data science is, among other things, a language, according to Robert Brunner, a professor in the School of Information Sciences at the University of Illinois. This concept might come as a shock to those who associate data science jobs with numbers alone. Data scientists increasingly work across entire organizations, and communication skills are as important as technical ability. Data science is booming in every industry, as more people and companies are investing their time to better understand this constantly expanding field. The ability to communicate effectively is a key talent differentiator. Whether you pursue a deeper knowledge of data science by learning a specialty, or simply want to gain a smart overview of the field, mastering the right terms will fast-track you to success on your educational and professional journey.
Here the top data science glossary terms to know about in 2020.
AI Chatbots–AI chatbots represent a class of software that is able to simulate a user conversation with a natural language through messaging applications. The main attraction of the technology is that it increases user response rate by being available 24/7 on your website in order to provide better customer satisfaction. Chatbots use machine learning and natural language processing (NLP) to deliver a near human like conversational experience.
AutoML–Automated machine learning or AutoML is the process of automating the end-to-end process of applying machine learning to achieve the goals of data science projects. AutoML is an attempt to make machine learning available to people without strong expertise in the field, although more realistically it is designed to help increase productivity of experienced data scientists by automating many steps in the data science process. Some of the advantages of using AutoML include: (i) increasing productivity by automating repetitive tasks which enables a data scientist to focus more on the problem rather than the models; (ii) automating components of the data pipeline helps to avoid errors that might slip in with manual processes; and (iii) AutoML is a step towards democratizing machine learning by making the power of machine learning accessible to those outside the data science team.
BERT–BERT (Bidirectional Encoder Representations from Transformers) – It was introduced in a recent paper published by researchers at Google AI Language. It has caused disruption in the machine learning community by presenting state-of-the-art results in a wide variety of NLP tasks. BERT’s main technical advance is applying the bidirectional training of Transformer, a popular attention model, to language modeling. This direction is in contrast to prior efforts which examined a sequence of text either from left to right or combined left-to-right and right-to-left training. BERT’s methodology shows that a language model which is bidirectionally trained is able to have a deeper sense of language context and flow than single-direction language models.
Cognitive computing– Cognitive computing is based on self-learning systems that use machine-learning techniques to perform specific, human-like tasks in an intelligent way. The main goal of cognitive computing is to simulate human thought processes using a computerized model. With self-learning algorithms that use pattern recognition and natural language processing, the computer is able to imitate the way the human brain functions.
Data pipeline– Data scientists depend on data pipelines to encapsulate a number of processing steps required to prepare data for machine learning. These steps may include acquiring data sets from various data sources, performing “data prep” operations such as cleansing data and handling missing data and outliers, and also transforming data into a form better suited for machine learning. A data pipeline also includes training or fitting a model and determining its accuracy.
