This course focuses on building scalable time series analysis solutions using Apache Spark, a critical skill for modern data-driven organizations. Learners gain a strong understanding of why time series analysis matters and how it supports forecasting, monitoring, and decision-making at scale.
Time Series Analysis with Spark
Gain insight into a topic and learn the fundamentals.
Intermediate level
Recommended experience
1 week to complete
at 10 hours a week
Flexible schedule
Learn at your own pace
What you'll learn
Master the process of preparing and organizing large-scale time series data for analysis.
Develop and evaluate scalable, production-ready time series models with Apache Spark and Databricks.
Leverage Generative AI and advanced Spark features to enhance predictive analytics and discover new patterns.
Details to know
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Recently updated!
February 2026
Assessments
11 assignments
Taught in English
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There are 11 modules in this course
In this section, we introduce the foundational concepts of time series data, discuss decomposition into trend, seasonality, and residuals, and demonstrate scalable analysis techniques using Apache Spark for real-world applications.
What's included
2 videos5 readings1 assignment
In this section, we examine the importance of time series analysis for forecasting, trend identification, and anomaly detection, applying these techniques to real-world industry cases to improve decision-making and operational efficiency.
What's included
1 video5 readings1 assignment
In this section, we explore Apache Spark's architecture and setup for efficient, scalable time series data analysis. We will learn key concepts for parallel processing and fault tolerance in distributed environments.
What's included
1 video4 readings1 assignment
In this section, we explore the end-to-end process of time series analysis projects using Apache Spark, applying DataOps, ModelOps, and DevOps to build, manage, and deploy robust analytics pipelines.
What's included
1 video7 readings1 assignment
In this section, we demonstrate how to ingest, clean, and transform time series data in Apache Spark, covering data quality checks, normalization, outlier handling, and preparation steps essential for accurate analytics.
What's included
1 video1 reading1 assignment
In this section, we perform exploratory data analysis on time series using Apache Spark, applying statistical analysis, resampling, decomposition, stationarity testing, and correlation metrics to reveal patterns and inform modeling decisions.
What's included
1 video2 readings1 assignment
In this section, we develop and evaluate SARIMA, LightGBM, and NeuralProphet models for time series forecasting, analyzing accuracy, complexity, and interpretability to select optimal approaches under real-world constraints.
What's included
1 video4 readings1 assignment
In this section, we demonstrate how to scale time-series analysis using Apache Spark by implementing distributed feature engineering, parallel hyperparameter tuning, and multi-model training for large datasets in enterprise environments.
What's included
1 video1 reading1 assignment
In this section, we examine how to deploy scalable time series models to production with Spark, emphasizing modular workflows, robust monitoring, and reporting frameworks to ensure operational reliability and actionable ML results.
What's included
1 video5 readings1 assignment
In this section, we learn to implement scalable time series analysis using Databricks, focusing on Delta Live Tables, automated workflows, security, and dashboard design for production use.
What's included
1 video4 readings1 assignment
In this section, we examine recent advances in time series analysis, including generative AI forecasting models, serving results through APIs for real-time use, and making analysis accessible to non-technical users.
What's included
1 video2 readings1 assignment
Instructor
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Frequently asked questions
Yes, you can preview the first video and view the syllabus before you enroll. You must purchase the course to access content not included in the preview.
If you decide to enroll in the course before the session start date, you will have access to all of the lecture videos and readings for the course. You’ll be able to submit assignments once the session starts.
Once you enroll and your session begins, you will have access to all videos and other resources, including reading items and the course discussion forum. You’ll be able to view and submit practice assessments, and complete required graded assignments to earn a grade and a Course Certificate.
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