Data Analysis and Visualization with Python

Data Analysis and Visualization with Python


This course is an introduction to Data Analysis & Visualization using Python. In this course you will get to know different libraries like numpy, pandas, matplotlib, seaborn, plotly, folium etc. and see how to use them to analyze a dataset. At the end of the course you will build an "Exploratory Data Analysis" project on a real-world data.

Numerical Computing with Numpy

  • Arrays, vectors, and matrices in Numpy
  • Array operations, slicing, and broadcasting
  • Reading from and writing to CSV files

Exploring Numpy Functions

  • Explore the Numpy documentation website
  • Demonstrate usage 5 numpy array operations
  • Publish a Jupyter notebook with explanations

Analyzing Tabular Data with Pandas

  • Querying, filtering, and sorting data frames
  • Grouping and aggregation for data summarization
  • Merging and joining data from multiple sources

Pandas Data Analysis Practice

  • Query and sort data from data frames
  • Group, merge, and aggregate data frames
  • Fix missing and invalid values in data

Visualization with Matplotlib & Seaborn

  • Basic visualizations with Matplotlib
  • Advanced visualizations with Seaborn
  • Tips for customizing and styling charts

Interactive Visualization with Plotly & Folium

  • Creating interactive graphs with Plotly
  • Markers, 3D charts, and animation
  • Plotting on maps using Folium

Data Analysis & Visualization Practice

  • Querying and filtering pandas data frames
  • Static charts with Matplotlib & Seaborn
  • Interactive charts with Plotly & Folium

Exploratory Data Analysis Case Study

  • Data preparation and cleaning with Pandas
  • Open-ended exploratory analysis & visualization
  • Asking and answering interesting questions

Project 2 - Exploratory Data Analysis

  • Find a large real-world dataset using online sources
  • Clean, process & analyze dataset using Pandas
  • Visualize the data, ask & answer relevant questions

Advanced Data Analysis Techniques

  • Downloading and processing large datasets
  • Categorical data and datatype-specific methods
  • Dataframe concatenation, merging, and joins