CompTIA DataAI

Mathematik und Statistik

Statistical methods

• Anwendung von t-tests
• Anwendung von chi-squared tests
• analysis of variance (ANOVA)
• hypothesis testing
• regression metrics
• gini index
• entropy
• p-value
• receiver operating characteristic / area under the curve (ROC/AUC)
• akaike information criterion / bayesian information criterion (AIC/BIC)
• confusion matrix

Probability and modeling

• distributions
• skewness
• kurtosis
• heteroskedasticity
• probability density function (PDF)
• probability mass function (PMF)
• cumulative distribution function (CDF)
• missingness
• oversampling
• stratification

Linear algebra and calculus

• rank
• eigenvalues
• matrix operations
• distance metrics
• partial derivatives
• chain rule
• logarithms

Temporal models

• time series
• survival analysis
• causal inference

Modellierung, Analyse und Ergebnisse

EDA methods

• exploratory data analysis (EDA)
• univariate analysis
• multivariate analysis
• charts
• graphs
• feature identification

Data issues

• Analyse von sparse data
• non-linearity
• seasonality
• granularity
• outliers

Data enrichment

• feature engineering
• scaling
• geocoding
• data transformation

Model iteration

• design
• evaluation
• selection
• validation

Results communication

• Erstellung von visualizations
• Auswahl geeigneter data
• Vermeidung von deceptive charts
• accessibility

Machine Learning

Foundational concepts

• loss functions
• bias-variance tradeoff
• regularization
• cross-validation
• ensemble models
• hyperparameter tuning
• data leakage

Supervised learning

• linear regression
• logistic regression
• k-nearest neighbors (KNN)
• naive bayes
• association rules

Tree-based learning

• decision trees
• random forest
• boosting
• bootstrap aggregation (bagging)

Deep learning

• artificial neural networks (ANN)
• dropout
• batch normalization
• backpropagation
• deep-learning frameworks

Unsupervised learning

• clustering
• dimensionality reduction
• singular value decomposition (SVD)

Operations und Prozesse

Business functions

• compliance
• key performance indicators (KPIs)
• requirements gathering

Data types

• generated data
• synthetic data
• public data

Data ingestion

• pipelines
• streaming
• batching
• data lineage

Data wrangling

• cleaning
• merging
• imputation
• ground truth labeling

Data science life cycle

• workflow models
• version control
• clean code
• unit tests

DevOps und MLOps

• continuous integration / continuous deployment (CI/CD)
• model deployment
• container orchestration
• performance monitoring

Deployment environments

• containerization
• cloud
• hybrid
• edge
• on-premises deployment

Spezialisierte Anwendungen der Data Science

Optimization

• constrained optimization
• unconstrained optimization

NLP concepts

• natural language processing (NLP)
• tokenization
• embeddings
• term frequency-inverse document frequency (TF-IDF)
• topic modeling
• NLP applications

Computer vision

• optical character recognition (OCR)
• object detection
• tracking
• data augmentation

Other applications

• graph analysis
• reinforcement learning
• fraud detection
• anomaly detection
• signal processing

Für eine optimale Teilnahme am Kurs empfehlen wir folgende Vorkenntnisse:

• 5 Jahre Erfahrung im Bereich Data Science oder einer ähnlichen Rolle werden empfohlen

• Data Scientist