knowledge. Make this repo delightful to consume through APIs, UIs, DB integrations & intelligent joins to your internal data
@sundaskhalid6 It's the price world is paying for efficient vaccine, it really worked we are out of the pandemic sooner than thought off.
Govt. Panicked gave away money ..result in
Mass Hiring by companies thought n miscalculated the transition period of there technology.