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Updated in [April 29th, 2023]

This Time Series Forecasting course provides students with the foundational knowledge to build and apply time series forecasting models in a variety of business contexts. Students will learn the key components of time series data and forecasting models, as well as how to use ETS (Error, Trend, Seasonality) and ARIMA (Autoregressive, Integrated, Moving Average) models to make forecasts. Additionally, students will gain the techniques to apply their knowledge in a data analytics program called Alteryx. This course is part of the Business Analyst Nanodegree Program.

[Applications]
Students who have completed the Time Series Forecasting course can apply their knowledge to a variety of business contexts. They can use the ETS and ARIMA models to make forecasts, and use the techniques learned in the course to apply their knowledge in a data analytics program such as Alteryx. Additionally, the course can be used as a foundation for further study in the Business Analyst Nanodegree Program.

[Career Paths]
1. Data Scientist: Data Scientists use their knowledge of time series forecasting to develop predictive models and analyze data to identify trends and patterns. They use their skills to develop strategies to improve business operations and decision-making. Data Scientists are in high demand and the demand is expected to continue to grow as businesses become increasingly reliant on data-driven decisions.

2. Business Analyst: Business Analysts use their knowledge of time series forecasting to develop strategies and solutions to improve business operations. They use their skills to analyze data and identify trends and patterns to inform decision-making. Business Analysts are in high demand and the demand is expected to continue to grow as businesses become increasingly reliant on data-driven decisions.

3. Financial Analyst: Financial Analysts use their knowledge of time series forecasting to develop strategies and solutions to improve financial performance. They use their skills to analyze data and identify trends and patterns to inform decision-making. Financial Analysts are in high demand and the demand is expected to continue to grow as businesses become increasingly reliant on data-driven decisions.

4. Data Engineer: Data Engineers use their knowledge of time series forecasting to develop data pipelines and systems to store and process data. They use their skills to develop strategies to improve data collection, storage, and analysis. Data Engineers are in high demand and the demand is expected to continue to grow as businesses become increasingly reliant on data-driven decisions.

[Education Paths]
1. Bachelor of Science in Data Science: A Bachelor of Science in Data Science is a degree program that focuses on the application of data science principles and techniques to solve real-world problems. This degree program typically includes courses in mathematics, statistics, computer science, and data analysis. Students will learn how to use data to make decisions, develop predictive models, and create visualizations. This degree is becoming increasingly popular as businesses and organizations recognize the value of data-driven decision making.

2. Master of Science in Business Analytics: A Master of Science in Business Analytics is a degree program that focuses on the application of data science principles and techniques to business problems. This degree program typically includes courses in data mining, machine learning, predictive analytics, and data visualization. Students will learn how to use data to make decisions, develop predictive models, and create visualizations. This degree is becoming increasingly popular as businesses and organizations recognize the value of data-driven decision making.

3. Master of Science in Artificial Intelligence: A Master of Science in Artificial Intelligence is a degree program that focuses on the application of artificial intelligence principles and techniques to solve real-world problems. This degree program typically includes courses in machine learning, natural language processing, computer vision, and robotics. Students will learn how to use AI to make decisions, develop predictive models, and create visualizations. This degree is becoming increasingly popular as businesses and organizations recognize the value of AI-driven decision making.

4. Doctor of Philosophy in Data Science: A Doctor of Philosophy in Data Science is a degree program that focuses on the application of data science principles and techniques to solve real-world problems. This degree program typically includes courses in mathematics, statistics, computer science, and data analysis. Students will learn how to use data to make decisions, develop predictive models, and create visualizations. This degree is becoming increasingly popular as businesses and organizations recognize the value of data-driven decision making.