Predicting Disease Outbreaks with Time Series Analysis you own this product

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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Data Science

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liveProject

$27.49

liveProject liveProjects give you the opportunity to learn new skills by completing real-world challenges in your local development environment. Solve practical problems, write working code, and analyze real data—with liveProject, you learn by doing. These self-paced projects also come with full liveBook access to select books for 90 days plus permanent access to other select Manning products. $27.49 $49.99 you save: $22 (45%)

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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.

book resources

When you start your liveProject, you get full access to the following books for 90 days.

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

you will learn

In this liveProject, you will learn predictive data analysis and time series techniques that are easy to transfer to a wide range of predictive modelling.

Using pandas for data manipulation and analysis of COVID-19’s spread
Using NumPy for core scientific computing and data formatting
Visualize case numbers and affected countries/regions with Plotly and Matplotlib/seaborn
Plotting data on an interactive leaflet map
Making interactive Jupyter notebooks
Creating interactive reports with Voila
Univariate Analysis of individual variables
Bivariate Analysis to understand the relationship between different types of cases
Data Visualization to look at the number of cases for the worst affected countries
Time Series Forecasting with ARIMA

features

Self-paced: You choose the schedule and decide how much time to invest as you build your project.
Project roadmap: Each project is divided into several achievable steps.
Get Help: While within the liveProject platform, get help from other participants and our expert mentors.
Compare with others: For each step, compare your deliverable to the solutions by the author and other participants.
book resources: Get full access to select books for 90 days. Permanent access to excerpts from Manning products are also included, as well as references to other resources.