News
Calendar
Alumni
Giving
Faculty + Staff
Bannerweb
eHelp
Library
Apply

1 800 CALL LTU

info@ltu.edu

LTU ALERT:

Subscribe to LTU’s Alert System

The campus will remain closed until 12 noon Thursday, 02/13/25. Students should log into Canvas for specific class information from their instructors. Please contact event organizers for information on specific activities. Normal operations will resume at 12pm on Thursday.

Big Data Analytics

Credit Hours: 

The Advanced Data Science course for graduate level aims to provide students with a comprehensive understanding of data science strategies and their implementation. This course will focus on time series forecasting techniques and the use of code programming to enhance accuracy. Students will begin by exploring time series features, such as moving average smoothing and trend estimation, using seasonal data. They will also learn how to apply simple statistics using R for data analysis. The course will then delve into the key steps of a tidy forecasting workflow, including defining a model and evaluating its effectiveness using various methods such as mean, naïve, and drift. The course will also cover important topics such as residual diagnostics, judgmental forecasting, and distributional forecasting, which will be applied to a real-world project. Students will be required to complete a data science project to demonstrate their understanding and present their findings to the class. In the next part of the course, students will learn about time series regression models, including the linear model and least squares estimation. They will also explore evaluation techniques and apply them to a project. The course will then move on to ARIMA models, covering topics such as stationarity, differencing, and estimation techniques. Students will also learn about the differences between ARIMA and ETS models. Another project will be assigned to students to apply their knowledge of ARIMA models. In the following section of the course, students will learn about dynamic regression models and how to apply them in projects. They will also explore forecasting hierarchical and grouped time series, using single-level approaches. The final part of the course will cover advanced forecasting methods, such as vector autoregressions and neural network models. Students will learn how to apply these techniques and their strengths and weaknesses. Another project will be assigned to students to showcase their understanding and skills in advanced forecasting methods. By the end of this course, students will have a deep understanding of data science and time series forecasting techniques and will be able to apply them in real-world scenarios. They will also have completed several projects that will help them build a strong portfolio for future endeavors in the field of data science.

» Document Viewer

Use Your Cell Phone as a Document Camera in Zoom

  • What you will need to have and do
  • Download the mobile Zoom app (either App Store or Google Play)
  • Have your phone plugged in
  • Set up video stand phone holder

From Computer

Log in and start your Zoom session with participants

From Phone

  • Start the Zoom session on your phone app (suggest setting your phone to “Do not disturb” since your phone screen will be seen in Zoom)
  • Type in the Meeting ID and Join
  • Do not use phone audio option to avoid feedback
  • Select “share content” and “screen” to share your cell phone’s screen in your Zoom session
  • Select “start broadcast” from Zoom app. The home screen of your cell phone is now being shared with your participants.

To use your cell phone as a makeshift document camera

  • Open (swipe to switch apps) and select the camera app on your phone
  • Start in photo mode and aim the camera at whatever materials you would like to share
  • This is where you will have to position what you want to share to get the best view – but you will see ‘how you are doing’ in the main Zoom session.