As a novice or seasoned Data Scientist, your work depends on the data, which is rarely perfect. Properly handling the typical issues with data quality and completeness is crucial, and we review how to avoid six of these common scenarios.
We tend to think it's all about the data. However, for real data science projects at real organizations in real life, there are more fundamental aspects to consider to do data science right.
How a single line in the code compromised security on all Apple devices
There are many mistakes a new programmer can make. Today, you will learn the most common mistakes that beginners make, and how you can avoid them.
Mobile Apps plays an essential role in today's business environment. Because, it helps you to optimize your sales, marketing efforts, revenue, and internal business process. Having a mobile app for your business which helps you reach the new set...