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Applied Data Science with Python

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Applied Data Science with Python


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.

Topics:


    Applied Data Science and Business Analytics

    Common Data Science algorithms for supervised and unsupervised machine learning

    NumPy, pandas, Matplotlib,  scikit-learn

    Python REPLs

    Jupyter notebooks

    Data analytics life-cycle phases

    Data repairing and normalizing

    Data aggregation and grouping

    Data visualization



Course Outline

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 Exercises

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


VIEW FULL COURSE OUTLINE



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