Six Mistakes to Avoid as a Data Science Professional
Whether you’re an amateur or an established data scientist within the data science industry, you'll find some awful practices which are often overlooked. At times, these practices could take a data scientist’s career for a toss. Failure may be a detour; not a blind alley. However, the thought here is to assist you to identify those mistakes and the way you'll avoid them. Let’s revisit the mistakes a data scientist may often fail to deal with. Below is that the following list you would like to stay in mind while taking over any data science projects. 1: specialize in USING THE RELEVANT DATASET Most often, a data science professional tends to use the whole dataset while performing on a data science project. make sure you don't make this error. the whole dataset may need several implications like missing values, redundant features, and outliers. You wouldn’t want to urge caught breaking your head trying to work out what’s important and what’s not, right? However, if the da