# Jupyter Notebook/Python
 -  The Jupyter Notebook is an open-source web application that allows you to create and share documents that contain live code, equations, visualizations, and narrative text. 
 -  Jupyter has support for over 40 different programming languages, and Python is one of them. 
 -  Python is a requirement (Python 3.3 or greater, or Python 2.7) for installing the Jupyter Notebook itself.

Data Analysis
     Load data from different sources
     Save data into different formats

  - Understand the data structures:
    > Lists:
      - Ordered, mutable collections of items.
      - Can store heterogeneous data types.
      - Created using square brackets [].
          Example: my_list = [1, "hello", 3.14]

   > Tuples:
      - Ordered, immutable collections of items.
      - Can store heterogeneous data types.
      - Created using parentheses ().
           Example: my_tuple = (1, "world", 2.71)

   > Sets:
     - Unordered, mutable collections of unique items.
     - Used for membership testing and eliminating duplicate entries.
     - Created using curly braces {} or the set() constructor.
           Example: my_set = {1, 2, 3, 2} (will result in {1, 2, 3})

    > Dictionaries:
      - Unordered, mutable collections of key-value pairs.
      - Keys must be unique and immutable; values can be of any type.
      - Created using curly braces {} with key-value pairs separated by colons.
             Example: my_dict = {"name": "Alice", "age": 30}
Data Cleaning in Python:
       > Remove Unwanted Observations: Eliminate duplicates, irrelevant entries or redundant data that add noise.
       > Fix Structural Errors: Standardize data formats and variable types for consistency.
       > Manage Outliers: Detect and handle extreme values that can skew results, either by removal or transformation.
       > Handle Missing Data: Address gaps using imputation, deletion or advanced techniques to maintain accuracy and integrity.

- sort
- filter - conditions; search for string
- slicing - select rows, cols on conditions
- merge data - row, col
- join 
- visualise- bar, pie, presentation, storytelling
- outliers 
- groupby summaries, pivot
- rotate data - long, wide
- statistics - mean, median, mode, std, skewness, kurtosis, correlation, covariance
- data distribution - normal,
- sampling