Executive Development Programme in Data Engineering for Machine Learning

This course is tailored for senior executives and data leaders seeking to deepen their understanding of data engineering in the context of machine learning. Participants will gain insights into the strategic aspects of data engineering, including data pipeline design, data governance, and the integration of data engineering practices with machine learning projects to drive business value.

Upon completion, attendees will be equipped with the knowledge to make informed decisions about data engineering investments, understand the technical challenges and solutions in deploying scalable machine learning models, and foster a data-driven culture within their organizations.

1. 1. Introduction to Data Engineering: Learners will understand the basics of data engineering, including data ingestion, storage, and management. They will gain practical skills in setting up data pipelines and working with data lakes.
2. 2. Data Cleansing and Transformation: This module covers techniques for cleaning and transforming raw data into a usable format. Learners will learn to use tools like Apache Spark and Python for data manipulation and gain hands-on experience in preparing data for machine learning.
3. 3. Data Storage Solutions: Learners will explore various data storage technologies, including relational and NoSQL databases, and learn how to choose the right storage solution for different use cases. Practical skills in setting up and managing these storage solutions will be developed.
4. 4. Big Data Processing with Apache Spark: This module delves into the use of Apache Spark for processing large-scale datasets. Learners will gain expertise in writing Spark jobs, understanding Spark's architecture, and optimizing data processing workflows.
5. 5. Machine Learning Fundamentals: Learners will study the basics of machine learning, including supervised and unsupervised learning, model selection, and evaluation metrics. Practical skills in implementing simple machine learning models will be developed.
6. 6. Advanced Machine Learning Techniques: This module covers more complex machine learning techniques such as deep learning, ensemble methods, and reinforcement learning. Learners will gain the ability to apply these techniques to real-world problems and build advanced predictive models.
7. 7. Data Engineering Best Practices: This module focuses on best practices for data engineering, including data governance, security, and compliance. Learners will learn how to design scalable, secure, and compliant data systems.
8. 8. Data Visualization and Communication: Learners will learn how to effectively visualize data and communicate insights to stakeholders. Practical skills in using tools like Tableau, Power BI, and Python for data visualization will be developed.
9. 9. DevOps for Data Engineering: This module covers DevOps practices specifically tailored for data engineering. Learners will learn to automate data pipeline deployment, testing, and monitoring, and understand the importance of continuous integration and delivery in data engineering.
10. 10. Case Studies and Capstone Project: Learners will work on real-world case studies and a capstone project that integrates all the skills learned throughout the programme. This module aims to provide practical experience in end-to-end data engineering for machine learning projects.