These skills do not affect the main grade but show the breadth of your profile.
AI Coding Assistants
▼
Uses ChatGPT/Claude for writing Python/R analysis scripts and generating data cleaning code. Applies AI assistants to explain statistical concepts and create initial data exploration narratives. Follows team guidelines for validating AI-generated analytical code and interpreting AI-suggested insights.
Independently applies ChatGPT/Claude for complex analytical workflows — generating statistical models, creating automated data quality checks, and drafting analytical reports. Understands trade-offs between AI-generated insights and rigorous statistical validation. Designs effective prompts for exploratory data analysis and hypothesis testing.
Defines ChatGPT/Claude adoption strategy for data analytics teams. Establishes standards for AI-assisted analytical workflows, automated EDA pipelines, and prompt engineering for statistical analysis. Conducts reviews ensuring AI-generated insights meet analytical rigor standards and data governance requirements.
Understands basic GitHub Copilot usage for data analysis: leveraging suggestions for pandas/SQL data manipulation code, using Copilot Chat for statistical method questions, accepting completions for visualization and reporting scripts. Follows team guidelines for validating AI-generated analytical code.
Independently configures GitHub Copilot for analytics workflows: crafts prompts for pandas data manipulation and statistical analysis code, uses Copilot Chat for visualization library guidance, evaluates suggestion quality for analytical correctness. Understands trade-offs between AI-assisted coding speed and data analysis accuracy.
Defines GitHub Copilot strategy for analytics teams: establishes guidelines for AI-assisted data analysis code development, designs validation workflows for AI-generated statistical and visualization code, drives Copilot adoption best practices across data teams.
Algorithms & Data Structures
▼
Understands basic algorithmic concepts for analysis: simple aggregation methods, basic sorting and filtering of data, linear search through results. Follows team guidance on algorithm selection for data processing and report generation.
Independently applies algorithmic thinking in data analysis: selects efficient aggregation methods for large datasets, understands sampling algorithm trade-offs, evaluates sorting and ranking approaches for analytical reports. Analyzes computational complexity of statistical operations on data.
Defines algorithmic standards for analytics team: query optimization practices, statistical algorithm selection criteria, data processing efficiency benchmarks. Conducts reviews of algorithmic approaches in complex analytical reports.
Understands basic data structures for analysis: pandas DataFrames for tabular data, SQL result sets, dictionary lookups for reference data. Applies simple data structures following team patterns for data loading and basic transformation tasks.
Independently selects appropriate data structures for analysis: pandas DataFrames for tabular data, dictionary structures for lookup operations, multi-index for hierarchical data grouping. Understands trade-offs between wide vs long data formats for different analytical operations.
Defines data structure standards for analytics team: analytical data model conventions, notebook data handling patterns, data quality validation structures. Conducts reviews of analytical methodology and data pipeline design. Establishes team guidelines for reproducible data analysis.
API Management
▼
Understands basics of API documentation for data access endpoints and analytics service APIs. Reads and follows existing API docs when querying data platforms and visualization services. Documents simple API usage patterns for data extraction and analysis workflows.
Independently creates API documentation for data access services and analytical pipeline endpoints. Writes integration guides for data extraction APIs with query parameter examples and pagination patterns. Documents data catalog APIs and automated analysis workflow endpoints.
Defines API documentation strategy for data analytics platform services and self-service data access. Establishes design standards for data query API documentation including performance guidelines and governance policies. Coordinates cross-team documentation reviews ensuring data API usability for analyst personas.
Defines organizational API documentation strategy for data analytics infrastructure spanning data catalogs, query services, and analytical pipelines. Designs platform-level documentation enabling self-service data access at organizational scale. Establishes enterprise API governance standards for data services ensuring discoverability and usability for analyst personas.
Application Security
▼
Familiar with key OWASP Top 10 vulnerabilities and understands their impact on analytical applications. Avoids SQL injection when writing database queries.
Applies OWASP recommendations when working with analytical web applications and APIs, validates user input in dashboards. Ensures secure storage of connection credentials.
Defines security policies for analytical applications based on OWASP guidelines, organizes regular audits. Trains the team in secure data and API handling practices.
