An Essential Tool for Business and Finance Professionals Looking to Advance Their Careers

Python is a tool to process huge amounts of data and is the foundation of data science and machine learning. This course is suitable for beginners and will upgrade your professional skills and help you develop functional fluency in Python. 

The Python Fundamentals Course

is the ideal next step for those new to programming and interested in furthering their career in the following specialties

  • Investment Banking

  • Sales & Trading

  • Capital Markets

  • Asset Management

  • Treasury Management

  • Corporate Development

Video & Applied Learning

With multiple exercises and examples, you will learn:

  • Installing Anaconda and Jupyter Notebooks for Python
  • Python Data Types (Strings, Booleans, Integers, Lists, Tuples, and Sets)
  • Efficient formatting of outputs
  • Building own custom functions
  • For loops and conditional logic
  • Using the Numpy and Pandas packages
  • Screen with python

    After Completing the Course

    You should expect to be able to:

  • Write and execute basic Python code to perform advanced calculations, generate outputs, create variables, and build own functions.
  • Develop an understanding for data structures, functions, loops, logical operations and other programming best practices.
  • Import and use external packages including NumPy and Pandas.
  • Generate random integers and samples.
  • Build programs to perform exploratory data analysis using basic statistical functions, filtering and grouping techniques.
  • Screen with python

    Course Curriculum

    • 2

      Chapter 1: Introduction to Python

      • Learning Objectives

      • Download Exercise Notebook L01

      • 1a - Download Anaconda

      • 1b - Introducing Jupyter Notebook

      • 1c - Calculations

      • 1d - Exercise

      • 1e - Solution

      • 1f - Dynamic Outputs

      • 1g - Mathematical Operators

      • 1h - Text Outputs

      • 1i - Exercise

      • 1j - Solution

      • 1k - Variables

      • 1l - Exercise

      • 1m - Solution

      • 1n - Chapter Review

    • 3

      Chapter 2: Python Objects

      • Learning Objectives

      • Download Exercise Notebook L02

      • 2a - Python Object Types

      • 2b - Exercise

      • 2c - Solution

      • 2d - Lists

      • 2e - Accessing List Objects

      • 2f - Exercise

      • 2g - Solution

      • 2h - Changing List Objects

      • 2i - More List Functions

      • 2j - Exercise

      • 2k - Solution

      • 2l - Tuples

      • 2m - Sets

      • 2n - Using Sets to Remove Duplicates

      • 2o - Set Operations

      • 2p - Exercise

      • 2q - Solution

      • 2r - Dictionaries

      • 2s - Accessing Dictionary Items

      • 2t - Exercise

      • 2u - Solution

      • 2v - Dictionary Functions

      • 2w - Exercise

      • 2x - Solution

      • 2y - Chapter Review

    • 4

      Chapter 3: Custom Functions, For Loops, Conditional Objects

      • Learning Objectives

      • Download Exercise Notebook L03

      • 3a - Creating Custom Functions

      • 3b - Excercise

      • 3c - Solution

      • 3d - Adding Arguments

      • 3e - For Loops

      • 3f - Exercise

      • 3g - Solution

      • 3h - Filling a List with a For Loop

      • 3i - Exercise

      • 3j - Solution

      • 3k - Conditional Logic

      • 3l - Exercise

      • 3m - Solution

      • 3n - Review

    • 5

      Chapter 4: NumPy

      • Learning Objectives

      • Download Exercise Notebook L04

      • 4a - Importing Libraries

      • 4b - NumPy

      • 4c - NumPy Arrays

      • 4d - Multidimensional Arrays

      • 4e - Exercise

      • 4f - Solution

      • 4g - Reshape & Transpose

      • 4h - Selecting Objects

      • 4i - Exercise

      • 4j - Solution

      • 4k - Array Functions

      • 4l - Array Calculations

      • 4m - Exercise

      • 4n - Solution

      • 4o - The Random Module

      • 4p - Setting a Random Seed

      • 4q - Random Sampling with .choice()

      • 4r - Generating Sequences

      • 4s - Exercise

      • 4t - Solution

      • 4u - Review

    • 6

      Chapter 5: Pandas

      • Learning Objectives

      • Download Exercise Notebook L05

      • 5a - Pandas

      • 5b - Importing Data

      • 5c - Import from .csv

      • 5d - Get to Know Your DataFrame

      • 5e - Exercise

      • 5f - Solution

      • 5g - Summary Statistics

      • 5h - Exercise

      • 5i - Solution

      • 5j - Series

      • 5k - Series Functions

      • 5l - Feature Engineering

      • 5m - Exercise

      • 5n - Solution

      • 5o - Boolean Masks

      • 5p - Indicator Variables

      • 5q - Exercise

      • 5r - Solution

      • 5s - Segmenting with .groupby()

      • 5t - Exercise

      • 5u - Solution

      • 5v - Review

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    Python Fundamentals & Applied Machine Learning

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