Data Scientist, Data Analysis Engineer, Data Engineer, BI Consultant, BI Programmer,
System Programmer, System Analyst, Development Manager, Project Manager or
Chief Technology Officer, Chief Innovation Officer.

Day 1 :
     1. Overview    
        • History of Python    
        • Advantages and disadvantages    
        • Downloading and installing    
        • How to find documentation   

     2. Introduction    
        • Using the Python console    
        • Getting help    
        • Learning about the environment    
        • Writing and executing scripts    
        • Saving your work    

     3. Installing Python Packages    
        • Finding resources    
        • Installing resources

     4. Manage Python Environment
        • Using Environment packages
        • Virtual Environment (virtualenv)
        • Anaconda (conda)
        • Using Jupyter Notebook

     5. Data Structures, Variables    
        • Variables and assignment    
        • Data types    
        • Indexing, subsetting    
        • Viewing data and summaries    
        • Naming conventions    
        • Objects   

     6. Getting Data into the Python Environment using Panda   
        • Built-in data    
        • Reading data from structured text files    
        • Reading data using ODBC    

Day 2 :
     7. Control Flow    
        • Truth testing    
        • Branching    
        • Looping    
        • Vectorized calculations    

     8. Functions in Depth    
        • Parameters    
        • Return values    
        • Variable scope    
        • Exception handling    

     9. Handling Dates in Python    
        • Date and date-time classes in Python    
        • Formatting dates for modeling

     10. Descriptive Statistics    
        • Continuous data    
        • Categorical data

     11. Inferential Statistics    
        • Bivariate correlation    
        • T-test and non-parametric equivalents    
        • Chi-squared test    
        • Distribution testing    
        • Power testing

Day 3 :   
     12. Group By Calculations    
        • Split apply combine strategy 

    13. Basic Graphics and Matplotlib  
        • Base graphics system in Python    
        • Scatterplots, histograms, barcharts, box and whiskers, dotplots    
        • Labels, legends, Titles, Axes    
        • Exporting graphics to different formats    

     14. Linear Regression    
        • Linear models    
        • Regression plots    
        • Confounding / Interaction in regression    
        • Scoring new data from models (prediction)   

     15. Conclusion  

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