Follows basic secure coding practices: validates input data, uses parameterized queries, and doesn't store secrets in code. Knows main vulnerability types.
Applies secure by design principles when developing analytical modules, conducts independent code security reviews. Uses security linters and static analysis.
Defines secure coding standards for the analytics team, organizes security reviews and training. Integrates DevSecOps practices into the analytical solution development process.
CI/CD
▼
Understands basic GitHub Actions for data work: running data validation pipelines, reading CI logs for SQL/notebook test failures, understanding workflow configurations for data quality checks. Follows team conventions for analytical code CI automation.
Configures CI/CD pipelines for automatic data validation, test execution, and analytical report deployment. Uses matrix strategies and dependency caching.
Defines CI/CD standards for the analytics team, creates reusable actions and workflow templates. Optimizes build times and ensures reliability of automated processes.
Classical Machine Learning
▼
Understands basic scikit-learn models (Linear Regression, Decision Trees, KMeans) for analytical tasks. Applies train_test_split and cross_val_score to validate hypotheses. Follows team standards for preprocessing with SimpleImputer and LabelEncoder.
Independently applies scikit-learn clustering (DBSCAN, KMeans) and dimensionality reduction (PCA, t-SNE) for exploratory analysis. Understands trade-offs between interpretable models and accuracy for stakeholders. Builds pipelines with feature selection for business metric prediction.
Defines analytics team strategy for applying scikit-learn models to BI and decision-support systems. Establishes standards for model validation, analytical assumptions documentation and reproducible reporting. Conducts reviews ensuring statistical rigor and stakeholder-ready interpretability.
Defines organizational strategy for scikit-learn in enterprise analytics, establishing governance for model-driven decisions across business units. Sets enterprise standards for analytical model lifecycle from prototyping to automated reporting. Mentors leads on self-service ML for stakeholders.
Clean Code & Refactoring
▼
Understands basic code quality principles for SQL queries and analysis scripts. Follows team SQL style guide and notebook organization conventions. Writes simple, readable queries with proper commenting. Accepts code review feedback on analytical code clarity.
Independently applies code quality practices in data analysis. Writes well-structured SQL queries and organized analysis notebooks with clear methodology. Understands trade-offs between ad-hoc exploration and reproducible analysis pipelines. Reviews analytical code for statistical correctness and visualization clarity.
Defines code quality standards for analytics team: SQL style guide enforcement, notebook documentation requirements, reproducibility standards. Conducts reviews of analytical methodology and data pipeline design. Establishes quality gates for production report code.
Cloud Providers
▼
Understands core AWS analytics services — S3, Athena, Glue — and can perform simple operations through the console. Knows basic cloud architecture principles.
Works with AWS services to build analytical pipelines: configures S3 buckets, creates Glue jobs, uses Athena and Redshift for data analysis. Manages IAM roles.
Defines AWS usage strategy for the analytics team, creates reference architecture and standards. Manages cloud resource budget and ensures compliance.
Code Review
▼
Understands basic code review for analytics code: reviewing SQL query changes, checking data transformation correctness, validating analytical methodology in code. Follows team review guidelines for analytical pipeline code quality.
Independently conducts code reviews for analytics: evaluates SQL query optimization, reviews data transformation logic, checks statistical methodology implementation. Provides constructive feedback on analytical code reproducibility and documentation.
Defines code review strategy for analytics team: SQL review standards, data transformation review governance, statistical methodology review practices. Establishes review culture for analytical code reproducibility and correctness.
Concurrency & Parallelism
▼
Understands the concept of asynchronous execution and can use async/await for simple data loading operations from APIs. Knows the difference between synchronous and asynchronous code.
Applies asynchronous programming for parallel data loading from multiple sources, manages concurrency and handles errors. Uses asyncio or aiohttp for I/O optimization.
Defines architectural approaches to asynchronous data processing within the team, selects tools and frameworks. Ensures proper error handling and monitoring of asynchronous processes.
Understands multithreading concepts and can use basic parallel data processing tools — ThreadPoolExecutor or multiprocessing. Knows about race condition issues.
