Course description
Introduction to Python
Python has recently become one of the most sought-after skills in finance. This highly practical course will get participants familiar with the Python language, and how it can be applied in finance.
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Upcoming start dates
Suitability - Who should attend?
Who Should Attend
Anyone who needs to learn Python, especially for financial applications.
Prerequisites
It will be useful, though not essential, to have experience of another programming language like JavaScript, C#, or similar.
Outcome / Qualification etc.
CPD: 14 hours
Learning Outcomes
By attending this course, you will:
- Explore the Jupyter user interface
- Understand Python data types and naming conventions
- Gain familiarity with operators, expressions, statements, and flow control
- Learn how to format output and present information neatly
- Work with modules
- Examine data structures like collections, lists, dictionaries, and tuples
- Develop functions and classes for code reusability
- Learn how to gracefully handle errors and exceptions
- Read and write from files
- Explore a range of essential Python libraries, like NumPy, SciPy, Matplotlib, Pandas, and others
- Create a useful finance application
Training Course Content
Introduction
- Introduction to Python
- 💻 Working with the Jupyter Notebook
- Language syntax
- Variables and data types
- Basic functions
- Operators
- 💻 Challenge – factorials
Input, Output, and Formatting
- Input and output
- Formatting numbers and strings
- 💻 Challenge – formatting
Modules
- What are modules?
- How to import modules
- 💻 Challenge – using the math module
Program Flow
- Flow control
- Iteration
- For vs. while
- Conditional statements
- 💻 Challenge – using loops
Collections
- Collections
- Lists
- Tuples
- 💻 Challenge – creating and manipulating lists
- List comprehension
- Dictionaries
- 💻 Challenge – creating a dictionary
Functions
- Built-in functions
- String functions
- Exporting functions
- Lambda functions
- 💻 Challenge – creating an option pricing function
Exception handling
- How to handle exceptions
- Common errors
Classes
- What is a class?
- How to write a class
- 💻 Challenge – creating a class
Working with files
- Reading from a file
- Writing to a file
- 💻 Challenge – analyzing a text file
Python Libraries
- Statistics
- NumPy
- 💻 Challenge – working with the normal distribution
- SciPy
- 💻 Challenge – distribution fitting
- Matplotlib and Seaborn
- Basic plotting
- Formatting charts
- Multiple charts
- 💻 Challenge – data visualization
- NumPy vectorization
- 💻 Challenge – Monte Carlo simulation
- Pandas
- Working with pandas
- 💻 Challenge – analyzing stock data
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Expenses
- London: £1,417.50 (plus VAT)
- New York: $1,755
- Virtual: £1,282.50 (plus VAT)