Course details

Data Wrangling with Pandas: Visualizations and Time-Series Data

Data Wrangling with Pandas: Visualizations and Time-Series Data


Overview/Description
Expected Duration
Lesson Objectives
Course Number
Expertise Level



Overview/Description

This 12-video Skillsoft Aspire course uses Python, the preferred programming language for data science, to explore data in Pandas with popular chart types such as the bar graph, histogram, pie chart, and box plot. Discover how to work with time series and string data in data sets. Pandas represents data in a tabular format which makes it easy to perform data manipulation, cleaning, and data exploration, all important parts of any data engineer's toolkit. You will learn how to use Matplotlib, a multiplatform data visualization library built on NumPy, the Python library that is used to work with multidimensional data. Learners will use Panda's features to work with specific kinds of data such as time series data and stream data. This course uses a real-world demonstration using Pandas to analyze stock market returns for Amazon. Finally, you will learn how to make data transformations to clean, format, and transform the data into a useful form for further analysis.



Expected Duration (hours)
1.5

Lesson Objectives

Data Wrangling with Pandas: Visualizations and Time-Series Data

  • Course Overview
  • load and explore the dataset used for visualization
  • plot pie charts, box plots, and scatter plots using Pandas
  • identify and work with time-series data
  • calculate deltas and percentage returns in stock prices
  • define time deltas and date ranges in Pandas
  • clean missing data in mismatched DataFrames
  • identify string data stored in DataFrames
  • perform advanced manipulations on string data
  • change column values by applying functions
  • transform data with user-defined functions
  • transform all columns in a DataFrame
  • recall how to plot visuals and transform column values
  • Course Number:
    it_dsdwppdj_02_enus

    Expertise Level
    Intermediate