Executive Development Programme in Batch Processing Automation for Data Scientists

This course is tailored for data scientists seeking to enhance their skills in automating batch processing workflows. Participants will gain expertise in using advanced tools and technologies to streamline data processing, improve efficiency, and enhance the scalability of their projects.

By the end of the program, attendees will be proficient in designing, implementing, and optimizing batch processing systems, enabling them to handle large datasets more effectively and integrate automation into their data science projects.

1. 1. Introduction to Batch Processing Automation: Learners will understand the basics of batch processing and its importance in data science workflows. They will gain foundational knowledge on setting up batch processing environments and scripting basics.
2. 2. Data Preparation for Batch Processing: This module covers data cleaning, transformation, and validation techniques essential for preparing data for batch processing. Learners will practice using data munging tools and scripts to handle large datasets efficiently.
3. 3. Automating Data Processing with Shell Scripts: Learners will learn how to write and execute shell scripts for automating data processing tasks. They will gain hands-on experience in using shell commands and scripting languages like Bash to manage data pipelines.
4. 4. Python for Batch Processing: This module focuses on using Python for batch processing tasks. Learners will learn to write Python scripts for data manipulation, analysis, and automation, using libraries like pandas and NumPy.
5. 5. Advanced Python for Data Science: Building on Python basics, learners will delve into advanced Python features for data science, including object-oriented programming, functional programming, and more complex data structures.
6. 6. Batch Processing with Apache Spark: This module introduces learners to Apache Spark, a powerful framework for processing large datasets. They will learn to use Spark for data processing, machine learning, and stream processing.
7. 7. Stream Processing and Real-time Data Processing: Learners will explore stream processing systems like Apache Kafka and Flink. They will gain skills in real-time data processing and handling large-scale, high-throughput data streams.
8. 8. Advanced Topics in Batch Processing: This module covers advanced topics such as data parallelism, distributed computing, and system design for batch processing environments. Learners will understand scalability and performance optimization strategies.
9. 9. Implementing Batch Processing Pipelines: Learners will design and implement end-to-end batch processing pipelines, integrating various tools and technologies learned throughout the programme. They will gain experience in managing data flow and ensuring data integrity.
10. 10. Best Practices and Case Studies in Batch Processing: This module focuses on best practices for batch processing, including data security, compliance, and best practices for maintaining and scaling batch processing systems. Case studies will provide real-world examples and insights.