Managing Financial Data using Python

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Basic Info : Managing Financial Data using Python
Level : Advanced
Commitment  : 14 hours
Language : English
Scheduled Date

: 25th Feb 2024

What Will I Learn

As a Data Analyst, you would understand the importance of collecting data, analysing it and segregating it accordingly. This might prove to be a very time-consuming task when you’re loaded with work. However, in the financial sector, data management can be made much easier by incorporating Python. Within the click of a button, you can consolidate complex data and save an immense amount of your time. The program on Managing Financial Data using Python can help to interpret and combine data without manually taking any complex steps. With this, students will be able to implement Python to manage financial data. 


Python Lists

  • Introduction to Python!
  • When to use Python?
  • Variables and Types
  • Python Lists
  • Subsetting Lists
  • Manipulating Lists
  • Inner workings of lists


  • Functions
  • Methods
  • Packages
  • Numpy
  • 2D Numpy Arrays
  • Numpy: Basic Statistics

  • Comparison operators
  • Boolean Operators
  • If, Elif, Else
  • Filtering Pandas DataFrame
  • While loop
  • For loop
  • Looping Data Structures, Part 1
  • Looping Data Structures, Part 2

  • Reading, and cleaning up CSV data
  • Importing Stock listing data
  • How to fix and clean up data
  • Read data from Excel 
  • Combining data from multiple worksheets

  • Using Data reader to access financial data online
  • Obtaining stock data for 1 company
  • Using Data reader to get current market prices
  • Obtaining stock data for top companies per industry/sector
  • Comparing performance

  • Summarize your data with descriptive stats
  • List the poorest and richest countries worldwide
  • Global incomes: Central tendency
  • Describe the distribution of your data with quantiles
  • Using Quantile to calculate Global incomes: Dispersion
  • Calculating Deciles of the global income distribution
  • Getting all statistics using Panda's and the .describe method
  • Visualize the distribution of your data
  • Using distplot()
  • Summarize categorical variables

  • Aggregating your data by category
  • Median market capitalization by sector
  • Median market capitalization by IPO year
  • All summary statistics by sector
  • More ways to aggregate your data
  • Company value by exchange and sector
  • Calculate several metrics by sector and exchange
  • Summary statistics by category with seaborn
  • Plot IPO timeline for all exchanges using countplot()
  • Global median per capita income over time - Using Seaborn
  • Calculate several metrics by sector and IPO year - using Seaborn
  • Distributions by category with seaborn
  • Calculate the Inflation trends in China, India, and the US using Seaborn
  • Calculate the Distribution of inflation rates in China, India, and the US using Seaborn

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