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This intensive training course provides theoretical and practical aspects of using Python in the realm of Data Science, Business Analytics, and Data Logistics. The coverage of the related core concepts, terminology, and theory is provided as well. This training course is supplemented by a variety of hands-on labs (the list of which is provided at the bottom of this outline) that help attendees reinforce their theoretical knowledge of the learned material.
Chapter 1. Python for Data Science
Chapter 2. Applied Data Science
Chapter 3. Data Analytics Life-cycle Phases
Chapter 4. Repairing and Normalizing Data
Chapter 5. Descriptive Statistics Computing Features in Python
Chapter 6. Data Grouping and Aggregation in Python
Chapter 7. Data Visualization with matplotlib
Chapter 8. Data Science and ML Algorithms in scikit-learn
Lab 1. Using Jupyter Notebook Lab 2. Python with NumPy and pandas Lab 3. Repairing and Normalizing Data Lab 4. Data Grouping and Aggregation Lab 5. Data Visualization with matplotlib Lab 6. Data Splitting Lab 7. The k-Nearest Neighbors Algorithm Lab 8. The Random Forest Algorithm Lab 9. The k-Means Algorithm
Participants should have a working knowledge of Python (or have the programming background and/or the ability to quickly pick up Python’s syntax), and be familiar with core statistical concepts (variance, correlation, etc.)
This course is aimed towards Business Analysts, Developers, IT Architects, and Technical Managers.
2 Days Course
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