Applies multithreading for parallel data loading and processing from multiple sources, manages thread pools and queues. Properly handles synchronization and deadlocks.
Defines multithreaded data processing standards for the team, selects parallelism tools and frameworks. Reviews code for correctness of concurrent operations.
Containerization
▼
Understands basic containerization concepts and can run ready-made Docker containers for local analytical tool development. Knows basic commands: docker run, pull, and logs for working with images.
Creates custom Dockerfiles for analytical pipelines, configures docker-compose for local environments with databases and BI tools. Can optimize image sizes and manage data volumes.
Defines containerization strategy for the team's analytical infrastructure, reviews Dockerfiles and compose configurations. Optimizes deployment and scaling processes for analytical services.
Git & Workflows
▼
Understands basic Git advanced features for data analysis: branching for parallel analysis tracks, cherry-pick for reusing transformation scripts across projects, stash for switching between analytical tasks. Follows team conventions for versioning notebooks and SQL queries.
Independently uses Git advanced features for analytics work: manages parallel analysis branches with clean merge history, resolves conflicts in shared notebooks and SQL scripts, implements hooks for data validation. Understands trade-offs between merge and rebase for notebook-heavy workflows.
Defines Git strategy for analytics teams: establishes branching standards for collaborative analysis workflows, designs code review processes for shared analytical assets, drives adoption of version control for notebooks and SQL across data teams.
GraphQL
▼
Understands basics of GraphQL as a data querying interface for analytical dashboards. Writes simple queries to retrieve dataset metadata and aggregated metrics. Follows team conventions for accessing data catalog and reporting APIs via GraphQL.
Designs GraphQL schemas for analytical APIs, implements resolvers for data aggregation from multiple sources. Optimizes queries using DataLoader and pagination.
Defines GraphQL API design standards for the analytics team, creates guidelines on naming and schema structure. Organizes schema review and versioning processes.
Integration Testing
▼
Understands integration testing goals and can write simple tests to verify interaction between analytical modules and databases. Knows the difference from unit tests.
Creates integration tests for ETL pipelines using test databases and mock services. Verifies data transformation correctness when multiple components interact.
Defines integration testing standards for the team, creates test infrastructure and shared utilities. Organizes regular integration test execution in CI/CD.
Kubernetes & Orchestration
▼
Understands basic Kubernetes concepts — pods, services, deployments — and can view analytical application status via kubectl. Knows basic commands.
Creates manifests for deploying analytical services in Kubernetes, configures resource limits and health checks. Manages configurations through ConfigMaps and Secrets.
Defines Kubernetes orchestration strategy for analytical services for the team, creates Helm charts and templates. Ensures reliability and performance of cluster resources.
Logging
▼
Understands basic structured logging for data analysis: reading pipeline logs for data quality investigation, understanding log formats for tracking data transformation steps, basic log queries for debugging analytical job failures. Follows team conventions for logging in data processing scripts.
Configures structured logging for data pipelines: implements logging for data transformation tracking and quality metrics, creates dashboards for pipeline execution monitoring, sets up alerts for data quality degradation. Analyzes data processing incidents using log correlation.
Defines observability strategy for data analytics platforms: establishes SLO-based approach for analytical data quality and latency, coordinates data incident management for analytics teams, optimizes MTTD/MTTR for data processing failures.
Metrics & Monitoring
▼
Understands basic Prometheus & Grafana for data pipeline monitoring: reading transformation job dashboards, understanding processing metrics for analytical workload health, navigating Grafana panels for data quality indicators. Follows team conventions for monitoring analytical infrastructure.
Configures Prometheus and Grafana for analytical infrastructure: implements metrics for data transformation job monitoring, creates dashboards for query performance and data quality tracking, sets up alerts for analytical workload anomalies. Analyzes processing incidents using metric-based investigation.
Defines observability strategy for analytics platforms: establishes SLO-based approach for analytical data quality and processing latency, coordinates data processing incident management, optimizes MTTD/MTTR for analytical pipeline failures.
