Python Introduction for Data Science and Machine Learning

Learning Objectives:

• Python Basics

1. Be able to understand general syntax and use primary and secondary data types.
2. Be able to understand and implement general operations.
3. Be able to understand and implement basic programming constructs.
4. Be able to use and write conditional statements.
5. Be able to define and implement loops in python.
6. Be able to implement various list operations: list comprehensions
7. Be able to implement various dictionary operations: dictionary comprehensions
8. Be able to write custom functions.
9. Be able to implement Lambda functions.
10. Be able to understand and use some of the useful functionalities time complexities and debugging.

II. Introduction to Numpy
1. Be able to understand the basic data types.
2. Be able to process multidimensional arrays i.e. indexing, slicing and iterating.
3. Be able to reshape numpy arrays.

III. Introduction to Pandas:
1. Be able to process, analyse and manipulate using Pandas.
2. Be able to create pandas series and Dataframe.
3. Be able to subset data frames using pandas functions.
4. Be able to use pandas functions and methods on data frames to understand data and describe it.
5. Be able to implement exploratory data analysis by applying required data preprocessing functions.
6. Be able to read data from different file formats.
7. Be able to differentiate numerical and categorical features and explicit type conversions if necessary.
8. Be able to use and apply functions for duplicates, missing value and extreme value analysis.
9. Be able to implement explicit data manipulation like standardization discretization and dummification which might be necessary for certain ML algorithms.
10. Be able to implement data aggregations and data merging.
11. Be able to handle date manipulations for data analysis.