Text Analytics with Python

What you will learn

• Construct applications using unstructured data like news articles and tweets.
• Apply machine learning classifiers to categorize documents by content and author.
• Practice using document similarity and topic models to work with large data sets.
• Visualize and interpret text analytics, including statistical significance testing.
• Assess the scientific and ethical foundations of new applications for text analysis.
• Human language technology is an important part of many tech companies like Google and Salesforce; these courses are a first step in exploring this type of role.
• Unstructured data analysis is an increasingly important part of data science and many analysts will benefit from the techniques introduced in these courses.
• Digital media roles often work with text analytics as part of the process of marketing, understanding consumer opinions and behaviours from documents.
• Researchers and journalists are starting to augment their traditional methods using text analytics to work with archives of documents.
• Lawyers and forensic investigators sometimes use text analytics when dealing with large numbers of emails or messages (e.g., patent applications, fraud charges).
• Political and social awareness campaigns use text analytics to target and adjust their message, evaluating which arguments are successful for which audiences.

Program Overview

Learn the core techniques of text analytics and natural language processing (NLP) while discovering the cognitive science that makes it possible in this certificate Text Analytics with Python. On the practical side, you’ll learn how to actually do an analysis in Python: creating pipelines for text classification and text similarity using machine learning. These pipelines are automated workflows that go all the way from data collection to visualization. On the scientific side, you’ll learn what it means to understand language computationally. Artificial intelligence and humans don’t view text documents in the same way. Sometimes deep learning sees patterns that are invisible to us. But often deep learning misses the obvious. We have to understand the limits of a computational approach to language together with the ethical requirements that guide how we choose what data to use and how we protect the privacy of individuals.

Along the way, you’ll explore real-world case studies using pandas, numpy, scikit-learn, tensorflow, matplotlib, seaborn, gensim, and spacy within jupyter notebooks to gain useful insights from unstructured data.

Courses in this program

Text Analytics 1: Introduction to Natural Language Processing

Learn the core techniques of computational linguistics alongside the cognitive science that makes it all possible and the ethics we need to use it properly.

Text Analytics 2: Visualizing Natural Language Processing

Extend your knowledge of the core techniques of computational linguistics by working through case-studies and visualizing their results.

Job Outlook