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Data Science Fundamentals
The Data Science Fundamentals Certificate covers the foundational concepts of data science: the data lifecycle, statistics, machine learning at conceptual depth, data engineering basics, visualization, and the governance considerations specific to data products. Targeted at conceptual depth for non-practitioners.
Who Should Take This
Auditors, business analysts, IT generalists, and managers who work alongside data scientists and need a working vocabulary. Assumes basic computing and statistical literacy. Learners finish able to discuss data science projects with practitioners and recognize common patterns and pitfalls.
What's Included in AccelaStudy® AI
Adaptive Knowledge Graph
Practice Questions
Lesson Modules
Console Simulator Labs
Exam Tips & Strategy
13 Activity Formats
Course Outline
1Data Lifecycle 3 topics
Data Sources and Ingestion
- Identify common data sources: transactional databases, APIs, log streams, file uploads, third-party feeds, sensors.
- Identify ingestion patterns: batch, micro-batch, streaming, change data capture (CDC).
Data Cleaning and Preparation
- Identify common data-quality problems: missing values, outliers, inconsistent formats, duplicate records, schema drift.
- Apply data-cleaning techniques: imputation, outlier handling, deduplication, schema validation, normalization.
- Analyze a data-quality scenario where 30% of a key column is missing and identify whether to drop the column, impute, or restructure the analysis.
Exploration and Feature Engineering
- Identify exploratory data analysis (EDA) and identify common steps: distribution plots, correlation review, missingness inspection, target-distribution check.
- Identify feature engineering as the creation of model-suitable inputs from raw data and identify common operations: encoding, scaling, aggregation, binning.
2Statistics 3 topics
Descriptive Statistics
- Identify common descriptive statistics: mean, median, mode, standard deviation, variance, percentile.
- Apply descriptive-statistics interpretation: identify when median is more informative than mean (skewed distributions, outliers).
Distributions and Sampling
- Identify common distributions: normal, binomial, Poisson, exponential, log-normal — and identify a real-world example of each.
- Identify sampling concepts: random sampling, stratified sampling, sampling bias, sample size.
Inference and Causality
- Identify hypothesis testing concepts: null hypothesis, alternative hypothesis, p-value, significance level, statistical power.
- Distinguish correlation and causation and identify common spurious-correlation pitfalls.
- Analyze a 'correlation reported as causation' headline and identify the missing controlled experiment or causal inference required.
3Machine Learning 3 topics
Learning Paradigms
- Distinguish supervised (labeled), unsupervised (unlabeled), and reinforcement learning and identify a representative use case for each.
- Identify the typical supervised tasks (classification, regression) and unsupervised tasks (clustering, dimensionality reduction).
Common Algorithms
- Identify linear regression, logistic regression, decision trees, random forests, gradient boosting, k-means, and neural networks at conceptual depth.
- Apply algorithm-selection guidance: linear models for small interpretable problems, gradient boosting for tabular data, neural networks for unstructured data (text, images).
Deep Learning and Foundation Models
- Identify deep learning as multi-layer neural networks and identify common architectures (CNN for images, RNN/Transformer for sequences).
- Identify foundation models and LLMs as pretrained models that can be adapted to many downstream tasks.
4Model Evaluation 3 topics
Splitting and Cross-Validation
- Identify train/test split, k-fold cross-validation, and identify the role of a held-out test set in unbiased evaluation.
- Identify common pitfalls: target leakage, train-test contamination, ignoring time order in time-series data.
Classification Metrics
- Identify accuracy, precision, recall, F1, ROC-AUC, and the confusion matrix.
- Apply metric-selection guidance: precision when false positives are expensive, recall when false negatives are expensive, F1 when both matter.
- Analyze a classifier with 99% accuracy on imbalanced data (1% positive class) and explain why accuracy is misleading.
Regression and Other Metrics
- Identify common regression metrics: MAE, RMSE, MAPE, R² — and identify when each is appropriate.
- Identify drift and degradation as the post-deployment failure modes that require ongoing monitoring.
5Data Engineering and Visualization 3 topics
Pipelines and Storage
- Identify ETL/ELT pipelines and identify common tools: Airflow, dbt, Spark, Fivetran, AWS Glue.
- Distinguish data warehouses (Snowflake, BigQuery, Redshift), data lakes (S3, GCS, ADLS), and lakehouses (Databricks, Iceberg, Delta).
Streaming and Real-Time
- Identify streaming data systems: Kafka, Kinesis, Pub/Sub — and identify their typical use cases (CDC, telemetry, event-driven pipelines).
- Apply batch-vs-streaming selection guidance: latency requirements, ordering needs, replay-ability.
