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This Applied Data Science with Python 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 intensive 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.
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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 Aggregation and Grouping
Chapter 7. Data Visualization with matplotlib
Chapter 8. Data Science and ML Algorithms in scikit-learn
Lab 1 - Learning the Lab Environment
Lab 2 - Using Jupyter Notebook
Lab 3 - Repairing and Normalizing Data
Lab 4 - Computing Descriptive Statistics
Lab 5 - Data Grouping and Aggregation
Lab 6 - Data Visualization with matplotlib
Lab 7 - Data Splitting
Lab 8 - k-Nearest Neighbors Algorithm
Lab 9 - The k-means Algorithm
Lab 10 - The Random Forest Algorithm
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