Introduction to Python for Finance
Build Python skills to elevate your finance career. Learn how to work with lists, arrays and data visualizations to master financial analyses.
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Kursbeschreibung
Learn the Fundamentals of Python for Finance
The financial industry uses Python extensively for quantitative analysis, ranging from understanding trading dynamics to risk management systems. This course will show you how to analyze your financial data by building your Python skills.Manipulate and Visualize Data with Python Packages
The first chapter explains how Python and finance go hand in hand. You will then learn Python basics such as printing output, performing calculations, understanding data types, and creating variables.Next, you’ll cover lists and arrays in Python, exploring how you can use them to work with data. You’ll use the NumPy and Matplotlib packages to manipulate and visualize data.
Perform Financial Analysis Using Python
Finally, you will finish the course by conducting a Python financial analysis on an S&P 100 dataset. Here, you will apply your Python skills to filter lists, summarize sector data, plot P/E ratios in histograms, visualize financial trends, and identify outliers.By the end of the course, you will be confident in your basic Python skills and practical financial analysis skills. These skills are highly rewarded in the finance industry to solve quantitative finance problems. This course is part of our Finance Fundamentals in Python track which is perfect for those who wish to delve deeper into Python for finance.
Trainierst du 2 oder mehr?
Verschaffen Sie Ihrem Team Zugriff auf die vollständige DataCamp-Plattform, einschließlich aller Funktionen.In den folgenden Tracks
Finanzgrundlagen in Python
Gehe zu Track- 1
Welcome to Python
KostenlosThis chapter is an introduction to basics in Python, including how to name variables and various data types in Python.
- 2
Lists
This chapter introduces lists in Python and how they can be used to work with data.
Lists50 xpCreating lists in Python100 xpIndexing list items100 xpSlicing multiple list elements100 xpNested lists50 xpStock up a nested list100 xpSubset a nested list100 xpList methods and functions50 xpExploring list methods and functions100 xpUsing list methods to add data100 xpFinding stock with maximum price100 xp - 3
Arrays in Python
This chapter introduces packages in Python, specifically the NumPy package and how it can be efficiently used to manipulate arrays.
Arrays50 xpCreate an array100 xpElementwise operations on arrays100 xpSubsetting elements from an array100 xp2D arrays and functions50 xpCreating a 2D array100 xpSubsetting 2D arrays100 xpCalculating array stats100 xpGenerating a sequence of numbers100 xpUsing arrays for analysis50 xpWho's above average?100 xpWho's in health care?100 xp - 4
Visualization in Python
In this chapter, you will be introduced to the Matplotlib package for creating line plots, scatter plots, and histograms.
Visualization in Python50 xpImporting matplotlib and pyplot100 xpAdding axis labels and titles100 xpMultiple lines on the same plot100 xpScatterplots100 xpHistograms50 xpWhat are applications of histograms in finance?50 xpIs data normally distributed?100 xpComparing two histograms100 xpAdding a legend100 xp - 5
S&P 100 Case Study
In this chapter, you will get a chance to apply all the techniques you learned in the course on the S&P 100 data.
Trainierst du 2 oder mehr?
Verschaffen Sie Ihrem Team Zugriff auf die vollständige DataCamp-Plattform, einschließlich aller Funktionen.In den folgenden Tracks
Finanzgrundlagen in Python
Gehe zu TrackMitwirkende
Adina Howe
Mehr AnzeigenAssistant Professor and Data Scientist
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