Data Scientist

Makes sense of data, generates and communicates insights to improve or create business processes, creates predictive ML models to support them. Dig deep into the math, science, and statistics behind machine learning, for learners skilled in math, statistics, and analysis who want to become machine learning (ML) subject matter experts within our organization.

Use machine learning frameworks and analysis tools can improve workplace collaboration. Then supplement skills with training and certification.

Requirements

Services and Terminology Understand both the machine learning stack and the terms and processes that build a good foundation in machine learning. Process Model: CRISP-DM on the AWS Stack Walk through the CRISP-DM methodology and framework and then apply the model's six phases to your daily work. Data Analytics Fundamentals The process for planning data analysis solutions and the various data analytic processes that are involved.

This course takes you through five key factors that indicate the need for specific AWS services in collecting, processing, analyzing, and presenting your data. Machine Learning Data Readiness Focuses on the concept of data readiness in the context of machine learning (ML). Determine data readiness and identify when to employ data readiness as part of your ML process.

Storage Deep Dives Architect and manage highly available solutions, with a focus on AWS storage services. Machine Learning Security Secure applications and environments with specific topics detailing NACLs, security groups, AWS identity and access management, and encryption key management. Big Data on AWS Cloud-based big data solutions such as Amazon Elastic Map.

Reduce (EMR), Amazon Redshift, Amazon Kinesis, and the rest of the AWS big data platform.