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

      1. Welcome and Course Objectives

        FREE PREVIEW

      2. FAQs: Maximizing Your Success in this Course

        FREE PREVIEW

      1. Learning Objectives

      2. Download Exercise Notebook L01

      3. 1a - Download Anaconda

      4. 1b - Introducing Jupyter Notebook

      5. 1c - Calculations

      6. 1d - Exercise

      7. 1e - Solution

      8. 1f - Dynamic Outputs

      9. 1g - Mathematical Operators

      10. 1h - Text Outputs

      11. 1i - Exercise

      12. 1j - Solution

      13. 1k - Variables

      14. 1l - Exercise

      15. 1m - Solution

      16. 1n - Chapter Review

      1. Learning Objectives

      2. Download Exercise Notebook L02

      3. 2a - Python Object Types

      4. 2b - Exercise

      5. 2c - Solution

      6. 2d - Lists

      7. 2e - Accessing List Objects

      8. 2f - Exercise

      9. 2g - Solution

      10. 2h - Changing List Objects

      11. 2i - More List Functions

      12. 2j - Exercise

      13. 2k - Solution

      14. 2l - Tuples

      15. 2m - Sets

      16. 2n - Using Sets to Remove Duplicates

      17. 2o - Set Operations

      18. 2p - Exercise

      19. 2q - Solution

      20. 2r - Dictionaries

      21. 2s - Accessing Dictionary Items

      22. 2t - Exercise

      23. 2u - Solution

      24. 2v - Dictionary Functions

      25. 2w - Exercise

      26. 2x - Solution

      27. 2y - Chapter Review

      1. Learning Objectives

      2. Download Exercise Notebook L03

      3. 3a - Creating Custom Functions

      4. 3b - Excercise

      5. 3c - Solution

      6. 3d - Adding Arguments

      7. 3e - For Loops

      8. 3f - Exercise

      9. 3g - Solution

      10. 3h - Filling a List with a For Loop

      11. 3i - Exercise

      12. 3j - Solution

      13. 3k - Conditional Logic

      14. 3l - Exercise

      15. 3m - Solution

      16. 3n - Review

      1. Learning Objectives

      2. Download Exercise Notebook L04

      3. 4a - Importing Libraries

      4. 4b - NumPy

      5. 4c - NumPy Arrays

      6. 4d - Multidimensional Arrays

      7. 4e - Exercise

      8. 4f - Solution

      9. 4g - Reshape & Transpose

      10. 4h - Selecting Objects

      11. 4i - Exercise

      12. 4j - Solution

      13. 4k - Array Functions

      14. 4l - Array Calculations

      15. 4m - Exercise

      16. 4n - Solution

      17. 4o - The Random Module

      18. 4p - Setting a Random Seed

      19. 4q - Random Sampling with .choice()

      20. 4r - Generating Sequences

      21. 4s - Exercise

      22. 4t - Solution

      23. 4u - Review

      1. Learning Objectives

      2. Download Exercise Notebook L05

      3. 5a - Pandas

      4. 5b - Importing Data

      5. 5c - Import from .csv

      6. 5d - Get to Know Your DataFrame

      7. 5e - Exercise

      8. 5f - Solution

      9. 5g - Summary Statistics

      10. 5h - Exercise

      11. 5i - Solution

      12. 5j - Series

      13. 5k - Series Functions

      14. 5l - Feature Engineering

      15. 5m - Exercise

      16. 5n - Solution

      17. 5o - Boolean Masks

      18. 5p - Indicator Variables

      19. 5q - Exercise

      20. 5r - Solution

      21. 5s - Segmenting with .groupby()

      22. 5t - Exercise

      23. 5u - Solution

      24. 5v - Review

    About this course

    • $95.00
    • 108 lessons
    • 3.5 hours of video content

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    Python Fundamentals & Applied Machine Learning

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