Introduction to Python and Data Analysis
This course is an introduction to Python and its main data analysis libraries, Pandas and Matplotlib for delegates with some understanding of programming concepts. It is a two-part course, the first is an introduction to Python programming, the second introduces Python's data analysis tools. For the programming environment we use JupyterLab on the Anaconda platform. Anaconda is one of the most, if not the most, popular Data Science platforms.
Description
Introduction to Python and Data Analysis Training Course
Course Introduction
- Administration and Course Materials
- Course Structure and Agenda
- Delegate and Trainer Introductions
Session 1: INTRODUCTION
- Python as an interpreted language
- Script mode by example
- Interactive mode
- Statements
- Comments
- Whitespace and Indentation
Session 2: PYTHON: VARIABLES & SCALAR TYPES
- Numerical types
- Text
- Boolean
- Variables as references
- The type() function
Session 3: OPERATORS & EXPRESSIONS
- Arithmetic Operators
- Assignment Operators
- Comparison Operators
- Logical Operators
- Membership Operators
Session 4: CONTAINERS
- Lists
- Tuples
- Sets
- Dictionary
Introduction to Python and Data Analysis Training Course
Session 5: CONDITIONS & LOOPS
- Basic if statement
- Else clause
- For loop
- While loop
- The range function
- Iterating over a list
- Break
- Continue
Session 6: FUNCTIONS
- inbuilt functions (len(), sum(), min(), max(), sorted())
- defining functions
- positional arguments
- names arguments
- default value arguments
Session 7: OBJECTS
- What is a Class?
- Data Attributes and Methods
- A simple example
- Some methods of inbuilt containers
Introduction to Python and Data Analysis Training Course
Session 8: INTRODUCTION TO DATAFRAMES
- What is a DataFrame?
- DataFrame attributes
- Loading and writing DataFrames
- Exploratory functions
- Subsetting
- Conditional subsetting
- Adding and dropping columns
- Inbuilt aggregating functions
- Missing values
Introduction to Python and Data Analysis Training Course
Session 9: GROUPBY AND AGGREGATION: SPLIT-APPLY-COMBINE
- Groupby one column and aggregate using single inbuilt function
- Groupby two columns and aggregate using single inbuilt function
- Groupby one column and aggregate using separate function for each column
Session 10: PLOTTING WITH MATPLOTLIB
- Bar chart
- Histogram
- Line plot