Time Series Analysis For Big Data Training Course: Forecasting & Insights

Introduction

Master the art of extracting valuable insights and making accurate predictions with our Time Series Analysis for Big Data Training Course. This program is designed to equip you with the essential skills to analyze and forecast time-dependent Big Data, enabling you to uncover patterns and trends that drive informed decision-making. In today's dynamic business environment, the ability to effectively analyze time series data is crucial for understanding historical trends and predicting future outcomes. Our time series training course provides hands-on experience and expert guidance, empowering you to build robust forecasting models.

This Big Data time series training delves into the core concepts of time series analysis, covering topics such as statistical modeling, forecasting techniques, and model evaluation. You'll gain expertise in using industry-standard tools and techniques to build time series models that handle the complexities of big data. Whether you're a data scientist, analyst, or engineer, this time series analysis course will empower you to leverage the full potential of your time-dependent data.

Target Audience:

• Data Scientists
• Data Analysts
• Machine Learning Engineers
• Big Data Engineers
• Financial Analysts
• Business Intelligence Professionals
• Anyone needing time series analysis skills

Course Objectives:

• Understand the fundamentals of time series analysis.
• Master statistical modeling techniques for time-dependent data.
• Implement various forecasting methods for big data.
• Utilize industry-standard tools and frameworks for time series analysis.
• Develop and evaluate time series models for various applications.
• Optimize time series models for accuracy and performance.
• Deploy time series models for real-world scenarios.
• Troubleshoot and debug time series analysis pipelines.
• Implement data security and access control in time series workflows.
• Integrate time series models with big data platforms.
• Understand how to monitor and maintain time series models.
• Explore advanced time series techniques for large datasets.
• Apply real world use cases for Time Series Analysis in Big Data.

Duration

10 Days

Course content

Module 1: Introduction to Time Series Analysis

• Fundamentals of time series data and analysis.
• Overview of time series components (trend, seasonality, noise).
• Setting up a development environment for time series analysis.
• Introduction to time series tools and libraries.
• Best practices for time series analysis.

Module 2: Statistical Modeling for Time Series Data

• Autoregressive (AR) models.
• Moving average (MA) models.
• Autoregressive moving average (ARMA) models.
• Autoregressive integrated moving average (ARIMA) models. ¹  
• Seasonal ARIMA (SARIMA) models.

Module 3: Forecasting Techniques for Big Data

• Exponential smoothing methods (Simple, Holt, Holt-Winters).
• Decomposition methods (additive, multiplicative).
• Machine learning approaches (regression, tree-based models).
• Deep learning approaches (LSTM, GRU).
• Hybrid forecasting methods.

Module 4: Time Series Tools and Frameworks

• Utilizing Python libraries (Pandas, Statsmodels, Scikit-learn).
• Using R packages (forecast, tseries).
• Implementing time series analysis in Spark.
• Utilizing cloud-based time series services.
• Best practices for tool selection.

Module 5: Model Evaluation and Performance Optimization

• Evaluating model performance using various metrics (RMSE, MAE, MAPE).
• Implementing cross-validation for time series data.
• Optimizing models for accuracy and computational efficiency.
• Handling missing data and outliers.
• Best practices for model evaluation.

Module 6: Feature Engineering for Time Series Data

• Creating lagged features and rolling statistics.
• Utilizing domain knowledge for feature engineering.
• Handling seasonality and trend components.
• Feature selection and transformation techniques.
• Best practices for feature engineering.

Module 7: Model Deployment and Productionization

• Deploying time series models in production environments.
• Utilizing containerization and orchestration tools.
• Implementing API endpoints for time series services.
• Monitoring model performance in production.
• Best practices for model deployment.

Module 8: Troubleshooting and Debugging Time Series Pipelines

• Debugging time series models and pipelines.
• Analyzing model errors and performance issues.
• Utilizing debugging tools and techniques.
• Identifying and resolving model biases.
• Best practices for model troubleshooting.

Module 9: Data Security and Access Control in Time Series

• Implementing data security in time series workflows.
• Utilizing authentication and authorization.
• Implementing data encryption and masking.
• Auditing and compliance in time series.
• Best practices for data security.

Module 10: Integrating Time Series Models with Big Data Platforms

• Integrating time series models with Hadoop and Spark.
• Utilizing cloud-based time series services.
• Implementing real-time time series pipelines.
• Best practices for integration.

Module 11: Model Monitoring and Maintenance

• Monitoring model performance and drift.
• Implementing model retraining and updating.
• Utilizing model monitoring tools and techniques.
• Handling model versioning and rollback.
• Best practices for model maintenance.

Module 12: Advanced Time Series Techniques

• Multivariate time series analysis.
• Dynamic time warping (DTW).
• State space models.
• Bayesian time series analysis.
• Advanced techniques for large-scale time series.

Module 13: Time Series Analysis on Cloud Platforms

• Utilizing cloud-based time series services.
• Deploying time series models on AWS, Azure, and GCP.
• Optimizing cloud resources for time series analysis.
• Best practices for cloud-based time series.

Module 14: Time Series Analysis and Data Governance

• Implementing data governance policies in time series.
• Utilizing metadata management tools.
• Implementing data lineage and data dictionary.
• Best practices for data governance.

Module 15: Future Trends in Time Series Analysis for Big Data

• Emerging trends in time series research and applications.
• Utilizing AI and automation in time series workflows.
• Implementing explainable time series models.
• Best practices for future time series.

Training Approach

This course will be delivered by our skilled trainers who have vast knowledge and experience as expert professionals in the fields. The course is taught in English and through a mix of theory, practical activities, group discussion and case studies. Course manuals and additional training materials will be provided to the participants upon completion of the training.

Tailor-Made Course

This course can also be tailor-made to meet organization requirement. For further inquiries, please contact us on: Email: info@skillsforafrica.org, training@skillsforafrica.org  Tel: +254 702 249 449

Training Venue

The training will be held at our Skills for Africa Training Institute Training Centre. We also offer training for a group at requested location all over the world. The course fee covers the course tuition, training materials, two break refreshments, and buffet lunch.

Visa application, travel expenses, airport transfers, dinners, accommodation, insurance, and other personal expenses are catered by the participant

Certification

Participants will be issued with Skills for Africa Training Institute certificate upon completion of this course.

Airport Pickup and Accommodation

Airport pickup and accommodation is arranged upon request. For booking contact our Training Coordinator through Email: info@skillsforafrica.org, training@skillsforafrica.org  Tel: +254 702 249 449

Terms of Payment: Unless otherwise agreed between the two parties’ payment of the course fee should be done 7 working days before commencement of the training.