Visualization
- Identify common chart types and their appropriate use: bar (categorical comparison), line (trends over time), scatter (correlation), heatmap (matrix relationships), box (distribution comparison).
- Analyze a misleading chart (truncated y-axis, dual axes, deceptive coloring) and propose a corrected version.
6Governance and Ethics 3 topics
Data Governance
- Identify data governance topics: data ownership, stewardship, lineage, data dictionary, master-data management.
- Identify the value of data lineage in audit and root-cause investigation.
Privacy and Sensitive Data
- Identify privacy-by-design concepts: minimization, anonymization, pseudonymization, k-anonymity at conceptual depth.
- Apply privacy-controls selection for a customer-analytics workload subject to GDPR and CCPA, balancing utility with minimization.
Model Ethics and Bias
- Identify common sources of model bias: training-data imbalance, label bias, proxy variables, deployment-context drift.
- Identify model-governance practices: model cards, evaluation across demographic slices, monitoring for drift, human-in-the-loop for high-stakes decisions.
- Analyze a hiring-screening model that produced disparate outcomes across demographic groups and identify the diagnostic and remediation steps.
7Data Science in Practice 8 topics
Working with Stakeholders
- Identify common stakeholder personas: business sponsor, subject-matter expert, data engineer, ML engineer, end-user, regulator.
- Apply requirements-elicitation for a data-science project: convert vague business asks into measurable success criteria, identify the acceptable range of model errors.
- Analyze a 'we just need a model that predicts churn' request and identify the missing scope (data access, labels, evaluation, deployment, monitoring).
Project Lifecycle and Reproducibility
- Identify CRISP-DM and TDSP as standard data-science process frameworks and identify their typical phases.
- Identify reproducibility concerns: random seeds, environment pinning, dataset versioning (DVC, LakeFS, Delta), experiment tracking (MLflow, Weights & Biases).
- Apply experiment-tracking discipline so that 6 months later a stakeholder can answer 'how was this number produced?' without rerunning the world.
Productionizing Models
- Identify deployment patterns: batch scoring, online inference, edge inference, embedded.
- Identify MLOps concepts: CI/CD for models, model registry, A/B testing, canary deploy, shadow mode.
- Apply 'shadow mode' deployment guidance for a high-stakes model: route requests to both old and new model, log differences, compare before cutover.
Generative AI in Data Science
- Identify how foundation models change data-science practice: code generation, EDA assistants, synthetic data, data labeling, summarization of analytic results.
- Apply generative AI safely in a data-science workflow: never paste sensitive data into public LLMs, use enterprise-licensed providers, validate generated outputs.
Data Science Career
- Identify common data-science career paths: data analyst, data scientist, ML engineer, data engineer, applied scientist, research scientist.
- Identify the spectrum of practitioner roles: business-analyst-leaning data scientist (SQL, viz, statistics) vs ML-engineer-leaning (production systems, MLOps).
Continuous Learning
- Identify continuous-learning sources: arXiv (cs.LG, cs.CL, stat.ML), NeurIPS / ICML / ACL papers, fast.ai courses, Andrej Karpathy's series, Distill.pub archive.
- Apply hands-on practice: Kaggle competitions, reproducing published results, contributing to open-source ML libraries.
- Analyze the rapid-AI-evolution information overload and propose a focus strategy: pick depth areas, time-box exploration, accept that some advances you will not master.
Communicating Data Science
- Identify common data-science communication failures: jargon overload, no business framing, 'all the metrics' instead of the relevant ones, ignoring uncertainty.
- Apply audience-aware reporting: executive summary with one chart, technical appendix with full evaluation, code repo for reviewers.
Ethics in Practice
- Identify common practical ethics scenarios: sensitive features in models, opt-out for users, transparency about AI involvement, dual-use concerns.
- Apply structured ethical-review for a proposed model: who is affected, what failure modes exist, what redress is available, what oversight will be in place.
Scope
Included Topics
- Data lifecycle: collection, ingestion, cleaning, exploration, modeling, deployment, monitoring.
- Statistical concepts: descriptive vs inferential, distributions, hypothesis testing, correlation vs causation.
- Machine learning concepts: supervised, unsupervised, reinforcement learning at conceptual depth.
- Common algorithms: linear/logistic regression, decision trees, random forests, gradient boosting, neural networks.
- Model evaluation: train/test split, cross-validation, accuracy/precision/recall/F1, ROC-AUC, confusion matrix.
- Data engineering basics: pipelines, data warehouses, data lakes, lakehouses, streaming.
- Visualization concepts and common chart types.
- Data governance: quality, lineage, privacy, ethics, model governance.
Not Covered
- Hands-on Python/R/SQL coding.
- Deep mathematical foundations.
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