After doing these courses, I feel confident creating professional visualizations and dashboards
Shoebur RahmanProcurement Specialist, Giz Bangladesh
Course Description
If you want to apply your new 'Python for Data Science' skills to real-world financial data, then this course will give you some very valuable tools.
First, you will learn how to get data out of Excel into pandas and back. Then, you will learn how to pull stock prices from various online APIs like
Google or Yahoo! Finance, macro data from the Federal Reserve, and exchange rates from OANDA. Finally, you will learn how to calculate returns for various time horizons,
analyze stock performance by sector for IPOs, and calculate and summarize correlations.
Prerequisites
Curriculum
Course outline
1
Importing stock listing data from Excel
In this chapter, you will learn how to import, clean and combine data from Excel workbook sheets into a pandas DataFrame. You will also practice grouping data, summarizing information for categories, and visualizing the result using subplots and heatmaps.
You will use data on companies listed on the stock exchanges NASDAQ, NYSE, and AMEX with information on company name, stock symbol, last market capitalization and price, sector or industry group, and IPO year. In Chapter 2, you will build on this data to download and analyze stock price history for some of these companies.
- Reading, inspecting, and cleaning data from CSV50 XP
- Import stock listing info from the NASDAQ100 XP
- How to fix the data import?50 XP
- Read data using .read_csv() with adequate parsing arguments100 XP
- Read data from Excel worksheets50 XP
- Load listing info from a single sheet100 XP
- Load listing data from two sheets100 XP
- Combine data from multiple worksheets50 XP
- Load all listing data and iterate over key-value dictionary pairs100 XP
- How many companies are listed on the NYSE and NASDAQ?50 XP
- Automate the loading and combining of data from multiple Excel worksheets100 XP
2
Importing financial data from the web
This chapter introduces online data access to Google Finance and the Federal Reserve Data Service through the `pandas` `DataReader`. You will pull data, perform basic manipulations, combine data series, and visualize the results.
3
Summarizing your data and visualizing the result
In this chapter, you will learn how to capture key characteristics of individual variables in simple metrics. As a result, it will be easier to understand the distribution of the variables in your data set: Which values are central to, or typical of your data? Is your data widely dispersed, or rather narrowly distributed around some mid point? Are there outliers? What does the overall distribution look like?
4
Aggregating and describing your data by category
This chapter introduces the ability to group data by one or more categorical variables, and to calculate and visualize summary statistics for each caategory. In the process, you will learn to compare company statistics for different sectors and IPO vintages, analyze the global income distribution over time, and learn how to create various statistical charts from the seaborn library.
Importing and Managing Financial Data in Python
Course
Complete
