There is a stark difference between large data and big data. Using Pandas with large data could help you explore another useful feature in Pandas to deal with large data by reducing memory usage and ultimately improving computational efficiency. Typically, Pandas has most of the features that we need for data wrangling and analysis. Pandas is seriously a game changer when it comes to cleaning, transforming, manipulating and analyzing data. In simple terms, Pandas helps to clean the mess.
Do you want to become a data scientist? You’re a self-motivated person who is very passionate about data science and bringing values to companies by solving complex problems. Great. But you have ZERO experience in data science and have no clue how to get started in this field. That’s why this post is dedicated to you — enthusiastic and aspiring data scientists — to answer the most common questions and challenges faced by most people.
Whenever we talk about data science, chances are we’ll first think of those fancy stuff like AI, deep learning, machine learning, etc. But nobody talks about documentation. Documentation is often not one of the most interesting things for data scientists. But still, it’s importance is no less than other data science workflow, especially in terms of data science project management. In fact, documentation is no longer just the task done by programmers or developers. It’s something that we as a data scientist should know and be able to perform this task only a regular basis.