AI & Data Scientist Knowledge Assessment

Evaluate your current skills across all domains and get personalized recommendations.

Quick Self-Assessment

Rate yourself honestly (1-5) on each domain:

• 1: No knowledge
• 2: Beginner (heard of it, basic understanding)
• 3: Intermediate (can use with documentation)
• 4: Advanced (comfortable, can teach others)
• 5: Expert (deep understanding, production experience)

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Domain 1: Programming & Software Engineering

Python Programming:

• Basic syntax, data structures, loops
• Object-oriented programming
• Pandas, NumPy for data manipulation
• Writing production-ready, tested code

SQL:

• SELECT queries with WHERE, GROUP BY
• JOINs (INNER, LEFT, RIGHT)
• Window functions, CTEs
• Query optimization

Git/Version Control:

• Basic commands (clone, commit, push, pull)
• Branching and merging
• Collaborating via pull requests
• Data versioning with DVC

Score: __ / 20

Recommendation:

• Score < 10: Start with Programming Foundations Agent
• Score 10-15: Intermediate coding, focus on best practices
• Score > 15: Strong foundation, move to advanced topics

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Domain 2: Mathematics & Statistics

Statistics:

• Descriptive statistics (mean, median, variance)
• Probability distributions (normal, binomial)
• Hypothesis testing (t-test, chi-square)
• A/B testing and experimental design

Linear Algebra:

• Vectors and matrices
• Matrix operations (multiplication, inverse)
• Eigenvalues and eigenvectors
• Applications in ML (PCA, SVD)

Calculus:

• Derivatives and gradients
• Optimization (gradient descent)
• Chain rule for backpropagation
• Multivariable calculus

Score: __ / 20

Recommendation:

• Score < 10: Start with Mathematics & Statistics Agent
• Score 10-15: Solid foundation, practice applications
• Score > 15: Strong math skills, focus on advanced ML

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Domain 3: Machine Learning & AI

Classical ML:

• Supervised learning (regression, classification)
• Model evaluation (accuracy, F1, ROC-AUC)
• Cross-validation and overfitting
• Hyperparameter tuning

Deep Learning:

• Neural networks basics
• CNNs for computer vision
• RNNs/LSTMs for sequences
• Transformers and transfer learning

Specialized Areas:

• NLP (sentiment analysis, text classification)
• Computer Vision (object detection, segmentation)
• Recommender systems
• Time series forecasting

Score: __ / 20

Recommendation:

• Score < 10: Start with Machine Learning & AI Agent
• Score 10-15: Build more projects, specialize
• Score > 15: Expert level, explore research or production

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Domain 4: Data Engineering

Data Pipelines:

• ETL/ELT concepts
• Apache Airflow for orchestration
• Data validation and quality
• Incremental vs full loads

Big Data:

• Apache Spark (PySpark)
• Distributed computing concepts
• Data lakes vs data warehouses
• Cloud platforms (AWS, GCP, Azure)

Databases:

• Relational databases (PostgreSQL, MySQL)
• NoSQL (MongoDB, Redis)
• Data warehouses (Snowflake, BigQuery)
• Database design and optimization

Score: __ / 20

Recommendation:

• Score < 10: Focus on Data Engineering Agent
• Score 10-15: Build production pipelines
• Score > 15: Lead data infrastructure projects

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Domain 5: MLOps & Deployment

Model Deployment:

• REST APIs (Flask, FastAPI)
• Docker containerization
• Kubernetes basics
• Cloud deployment (SageMaker, Vertex AI)

ML Infrastructure:

• CI/CD pipelines for ML
• Model versioning (MLflow, DVC)
• Model monitoring and drift detection
• A/B testing for models

Production Best Practices:

• Logging and monitoring
• Scalability and performance
• Security and compliance
• Cost optimization

Score: __ / 20

Recommendation:

• Score < 10: Learn MLOps & Deployment Agent
• Score 10-15: Build production systems
• Score > 15: Design ML platforms

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Domain 6: Data Visualization & Communication

Visualization Tools:

• Matplotlib, Seaborn basics
• Interactive viz (Plotly, Dash)
• BI tools (Tableau, Power BI)
• Dashboard design principles

Communication:

• Exploratory data analysis (EDA)
• Data storytelling
• Stakeholder presentations
• Creating actionable insights

Score: __ / 20

Recommendation:

• Score < 10: Work with Visualization & Communication Agent
• Score 10-15: Improve storytelling skills
• Score > 15: Mentor others on communication

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Domain 7: Business & Career

Business Acumen:

• Translating business problems to DS problems
• ROI and impact measurement
• Industry knowledge (finance, healthcare, etc.)
• Agile/project management

Professional Skills:

• Portfolio with 3-5 projects
• GitHub profile
• Networking (LinkedIn, conferences)
• Interview preparation (coding, ML, case studies)

Ethics & Compliance:

• Responsible AI principles
• Bias and fairness
• Data privacy (GDPR, HIPAA)
• Model explainability

Score: __ / 20

Recommendation:

• Score < 10: Use Domain & Career Agent
• Score 10-15: Build professional brand
• Score > 15: Give back to community

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Overall Assessment

Total Score: __ / 140

Skill Level Classification

Beginner (0-40 points):

• Focus: Foundations (Python, SQL, basic ML)
• Timeline: 6-12 months to intermediate
• Action: Follow complete beginner roadmap
• Daily practice: 2-3 hours minimum

Intermediate (41-80 points):

• Focus: Projects, specialization, portfolio
• Timeline: 6-12 months to advanced
• Action: Build 5+ projects, contribute to open source
• Daily practice: 1-2 hours + weekend projects

Advanced (81-110 points):

• Focus: Production systems, specialization depth
• Timeline: 6+ months to expert
• Action: Lead projects, mentor others, publish
• Daily practice: Continuous learning

Expert (111-140 points):

• Focus: Innovation, research, thought leadership
• Timeline: Ongoing mastery
• Action: Contribute to field, speak at conferences
• Daily practice: Stay cutting-edge

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Personalized Learning Plan

Based on your scores, here's your recommended learning path:

Weakest Domain (Lowest score): → Start here first

Second Weakest: → Learn in parallel

Strongest Domain: → Help others, contribute to community

Career Stage:

• Looking for first role: Focus on Programming + ML + Portfolio
• Transitioning careers: Add Domain Agent + Interview prep
• Advancing in role: MLOps + Specialization + Leadership

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Skill Gaps Analysis

Critical Gaps (Score 0-2 in any category)

These are blocking your progress - prioritize immediately!

Important Gaps (Score 3 in multiple categories)

Build competency here for well-rounded skills.

Growth Areas (Score 4)

Push to expert level for differentiation.

Strengths (Score 5)

Leverage these! Help others, create content.

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Next Steps

1. Calculate your total score
2. Identify weakest domain
3. Set 90-day learning goal
4. Choose relevant agent to start
5. Build while learning - theory + practice
6. Re-assess in 3 months

Commands to Continue

# Start learning with personalized path
/learn

# View complete roadmap
/roadmap

# Explore hands-on projects
/projects

# Browse all agents
/browse-agent

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