This course covers theoretical and technical aspects of using Python in Applied Data Science projects and Data Logistics use cases.
NumPy, pandas, Matplotlib, scikit-learn; Python REPLs; Jupyter Notebooks; Data analytics life-cycle phases; Data repairing and normalizing; Data aggregation and grouping; Data visualization; Data science algorithms for supervised and unsupervised; Machine Learning.
Virtual Instructor Led Training
Data Scientists, Software Developers, IT Architects, and Technical Managers. Participants should have the general knowledge of statistics and programming and also be familiar with Python.