The Intermediate Google Cloud for Data Analysts training course is designed to advance the skills and knowledge of particpants already familiar wirth data analysis using Google Cloud to the more advanced features and functionality, including predictive, transactional, and large scale distributed data analytics. The course begins using the MapReduce-based, batch data analysis tools available as managed infrastructure services on Google Cloud, including Apache Hive, Apache Pig, and PySpark. Next, participants will use Apache Beam to analyze both batch and streaming data using a single data pipeline. The course concludes by preparing participants to perform data analysis operations commonly used in predictive analytics and machine learning, including feature creation and feature pre-processing.
Prerequisites:
- Experience using Google Cloud for structured data analysis
- Effective use of SQL for data analysis use cases
Purpose
| Learn how to analyze large scale, distributed, and real-time datasets with MapReduce and Apache Beam based capabilities of Google Cloud and practice identification and analysis of effective data features for predictive analytics with BigQuery ML and TensorFlow |
Audience
| Developers using Google Cloud who need to take their MapReduce and Apache Beam capabilities to the next level |
Role
| Business Analyst - Data Engineer | Data Scientist | Software Developer | Technical Manager |
Skill Level
| Intermediate |
Style
| Lecture | Hands-on Activities | Labs |
Duration
| 2 Days |
Related Technologies
| MySQL | BigQuery | Hadoop | Google Cloud | Tensorflow | Apache |
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Learning Objectives
- Employ DataProc to perform MapReduce based data analysis
- Integrate transactional data from a Cloud SQL database in data analysis
- Apply Apache Beam based data analysis pipelines for batch and streaming data
- Support data science and machine learning through analysis of effective data features
- Use Google Colab and Jupyter notebooks for Python based data analysis
