George Seif is a Machine Learning Engineer and passionate technologist. He's driven to bringing the most cutting edge technologies to life by building real-world, applicable products. He classifies himself as a Certified Nerd.
Data Scientists work with tons of data, and many times that data includes natural language text. This guide reviews 7 common techniques with code examples to introduce you the essentials of NLP, so you can begin performing analysis and building models from textual data.
The field of Data Science is growing with new capabilities and reach into every industry. With digital transformations occurring in organizations around the world, 2019 included trends of more companies leveraging more data to make better decisions. Check out these next trends in Data Science expected to take off in 2020.
While Pandas is the library for data processing in Python, it isn't really built for speed. Learn more about the new library, Modin, developed to distribute Pandas' computation to speedup your data prep.
The pandas library offers core functionality when preparing your data using Python. But, many don't go beyond the basics, so learn about these lesser-known advanced methods that will make handling your data easier and cleaner.
How can you keep your machine learning models and data organized so you can collaborate effectively? Discover this new tool set available for better version control designed for the data scientist workflow.
Recommender systems are an important class of machine learning algorithms that offer "relevant" suggestions to users. Categorized as either collaborative filtering or a content-based system, check out how these approaches work along with implementations to follow from example code.
Data Scientists need computing power. Whether you’re processing a big dataset with Pandas or running some computation on a massive matrix with Numpy, you’ll need a powerful machine to get the job done in a reasonable amount of time.
All of the setup for software, networking, security, and libraries is automatically taken care of by the Saturn Cloud system. Data Scientists can then focus on the actual Data Science and not the tedious infrastructure work that falls around it
Looping over Python arrays, lists, or dictionaries, can be slow. Thus, vectorized operations in Numpy are mapped to highly optimized C code, making them much faster than their standard Python counterparts.
A data scientist should know how to effectively use statistics to gain insights from data. Here are five useful and practical statistical concepts that every data scientist must know.
An Introductory Guide to NLP for Data Scientists with 7 Common Techniques
The 4 Hottest Trends in Data Science for 2020
How to Speed up Pandas by 4x with one line of code
5 Useful Statistics Data Scientists Need to Know