Predicting Disease Outbreaks with Time Series Analysis

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prerequisites
intermediate Python • basics of NumPy, pandas, and Matplotlib libraries • basics of machine learning and time series
skills learned
time series forecasting with ARIMA • univariate and bivariate analysis • data visualization
Harshit Tyagi
5 weeks · 5-7 hours per week · INTERMEDIATE
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Time series analysis is an essential tool for data forecasting, allowing data analysts to make predictions about the future events and track relationships between data. In this liveProject, you’ll utilize the Python data ecosystem and time series analysis to analyze the spread of the COVID-19 virus in different parts of the globe.

Your goal is to make near-future predictions about virus spread based on your available data. You’ll start with an exploratory data analysis into the types of data you have access to, establishing the kind of questions it can reasonably answer. You’ll then develop an ARIMA model for time-series forecasting. Finally, you’ll develop an interactive Voilà dashboard using ipywidgets that will allow stakeholders to access and understand your analysis.
This project is designed for learning purposes and is not a complete, production-ready application or solution.

project author

Harshit Tyagi
Harshit Tyagi has helped over a thousand students master the fundamentals of programming and data science. In his roles at OpenClassrooms and Coding Ninjas, he leverages his technical expertise to conduct workshops and help students bring their course projects to the finish line. He also has a YouTube channel, where he covers fundamental concepts in data science and Python, interview tips, and more. In addition to focusing on data science education, Harshit has developed data processing algorithms with research scientists at Yale, MIT and UCLA.

prerequisites

The liveProject is for intermediate Python programmers who know the basics of data science. To begin this liveProject, you will need to be familiar with:

TOOLS
  • Basics of NumPy and pandas
  • Basics of Matplotlib/seaborn/Plotly
  • Basics of Jupyter Notebook and ipywidgets
TECHNIQUES
  • Basics of machine learning
  • Familiarity with time series analysis

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