Defines organizational observability strategy for data platforms with Prometheus & Grafana: implements enterprise analytical pipeline monitoring, builds data quality culture across data teams, establishes enterprise SLO framework for analytical data reliability.
Networking
▼
Understands basic network concepts — TCP/IP, DNS, HTTP — and can diagnose simple data source connection issues. Knows API basics.
Understands network architecture of analytical infrastructure, configures connections via VPN and SSH tunnels to remote databases. Diagnoses network performance issues.
Defines network requirements for the team's analytical infrastructure, coordinates with DevOps on network policy setup. Ensures reliability and security of network connections.
OOP & Design Patterns
▼
Knows basic design patterns — Singleton, Factory, Observer — and understands their application in creating analytical tools. Can recognize patterns in existing code.
Applies Strategy, Template Method, and Pipeline patterns for building flexible ETL processes. Uses Repository pattern for abstracting access to various data sources.
Defines a pattern catalog for the analytics team, trains engineers on proper pattern selection and application. Conducts architectural reviews focusing on reusability and extensibility.
Knows basic OOP principles — encapsulation, inheritance, and polymorphism — and applies them when writing simple data processing classes. Understands the difference between classes and objects.
Applies SOLID principles when designing analytical modules and ETL components. Uses inheritance and composition patterns for creating extensible data processing classes.
Defines OOP architectural standards for the team's analytical projects, reviews class design. Trains the team in proper OOP principle application in data engineering contexts.
Prompt Engineering
▼
Leverages AI tools for exploratory data analysis and visualization code generation. Writes prompts that include data schema context and expected output format. Validates AI-generated code before applying to production datasets.
Builds prompt pipelines for automated data quality checks and anomaly detection. Uses few-shot examples with real dataset samples to improve code generation accuracy. Creates prompt libraries for statistical analysis, visualization, and reporting tasks.
Defines prompt engineering strategy for data analytics teams. Establishes best practices for AI-augmented analysis: standardized prompt formats, reproducibility requirements, and validation workflows. Conducts reviews of prompt-based analytics pipelines for accuracy and reliability. Creates training programs on prompt engineering for data professionals.
REST API
▼
Understands basic REST API concepts for data work: fetching datasets from APIs, pagination for large data retrieval, API response parsing for analytical workflows. Follows team conventions for extracting data from external API sources.
Independently works with REST APIs for analytics: designing efficient data retrieval API queries, handling pagination and rate limits, building reusable API client code for data pipelines. Understands best practices for API response caching and data freshness management.
Defines API strategy for analytics data access at product level: data API standards for analytical workflows, API-based data pipeline governance, analytics endpoint performance requirements. Conducts API architecture reviews for data platform integrations.
System Design
▼
Understands basic system design concepts — client-server architecture, REST API, databases. Can draw a simple analytical system diagram.
Designs medium-scale analytical system architecture considering scalability and fault tolerance. Selects appropriate data storage and processing technologies for specific tasks.
Leads analytical system design process for the team, conducts design reviews and defines architectural standards. Balances technical debt against new business requirements.
Unit Testing
▼
Writes simple unit tests for data processing and transformation functions using pytest or unittest. Understands the concept of test fixtures and basic assertions.
Creates comprehensive test suites for analytical pipelines, including parameterized tests and mock objects for external data sources. Maintains code coverage above 70%.
Defines testing standards for the analytics team, implements test quality metrics and coverage thresholds. Organizes code review process with focus on analytical code testability.
Web Frameworks
▼
Uses Python Web Frameworks at a basic level in pandas/Jupyter. Performs simple tasks using ready-made templates. Understands basic concepts and follows team practices.
Independently implements tasks with Python Web Frameworks in pandas/Jupyter. Understands internals and optimizes performance. Writes tests using data validation.
Defines architecture for data exploration platforms and metric APIs using Flask and Streamlit. Establishes standards for query endpoint design, pagination, and data export formats. Conducts design reviews of analytics backends and defines the roadmap for internal data tooling.
Defines company-wide strategy for data exploration and reporting web platforms, evaluating Flask, Streamlit, and Panel. Establishes enterprise standards for metric API architectures, data catalog frontends, and reference patterns for analyst-facing web applications.