Forecasting Analytics
This course will teach you how to choose an appropriate time series model: fit the model, conduct diagnostics, and use the model for forecasting.
Overview
Complex sample designs such as stratified cluster sampling make it possible to extract maximum information at minimum cost, but they are typically harder to work with than simple random samples. How do you analyze the resulting data – in particular, how do you determine margins of error? This course teaches you how to estimate variances when analyzing survey data from complex samples, and also how to fit linear and logistic regression models to complex sample survey data.
- Intermediate
- 4 Weeks
- Expert Instructor
- Tuiton-Back Guarantee
- 100% Online
- TA Support
Learning Outcomes
This class teaches students how to:
- Visualize time series data
- Understand the different components of time series data
- Distinguish explanation from forecasting
- Specify appropriate metrics to assess forecasting models
- Use smoothing methods with time series data (moving average, exponential smoothing)
- Adjust for seasonality
- Use regression methods for forecasting
- Account for autocorrelation
- Distinguish real trend and patterns from random behavior
Who Should Take This Course
Data Scientists, data analysts, sales forecasters, marketing managers, accountants, economists, financial analysts, risk managers, anyone who needs to produce, interpret or assess forecasts will find this course useful. Participants should be familiar with basic statistics, including linear regression.
Our Instructors
Course Syllabus
Week 1
Characterizing Time Series and the Forecasting Goal; Evaluating Predictive Accuracy and Data Partitioning
- Visualizing time series
- Time series components
- Forecasting vs. explanation
- Performance evaluation
- Naive forecasts
Week 2
Smoothing-based Methods
- Model-driven vs. data-driven methods
- Centered and trailing Moving Average (MA)
- Exponential Smoothing (simple, double, triple)
- De-trending and seasonal adjustment
- Differencing
Week 3
Regression-based Models
- Overview of forecasting methods
- Capturing trend, seasonality and irregular patterns with linear regression
- Measuring and interpreting autocorrelation
- Evaluating predictability and the Random Walk
- Second-layer models using Autoregressive (AR) models
Week 4
Forecasting in Practice
- Forecasting implementation issues (automation, managerial forecast adjustments, and more)
- Communicating forecasts to stakeholders
- Overview of further forecasting methods (neural nets, ARIMA, and logistic regression)
- Forecasting binary outcomes
Prerequisites
The Statistics.com courses have helped me a lot, pushing me to the limit and making me learn much more than I expected I could. The knowledge I gained I could immediately leverage in my job … then eventually led to landing a job in my dream company – Amazon.
Karolis Urbonas
This program has been a life and work game changer for me. Within 2 weeks of taking this class, I was able to produce far more than I ever had before.
Susan Kamp
The material covered in the Analytics for Data Science Certificate will be indispensable in my work. I can’t wait to take other courses. Great work!
Stephen McAllister
I learned more in the past 6 weeks than I did taking a full semester of statistics in college, and 10 weeks of statistics in graduate school. Seriously.
Amir Aminimanizani
This is the best online course I have ever taken. Very well prepared. Covers a lot of real-life problems. Good job, thank you very much!
Elena Rose
The more courses I take at Statistics.com, the more appreciation I have for the smart approach, quality of instructors, assistants, admin and program. Well done!
Leonardo Nagata
This course greatly benefited me because I am interested in working in AI. It has given me solid foundational knowledge…After completing this last course, I feel I have gained valuable skills that will enhance my employability in Data Science, opening up diverse career opportunities.
Richard Jackson
Frequently Asked Questions
-
What is your satisfaction guarantee and how does it work?
-
Can I transfer or withdraw from a course?
-
Who are the instructors at Statistics.com?
Visit our knowledge base and learn more.
Additional Information
Time Requirements
About 15 hours per week, at times of your choosing.
Homework
Homework in this course consists of short answer questions to test concepts, guided data analysis problems using software and guided data modeling problems using software.
In addition to assigned readings, this course also has an end of course data modeling project.
Course Text
“Practical Time Series Forecasting” in eBook or hardcopy, or, if you are using R, “Practical Time Series Forecasting in R.” Those in South Asia can purchase the books online here.
Software
This is a hands-on course, and, while any software capable of doing time series forecasting can be used, assignment support is offered for two programs:
1. XLMiner, a data mining program available either (a) for Windows versions of Excel or (b) over the web. Course participants will have access to a low-cost license for XLMiner.
2. R, a free statistical programming environment.
Be sure to choose the book that corresponds to your chosen software program.
For XLMiner users: Course participants will have receive a low-cost license for XLMiner – this is a special version, for this course. Do NOT download the free trial version of XLMiner from solver.com as it may conflict with the special course version.
[testimonial_view id=”17″ post_ids=”15447″]
Supplemental Information
Literacy, Accessibility, and Dyslexia
At Statistics.com, we aim to provide a learning environment suitable for everyone. To help you get the most out of your learning experience, we have researched and tested several assistance tools. For students with dyslexia, colorblindness, or reading difficulties, we recommend the following web browser add-ons and extensions:
Chrome
- Color Enhancer (for colorblindness)
- HelperBird (for colorblindness, dyslexia, and reading difficulties)
Firefox
- Mobile Dyslexic
- Color Vision Simulation (native accessibility feature)
- Other native accessibility features instructions
Safari
- Navidys (for colorblindness, dyslexia, and reading difficulties)
- HelperBird for Safari (for colorblindness, dyslexia, and reading difficulties)