Overview

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. Through a structured, end-to-end approach, the course guides learners from understanding time series data to preparing datasets, performing exploratory analysis, and building robust models. You will develop practical skills to test, evaluate, and refine models while handling real-world data challenges. What sets this course apart is its emphasis on combining core time series concepts with distributed computing using Spark. Learners explore how theory translates into scalable, production-ready systems used in industry. This course is ideal for data professionals, engineers, and analysts looking to scale time series workflows using Spark. Prior experience with basic data analysis and some familiarity with programming concepts is recommended.

Syllabus

What Are Time Series

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.

Why Time Series Analysis

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.

Introduction to Apache Spark

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.

End-to-End View of a Time Series Analysis Project

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.

Data Preparation

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.

Exploratory Data Analysis

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.

Building and Testing Models

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.

Going at Scale

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.

Going to Production

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.

Going Further with Apache Spark

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.

Recent Developments in Time Series Analysis

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.