Technology & Engineering

Data Scientist

Turns an organisation's raw records into findings people can act on, assembling the datasets and pipelines first, testing them for the statistical patterns that hold up, and finishing with a dashboard and a plain answer.

Level: Associate
Requirements: 7
Courses: 35

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Requirements

What the role asks for

Each skill is earned by completing a short list of verifying courses. Set the role as your target in the app and it orders the work for you.

01
Data Analysis Workflows & Tools
Covers designing, implementing, and optimizing end-to-end data analysis processes using specialized tools and platforms, including data ingestion, transformation, visualization, and collaboration within analytics and business intelligence environments.
Any 3 of 5 courses
02
Data Ingestion & Transformation (ETL)
Covers designing, implementing, and optimizing ETL pipelines to extract data from diverse sources, transform it for quality and consistency, and load it efficiently into target data stores for analytics or operational use.
Any 3 of 5 courses
03
Data Visualization Tools & Techniques
Covers the use of specialized software and libraries to create, customize, and interpret visual representations of data, including charts, dashboards, and interactive graphics, for effective data analysis and communication.
Any 3 of 5 courses
04
Data Warehousing & Big Data
Covers designing, building, and managing large-scale data storage systems, including data warehouses and big data platforms, for efficient data integration, processing, and analytics in enterprise environments.
Any 3 of 5 courses
05
Database Technologies & Administration
Covers the design, deployment, configuration, maintenance, and optimization of relational and NoSQL databases, including backup strategies, performance tuning, user management, and security controls across on-premises and cloud environments.
Any 3 of 5 courses
06
Statistical Analysis & Predictive Modeling
Applies statistical techniques and machine learning algorithms to analyze data, identify trends, and build predictive models for forecasting outcomes and supporting data-driven decision-making in technology projects.
Any 3 of 5 courses
07
Supervised & Unsupervised Learning
Covers the principles and practical techniques of supervised and unsupervised machine learning, including model selection, training, evaluation, and clustering, with applications in data classification, pattern recognition, and predictive analytics.
Any 3 of 5 courses