Time Series Analysis allows us to analyze data which is generated over a period of time and has sequential interdependencies between the observations. The Applied Time Series Analysis course is a lab-intensive, hands-on class that explores special mathematical tricks and techniques which are geared towards exploring the internal structures of time series data and generating powerful descriptive and predictive insights. The course is full of real-life examples of time series and their analyses using cutting-edge solutions developed in Python.
Throughout the course, students will learn descriptive analysis to create insightful visualizations of internal structures such as trend, seasonality and autocorrelation, as well as statistical methods of dealing with autocorrelation and non-stationary time series. The course also covers using exponential smoothing to produce meaningful insights from noisy time series data. Later in the course, the focus will shift focus towards predictive analysis, introducing autoregressive models such as ARMA and ARIMA for time series forecasting. Students will also learn powerful deep learning methods to develop accurate forecasting models for complex time series, and under the availability of little domain knowledge. All the topics are illustrated with real-life problem scenarios and their solutions by best-practice implementations in Python.
Attending students will get their first experience with data analysis with one of the most powerful types of analysis—time-series. They will learn to find patterns in their data and predict the future pattern based on historical data, and learn the statistics, theory, and implementation of Time-series methods using this example-rich guide.
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.
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.
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
- Moving average models
Deep Learning for Time Series Forecasting
- Multi-layer perceptrons
- Recurrent neural networks
- Convolutional neural networks
Getting Started with Python
- Basic Data Types
- Keywords and functions
- Iterators, iterables, and generators
- Classes and objects
TSD Overview Project
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.