Python is an excellent programming language for data science, due to its rich set of data structures, the maturity of its data-focused libraries, and its ability to execute all aspects of a data science pipeline.
This workshop will train you towards becoming a well-rounded data scientist, with a good balance between understanding the data exploration aspect needed for modelling and the engineering part necessary to create machine learning pipelines.
This class is also a recommended preparatory workshop for students planning to enroll in General Assembly’s Data Science course.
Working with real-world examples, you’ll:
Ability to use Python for basic data analysis tasks: read & write data, data cleaning, aggregations, handling missing data and outliers.
Advanced understanding of pandas, to transform datasets for analysis or modelling
Practical knowledge of performance strategies: caching and multiprocessing
Skills to prepare a simple machine learning pipelines Greater preparedness for GA’s Data Science course
Laptop with a working install of python and Jupyter notebook
Understanding of Python’s data structures and control flow
Ability to use Python for basic data manipulation tasks
Please note: This session will not be recorded, however slides will be shared following the class.
Conor is a research mathematician/data scientist with 7 years experience applying statistical machine learning, artificial intelligence, and topological data analysis to large heterogeneous data sets. Conor is adept at communicating data science, machine learning, as well as other high-level mathematical concepts to both technical and non-technical/business audiences. He has diverse industry experience including Sainsbury Bank and Mark and Spencer. He holds a degree in mathematics and physics, and both a Master's and PhD in pure mathematics.
We understand that, sometimes, plans change. If you can no longer make it to a class or workshop, please email us at least 7 days before the scheduled event date. No refunds will be given to cancellations made within a week of the class or workshop.
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