Time Series analysis is a statistical method that deals with how data values change over time. The applications of time series are vast ranging from predicting asset prices over time to understanding how weather patterns have changed over time. The key to time series analysis is attempting to understand how a variable changes over specific time periods and intervals.
In this training, participants will obtain an overview of times series models and how to use them to solve real-world problems. The content includes testing and fixed common statistical problems such as serial correlation, visualizing time series data, testing for stationarity, and selecting model parameters. The course ends with a hands-on project that effectively bridges the covered material together into a unified story.
Attendees can use either a Mac or Windows computer for this workshop.
An overview of the ARIMA time series model and how to evaluate model performance
All of our classes use relevant, Python-based exercises to help students apply theory to practice. Students write their own Python code during the workshop
Understanding of how to take very complex problems and break them down into manageable pieces
Understanding of effective data visualization techniques, how to execute data visualization tasks in Python, and how to communicate results to key stakeholders
Participants will have opportunities to discuss current machine learning topics with other attendees & their Cognitir instructor and brainstorm practical strategies to improve decision making
All Cognitir participants receive course notes, a certificate of completion, instructions on how to add the course to their LinkedIn profiles, and post-seminar email support
This class is relevant for business and finance professionals who are interested in identifying statistical patterns in their data over time. Nonbusiness professionals in law, medicine, politics, public health, public policy, and sports analytics would also benefit from the content.
Prior Python programming experience is required.
Not sure if this is the right course for you? Contact us.
Yes. You must have prior Python programming experience and a core data science foundation before taking this course. The most efficient way to obtain both is to enroll in our Introduction to Data Science course.How is this course different from Introduction to Data Science?
This course is focused purely on the creation and evaluation of time series models. Classification, clustering, and other models discussed in Introduction to Data Science are not covered in this course.How is this course different from Machine Learning Classification?
This course is focused purely on the creation and evaluation of time series models. Classification, clustering, and other models discussed in Introduction to Data Science are not covered in this course.Do I have the right laptop for this course?
You will need a PC (Windows Vista or newer), Mac (OS X 10.7 or newer), or Linux (Ubuntu, RedHat and others; CentOS 5+). Please contact us if you are planning to use a different setup for this course.Do I need to install software prior to the class?
Yes, you will need to download and install free Python software on your laptop. In case you are planning on using a company laptop, please make sure that you have the necessary rights to install the tools. We will send out detailed information on the download and installation procedures a few days before the course starts.
Visit our FAQs to see commonly asked questions from participants about data science.