Time Series with Python, Short Course | Part time online | Data Camp | United States
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Short Online

Time Series with Python

1 days
Duration
Free
Free
Unknown
Tuition fee
Anytime
Unknown
Apply date
Anytime
Unknown
Start date

About

In this Time Series with Python track at Data Camp, you'll learn how to manipulate time series data using pandas, work with statistical libraries including NumPy and statsmodels to analyze data, and develop your visualization skills using Matplotlib, SciPy, and seaborn.

Overview

Join over 9,780,000 learners and start Time Series with Python today!

You'll then apply your time series skills using real-world data, including financial stock data, UFO sightings, CO2 levels in Maui, monthly candy production in the US, and heartbeat sounds. By the end of this Time Series with Python track at Data Camp, you'll know how to forecast the future using ARIMA class models and generate predictions and insights using machine learning models.

Programme Structure

Courses include:

  • Manipulating Time Series Data in Python 
  • Time Series Analysis in Python 
  • Visualizing Time Series Data in Python 
  • ARIMA Models in Python 
  • Machine Learning for Time Series Data in Python

Key information

Duration

  • Part-time
    • 1 days

Start dates & application deadlines

You can apply for and start this programme anytime.

Language

English

Delivered

Online

Academic requirements

We are not aware of any specific GRE, GMAT or GPA grading score requirements for this programme.

English requirements

We are not aware of any English requirements for this programme.

Tuition Fee

To always see correct tuition fees
  • International

    Free
    Tuition Fee
    Based on the tuition of 0 USD for the full programme during 1 days.
  • National

    Free
    Tuition Fee
    Based on the tuition of 0 USD for the full programme during 1 days.

Basic Access: Free; Premium (for individuals): $12.42 per month billed annually; Teams: $25 per month billed annually; Enterprise: Contact sales for pricing

Funding

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