Applied Time Series Analysis

Course Objectives

This skills-based course is approximately 50% hands-on, combining expert lecture, real-world demonstrations and group discussions with machine-based practical labs and exercises.  Our engaging instructors and mentors are highly experienced practitioners who bring years of current "on-the-job" experience into every classroom.  

Working in a hands-on learning environment, guided by our expert team, attendees will learn to:

• Understand the basic concepts of Time Series Analysis and appreciate its importance for the success of a data science project
• Develop an understanding of loading, exploring, and visualizing time-series data
• Explore auto-correlation and gain knowledge of statistical techniques to deal with non-stationarity time series
• Take advantage of exponential smoothing to tackle noise in time series data
• Learn how to use auto-regressive models to make predictions using time-series data
• Build predictive models on time series using techniques based on auto-regressive moving averages
• Discover recent advancements in deep learning to build accurate forecasting models for time series
• Gain familiarity with the basics of Python as a powerful yet simple to write programming language

Need different skills or topics?  If your team requires different topics or tools, additional skills or custom approach, this course may be further adjusted to accommodate.  We offer additional python, data science, AI / machine learning and other related topics that may be blended with this course for a track that best suits your needs. Our team will collaborate with you to understand your needs and will target the course to focus on your specific learning objectives and goals.

Course Prerequisites

This course is geared for experienced data analysts, developers, engineers or anyone tasked with utilizing Time Series Analysis with Python. 

Attending students are required to have

• A background in basic Python development skills.
• Basic to Intermediate IT Skills Deep Learning for IoT.
• Good foundational mathematics or logic skill.
• Basic Linux skills, including familiarity with command-line options such as ls, cd, cp, and su

Please see the Related Courses tab for specific Pre-Requisite courses, Related Courses that offer similar skills or topics, and next-step Learning Path recommendations.

Course Agenda

Please note that this list of topics is based on our standard course offering, evolved from typical industry uses and trends. We’ll work with you to tune this course and level of coverage to target the skills you need most.

Introduction to Time Series

• Different types of data
• Internal structures of time series
• Models for time series analysis
• Autocorrelation and Partial autocorrelation

Understanding Time Series Data

• Advanced processing and visualization of time series data
• Resampling time series data
• Stationary processes
• Time series decomposition

Exponential Smoothing based Methods

• Introduction to time-series smoothing
• First order exponential smoothing
• Second order exponential smoothing
• Modeling higher-order exponential smoothing

Auto-Regressive Models

• Auto-regressive models
• Moving average models

Deep Learning for Time Series Forecasting

• Multi-layer perceptrons
• Recurrent neural networks
• Convolutional neural networks

Getting Started with Python

• Installation
• Basic Data Types
• Keywords and functions
• Iterators, iterables, and generators
• Classes and objects

TSD Overview Project

• Course Project

Course Materials

Each student will receive a Student Guide with course notes, code samples, software tutorials, step-by-step written lab instructions, diagrams and related reference materials and links (as applicable). Students will also receive the project files (or code, if applicable) and solutions required for the hands-on work.

Lab Setup Made Simple.   All course labs and solutions, data sets, Tableau course software (limited version, for course use only), detailed courseware, lab guides and resources (as applicable) are provided for attendees in our easy access, no installation required, remote lab environment for the duration of the course. Our tech team will help set up, test and verify lab access for each attendee prior to the course start date, ensuring a smooth start to class and successful hands-on course experience for all participants.