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AI & Data Scientist Knowledge Assessment

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Master Python, R, SQL, Git, data structures, and algorithms for data science excellence

Master linear algebra, calculus, probability, statistics, and mathematical foundations for ML/AI

Master ETL/ELT pipelines, big data processing (Spark, Hadoop), data lakes, streaming, and data quality

Master supervised/unsupervised learning, deep learning, NLP, computer vision, and model optimization

Master EDA, dashboards, storytelling, BI tools (Tableau, Power BI), and stakeholder communication

Master model deployment, Docker/Kubernetes, CI/CD, monitoring, cloud platforms, and production ML

Business acumen, ethics, compliance, project management, career paths, and portfolio building

AI research methodology, experimental design, paper writing, literature review, reproducible research, and scientific innovation

Image processing, object detection, segmentation, and vision models. Use for image classification, object detection, or visual analysis tasks.

ETL pipelines, Apache Spark, data warehousing, and big data processing. Use for building data pipelines, processing large datasets, or data infrastructure.

EDA, dashboards, Matplotlib, Seaborn, Plotly, and BI tools. Use for creating visualizations, exploratory analysis, or dashboards.

Neural networks, CNNs, RNNs, Transformers with TensorFlow and PyTorch. Use for image classification, NLP, sequence modeling, or complex pattern recognition.

Supervised/unsupervised learning, model selection, evaluation, and scikit-learn. Use for building classification, regression, or clustering models.

Docker, Kubernetes, CI/CD, model monitoring, and cloud platforms. Use for deploying ML models to production, setting up pipelines, or infrastructure.

Quantization, pruning, AutoML, hyperparameter tuning, and performance optimization. Use for improving model performance, reducing size, or automated ML.

Text processing, sentiment analysis, LLMs, and NLP frameworks. Use for text classification, named entity recognition, or language models.

Python fundamentals, data structures, OOP, and data science libraries (Pandas, NumPy). Use when writing Python code, data manipulation, or algorithm implementation.

Q-learning, DQN, PPO, A3C, policy gradient methods, multi-agent systems, and Gym environments. Use for training agents, game AI, robotics, or decision-making systems.

Probability, distributions, hypothesis testing, and statistical inference. Use for A/B testing, experimental design, or statistical validation.

ARIMA, SARIMA, Prophet, trend analysis, seasonality detection, anomaly detection, and forecasting methods. Use for time-based predictions, demand forecasting, or temporal pattern analysis.