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Python for

Time Series Analysis

Details

PREREQUISITES

Introduction to Python or similar Introduction to Statistics or similar Machine Learning Foundations or similar

LENGTH

2 Days

LOCATION

Live Online

STUDENT PROFILE

Managers and analytical staff with intermediate data skills who are tasked with improving forecasting performance by adding to their tool set.

Course Description

This 2 day course helps improve your company's forecasting operations by teaching employees with intermediate data and analytics skills how to identify and evaluate forecast performance. Addressing possible problems and pitfalls, they’ll discover effective ways to identify, develop, and improve forecast approaches using a wide range of techniques and challenges, including visualizations to communicate results with stakeholders. The course uses Python to explore, visualize, and model time series data—all with an emphasis on practical application. 

Course Outcomes

Upon completion of the course, attendees should be able to:

Define and identify time series applications

Understand modeling concepts including stationarity, autocorrelation, and seasonality

Use Python to implement and assess time series forecasting models

Training Content

MODULE 1:

Time Series Analysis in Python

Pandas for time series data

Approaches to common issues of time series modeling in Python

Implementing standard models

Introduction to packages and tools

Applications, common issues, and solution types

MODULE 2:

Time Series Modeling

Motivation: Case studies with data samples

Transformations and seasonal decomposition

Standard time series approaches in Python

Visualizing time series data in Python

Details

PREREQUISITES

Introduction to Python or similar Introduction to Statistics or similar Machine Learning Foundations or similar

LENGTH

2 Days

LOCATION

Live Online

STUDENT PROFILE

Managers and analytical staff with intermediate data skills who are tasked with improving forecasting performance by adding to their tool set.