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The financial industry is increasingly adopting Python for general-purpose programming and quantitative analysis, ranging from understanding trading dynamics to risk management systems. This course focuses specifically on introducing Python for financial analysis. Using practical examples, you will learn the fundamentals of Python data structures such as lists and arrays and learn powerful ways to store and manipulate financial data to identify trends.
Welcome to PythonFree
This chapter is an introduction to basics in Python, including how to name variables and various data types in Python.Welcome to Python for Finance!50 xpWhy might you use Python in finance?50 xpRun code vs. submit answer100 xpComments and variables50 xpPrinting output100 xpFinding the average revenue100 xpData types50 xpCreating variables100 xpDetermining types100 xpBooleans in Python100 xpCombining data types100 xp
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
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
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
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.
In the following tracksFinance Fundamentals
Assistant Professor and Data Scientist
Adina is an Assistant Professor in the College of Engineering at Iowa State University (ISU) in Ames, Iowa. She teaches numerical methods, computational biology, and data science courses in the undergraduate and graduate programs. Her research focuses on using computational tools to help address global grand challenges, such as how to best manage our land and water resources. She loves dogs, board games, and using programming to solve problems that help people.
What do other learners have to say?
I've used other sites—Coursera, Udacity, things like that—but DataCamp's been the one that I've stuck with.
Devon Edwards Joseph
Lloyds Banking Group
DataCamp is the top resource I recommend for learning data science.
Harvard Business School
DataCamp is by far my favorite website to learn from.
Decision Science Analytics, USAA