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Catalog / All levels / CompTIA DataAI Certification with Practice Exam
Mammoth Club All levels 8 sections 81 lectures

CompTIA DataAI Certification with Practice Exam

Get certified and let your analysis speak for itself.

JM
Created by Jared Matson
01
Skill level
All levels
02
Sections
8
03
Lectures
81
04
Instructor
Jared Matson
What's inside

This course includes.

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8
Sections
✓
81
Lectures
✓
25
Resources
✓
1
Quiz
✓
Certificate of completion
Included
✓
Mobile and desktop access
Included
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AI learning assistance
Included
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Course content

Curriculum & lectures.

7 sections · 80 lectures
+ Domain 0 — Introduction 1 lecture
0.1 Introduction Locked
+ Domain 1 — Mathematics and statistics 15 lectures
1.1 Statistical Significance Tests: T-Tests, ANOVA, and Chi-Squared Locked
1.2 Hypothesis Testing: P-Values, Error Types, and Confidence Intervals Locked
1.3 Correlation and Regression Metrics: Pearson, Spearman, R-Squared, RMSE, and AIC/BIC Locked
1.4 Classification Metrics: Confusion Matrix, Precision, Recall, F1, MCC, and ROC/AUC Locked
1.5 Decision Tree Impurity: Gini, Entropy, and Information Gain Locked
1.6 Probability Distributions and Their Functions: Normal, Poisson, Binomial, and PDF/PMF/CDF Locked
1.7 Distribution Shape: Skewness, Kurtosis, and Heteroskedasticity Locked
1.8 Probability and Simulation Tools: Expected Value, Bayes' Rule, Monte Carlo, and Bootstrapping Locked
1.9 Missing Data and Sampling: MCAR/MAR/MNAR, Oversampling, and Stratification Locked
1.10 Linear Algebra Essentials: Rank, Span, Eigenvalues, and Eigenvectors Locked
1.11 Matrix Operations and Distance Metrics: Multiplication, Inversion, Euclidean, and Cosine Locked
1.12 Calculus for Machine Learning: Partial Derivatives, the Chain Rule, Exponentials, and Logarithms Locked
1.13 Time Series Models: AR, MA, and ARIMA Locked
1.14 Longitudinal and Survival Analysis: Censoring, Parametric, and Non-Parametric Methods Locked
1.15 Causal Inference: RCTs, A/B Testing, Difference-in-Differences, and DAGs Locked
+ Domain 2 — Modeling, analysis, and outcomes 16 lectures
2.1 EDA Workflow: Univariate, Multivariate, Behaviors, and Attributes Locked
2.2 EDA Charts I: Distribution and Shape Plots Locked
2.3 EDA Charts II: Relationship and Multivariate Plots Locked
2.4 Feature Types: Categorical, Discrete, Continuous, Ordinal, Nominal, and Binary Locked
2.5 Common Data Issues I: Sparsity, Multicollinearity, Insufficient Features, Granularity, and Outliers Locked
2.6 Common Data Issues II: Non-Stationarity, Seasonality, Lagged and Differenced Observations, and Non-Linearity Locked
2.7 Feature Engineering and Encoding: One-Hot, Label, Cross-Terms, Binning, Ratios, and Pivoting Locked
2.8 Scaling and Distribution Transforms: Normalization, Standardization, Log/Exp, and Box-Cox Locked
2.9 Adding Data Sources: Geocoding, Data Augmentation, and Synthetic Data Locked
2.10 Model Design: Constraints and Requirements Validation Locked
2.11 Evaluating Model Performance: Metrics, Cost, Inference Over Time, and Diagnostic Plots Locked
2.12 Model Selection and Iteration: Literature Review, Hyperparameter Tuning, Experiment Tracking, and Architecture Iteration Locked
2.13 Justifying Model Selection: Baselines, Benchmarks, and Business Requirements Locked
2.14 Communicating Results: Reports, Visualization Selection, and Stakeholder Audiences Locked
2.15 Honest and Accessible Charts: Avoiding Deception and Meeting Accessibility Standards Locked
2.16 Data and Model Documentation: Code Docs, Data Dictionary, Metadata, and Change Descriptions Locked
+ Domain 3 — Machine learning 21 lectures
3.1 Bias-Variance Tradeoff: Loss, Overfitting, Underfitting, Regularization, and Occam's Razor Locked
3.2 Validation and Tuning: Cross-Validation, In/Out of Sample, Interpolation vs. Extrapolation, and Grid/Random Search Locked
3.3 Feature Selection and Dimensionality: Feature Importance, Multicollinearity, VIF, and Embeddings Locked
3.4 Class Imbalance: Oversampling, Undersampling, and SMOTE Locked
3.5 Supervised Learning Tasks and Ensembles: Classification, Regression, and Combined Models Locked
3.6 Recommender Systems: Collaborative Filtering, ALS, and Similarity-Based Methods Locked
3.7 Model Interpretability: Interpretable Models and Post Hoc Explanations Locked
3.8 Real-World ML Issues: Drift, Leakage, Transfer Learning, and Cold Start Locked
3.9 Linear Regression Family: OLS, Weighted Least Squares, Ridge, LASSO, and Elastic Net Locked
3.10 Logistic Regression and Discriminant Analysis: Logit, Probit, LDA, and QDA Locked
3.11 Naive Bayes and Association Rules: Support, Confidence, and Lift Locked
