Course Outline
Introduction
Overview of Data Mining Concepts
Data Mining Techniques
Finding Association Rules
Matching Entities
Analyzing Networks
Analyzing the Sentiment of Text
Recognizing Named Entities
Implementing Text Summarization
Generating Topic Models
Detecting Data Anomalies
Best Practices
Summary and Conclusion
Requirements
- An understanding of Python programming.
- An understanding of Python libraries in general.
Audience
- Data analysts
- Data scientists
Testimonials (5)
The fact of having more practical exercises using more similar data to what we use in our projects (satellite images in raster format)
Matthieu - CS Group
Course - Scaling Data Analysis with Python and Dask
Very good preparation and expertise of a trainer, perfect communication in English. The course was practical (exercises + sharing examples of use cases)
Monika - Procter & Gamble Polska Sp. z o.o.
Course - Developing APIs with Python and FastAPI
Trainer develops training based on participant's pace
Farris Chua
Course - Data Analysis in Python using Pandas and Numpy
Richard's training style kept it interesting, the real world examples used helped to drive the concepts home.
Jamie Martin-Royle - NBrown Group
Course - From Data to Decision with Big Data and Predictive Analytics
The example and training material were sufficient and made it easy to understand what you are doing.