Text Analytics, NLP & Social Media Analytics

Learn to analyze unstructured data for pattern finding, sentiment analysis and knowledge discovery. Based on industry use-cases by Experfy in Harvard Innovation Lab.

Track fact market
Big Data annual spending to reach $48.6 billion by 2019

International Data Corporation

Track fact sallary
Average salary for a NLP Data Scientist is $121,000

Indeed Salary Search

Track fact job
190K data scientist jobs by 2018

McKinsey Global Institute

Text Analytics and NLP Training Track

With the emergence of platforms that mainly rely on user-generated content, text data that contains valuable information about people's opinions and relationships became available to the public. Similarly, along with the digital revolution, surveys are now presented to a wider range of users, and taken in mobile devices. Our main medium for communication has become messaging applications or email. Medical records, research papers, newspaper articles are being created and stored in digital mediums. 

All of these developments present businesses or individuals unique resources of information. Learning how to tap into these resources require a unique set of skills, and the methods used in this type of information-gathering is referred to as Text Analytics.

Businesses use this data to analyze customer and competitive data, pharmaceutical industry analyzes patents and research papers to increase the rate of new drug discovery, in academia, mining vast amounts of research papers increase the rate of knowledge gathering. 

Text analytics is defined as the proces of extracting high quality information from text. Information is modeled and extracted using either linguistic, statistical, or machine learning techniques. The process consists of four main steps: 

  • Information Retrieval: In this stage, sophisticated keyword searches help the researcher retrieve relevant documents. 
  • Linguistic Analysis/Entity Recognition: This step converts the unstructured text data into structured data computers can work with. Frequencies of words appearing in a document can be normalized and put into entries of a vector for example. 
  • Information Extraction: This is the process of identifying bits and pieces of useful information in each individual document. 
  • Knowledge Discovery: This last step takes all of the bits and pieces of useful information in each document, and analyzes this collection of data to see if there are any underlying patterns that can lead to new information. 

By taking Experfy's courses on Text Analytics and NLP, you will make sure that you're gaining a skill set that you can use to solve real-life, industry-specific problems. Whether it's analyzing open-ended survey responses, automatic processing of emails or messages, or investigating insurance claims, our courses will not only provide information about different methodologies, but they will also make sure that you understand how these methods are applied in real life. 

According to Indeed, data scientists who possess NLP skills have an average annual salary of $138,000. 

All Courses of this Track

Introduction to Python

By: Veysel Kocaman

Learn the most popular programming language of Data Science community


Natural Language Processing for Retail

By: Hadi Harb

Text classification: sentiment analysis and dialog act classification


Marketing Analytics: Text Analysis & Recommendation Systems

By: Hadi Harb

Techniques for: Information Retrieval, Classification, Clustering & Recommenders


Chinese Natural Language Processing in Practice

By: Jacky Ma

Understand machine learning techniques that are specifically applied to Chinese language


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