3.12 Decision Trees and Bagging: Decision Trees, Bootstrap Aggregation, and Random Forest Locked
3.13 Boosting: Gradient Boosting and XGBoost Locked
3.14 Neural Network Architecture: Perceptron, Artificial Neuron, MLP, and Layer Types Locked
3.15 Activation Functions: ReLU, Sigmoid, Tanh, and Softmax Locked
3.16 Training Neural Networks: Backpropagation, Mini-Batches, and Optimizers Locked
3.17 Stabilizing Training: Dropout, Batch Normalization, Early Stopping, and Schedulers Locked
3.18 Deep Learning Architectures: CNN, RNN, LSTM, GANs, Autoencoders, and Transformers Locked
3.19 Learning Paradigms and Frameworks: Zero-, One-, and Few-Shot Learning, PyTorch, TensorFlow, and AutoML Locked
3.20 Clustering and K-Nearest Neighbors: K-Means, Hierarchical, DBSCAN, and KNN Locked
3.21 Dimensionality Reduction: PCA, T-SNE, UMAP, and SVD Locked
+ Domain 4 — Operations and processes 16 lectures
4.1 Compliance, Security, and Privacy: PII, Proprietary Data, Anonymization, and Data Regulations Locked
4.2 Business Alignment: KPIs, Requirements Gathering, and Cost-Benefit Analysis Locked
4.3 Generated Data Sources: Survey, Administrative, Sensor, Transactional, and Experimental Locked
4.4 Synthetic and Commercial/Public Data: Costs, Benefits, Licensing, and Restrictions Locked
4.5 Data Formats, Storage, and Infrastructure: CSV/JSON/Parquet, Structured/Semi/Unstructured, and GPU/TPU Sizing Locked
4.6 Data Pipelines: Streaming vs. Batching, Orchestration, Persistence, and Lineage Locked
4.7 Merging and Joining Data: Keys, Join Types, Union, Intersection, and Fuzzy Matching Locked
4.8 Cleaning Data: Date/Time Standardization, Regex, Deduplication, Unit Conversion, and Missing Codes Locked
4.9 Errors, Outliers, and Imputation: Systematic vs. Idiosyncratic Errors, Winsorization, and Imputation Types Locked
4.10 Flattening and Labeling: XML/JSON Flattening and Ground Truth Labeling Locked
4.11 Workflow Models and Version Control: CRISP-DM, DAMA, and Versioning Code, Data, and Models Locked
4.12 Clean Code and Documentation: Clean Code, Unit Tests, Docstrings, and Markdown Locked
4.13 Tooling and Access: IDEs, Dependency Licensing, and API Access Locked
4.14 DevOps and MLOps Foundations: CI/CD, Container Orchestration, Virtualization, and Deployment Locked
4.15 Model Monitoring and Validation: Performance Monitoring, Online/Offline Validation, and A/B Testing Locked
4.16 Deployment Environments: Containerization, Cloud, Cluster, Hybrid, Edge, and On-Premises Locked
+ Domain 5 — Specialized applications of data science 10 lectures
5.1 Constrained Optimization: Routing, Scheduling, Solvers, Pricing, and Resource Allocation Locked
5.2 Unconstrained Optimization: One-Armed and Multi-Armed Bandits, and Finding Local Optima Locked
5.3 Text Preparation: Tokenization, Stemming, Lemmatization, Stop Words, and POS Tagging Locked
5.4 Text Representation: Bag of Words, TF-IDF, Document Term Matrix, N-Grams, and Edit Distance Locked
5.5 Word Embeddings, Language Models, and Topic Modeling: Word2Vec, GloVe, LLMs, and LDA Locked
5.6 NLP Applications: Sentiment, NER, Summarization, Q&A, Generation, Speech, NLU, and NLG Locked
5.7 Computer Vision Tasks: OCR, Segmentation, Object Detection, Tracking, and Sensor Fusion Locked
5.8 Computer Vision Data Augmentation: Rotation, Flipping, Cropping, Occlusion, Masking, and Noise Locked
5.9 Graphs, Heuristics, and Reinforcement Learning: Graph Theory, Greedy Algorithms, and RL Locked
5.10 Detection and Specialized ML: Event, Fraud, and Anomaly Detection, Multimodal ML, Edge Optimization, and Signal Processing Locked
+ Domain 6 — Finding Your Exams 1 lecture
6.1 Finding Your Exams Locked
Description

About this course.

Data is only valuable when someone knows what to do with it. This course helps you become that person.CompTIA DataAI+ is built for professionals who want to work confidently with complex data sets, turn raw numbers into business decisions, and prove their analytical skills with a recognized certification.

► Work with complex data sets and draw actionable insights.

► Implement data-driven solutions for business growth.

► Interpret data in ways that actually influence decisions.

► Prepare for the CompTIA DataAI+ certification exam.

If data is part of your role, this certification puts a stamp on skills you may already be building.

✅ Lifetime access to all modules.

✅ Graded practice exam with unlimited attempts.

Instructors

Taught by people who ship.

JM

Jared Matson

Instructor

Produced by a team of Mammoth Club industry experts. Over 15 years, Mammoth Club has built a global student community in 190+ countries with 9+ million courses sold, releasing over 1,000+ courses and 5,000+ hours of video content.

Ready to start building?

Get certified and let your analysis speak for itself.

Buy lifetime access →