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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
14 Hours
Testimonials (4)
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
It was a though course as we had to cover a lot in a short time frame. Our trainer knew a lot about the subject and delivered the content to address our requirements. It was lots of content to learn but our trainer was helpful and encouraging. He answered all our questions with good detail and we feel that we learned a lot. Exercises were well prepared and tasks were tailored accordingly to our needs. I enjoyed this course
Bozena Stansfield - New College Durham
Course - Build REST APIs with Python and Flask
Trainer develops training based on participant's pace
