Big Data Analysis with R and Python Training Course

Participants can structure reproducible R and Python projects with clear dependencies, configuration and documentation.

• Project directories separate raw data, code, outputs, documentation and tests
• Virtual environments isolate Python packages and reproducible package versions
• R project configuration manages libraries, paths and analysis parameters
• Git commits record analytical changes, review points and decision rationale
• Configuration files externalise credentials, thresholds and source locations
• Notebook and script roles selected for exploration, automation and reporting

Participants can acquire data from common sources and choose suitable storage formats for analytical workloads.

• CSV, JSON and Parquet formats compared for size, schema and access
• API requests retrieve paginated records with authentication and rate controls
• Database connectors extract parameterised query results into analysis environments
• Schema inspection identifies field types, null patterns and encoding problems
• Partitioned files organised by date, geography or business unit
• Data dictionaries document source ownership, refresh frequency and field meaning

Participants can profile incoming datasets and implement checks that detect material quality failures.

• Column profiling calculates completeness, uniqueness, ranges and cardinality measures
• Business rules validate permitted values, dates, thresholds and relationships
• Duplicate detection combines exact matching with probabilistic record linkage
• Missing-data mechanisms classified as MCAR, MAR and MNAR assumptions
• Outlier screening uses interquartile range, z-scores and domain thresholds
• Data quality scorecards prioritise failures by decision impact and recurrence

Participants can transform raw records into analysis-ready datasets using efficient R and Python methods.

• Pandas DataFrames filter, join, reshape and aggregate operational records
• dplyr pipelines express grouped transformations with readable chained operations
• Date-time parsing standardises time zones, reporting periods and elapsed durations
• String normalisation resolves casing, whitespace, codes and malformed identifiers
• Window functions calculate rolling totals, ranks and period-over-period changes
• Tidy analytical tables separate entities, events, measures and reference attributes

Participants can write SQL queries that prepare joined, aggregated and windowed datasets for analysis.

• SELECT, WHERE and CASE expressions derive analysis-ready business fields
• Inner, left and anti joins diagnose matching coverage and orphan records
• Common table expressions organise multi-stage transformations and quality checks
• GROUP BY aggregations calculate customer, account and transaction measures
• Window functions produce cohorts, running balances and behavioural features
• Query plans reviewed to identify expensive joins, scans and aggregations

Participants can investigate distributions, relationships and anomalies using visual and statistical diagnostics.

• Univariate summaries reveal skewness, concentration, missingness and unusual ranges
• Histograms and density plots compare distributions across customer segments
• Boxplots identify outliers requiring investigation, treatment or retention
• Scatterplots expose nonlinear relationships, clusters and measurement artefacts
• Correlation matrices distinguish association patterns from causal evidence
• Faceted charts compare trends by product, region, channel and cohort

Participants can apply statistical tests and confidence intervals without mistaking statistical significance for decision value.

• Sampling distributions explain uncertainty in estimates from large populations
• Confidence intervals communicate plausible ranges around key performance measures
• Hypothesis tests compare proportions, means and distributions between groups
• Effect sizes distinguish material changes from trivial significant results
• Multiple-testing controls reduce false discoveries across many comparisons
• Bootstrap resampling estimates uncertainty when parametric assumptions fail

Participants can create, select and document features suitable for predictive models.

• Recency, frequency and monetary features summarise customer transaction behaviour
• Lagged variables capture prior activity without introducing target leakage
• Categorical encoding handles nominal, ordinal and high-cardinality variables
• Text features use tokenisation, n-grams and term frequency measures
• Feature scaling prepares numeric variables for distance-based algorithms
• Feature documentation records definitions, lineage, refresh rules and caveats

Participants can process large datasets using Apache Spark DataFrames and Spark SQL operations.

• Spark execution model explains lazy evaluation, transformations and actions
• DataFrame schemas enforce types, nullable fields and nested structures
• Spark SQL queries aggregate partitioned data across distributed workers
• Partition pruning reduces file reads through selective predicate filters
• Caching decisions balance repeated access against available cluster memory
• Shuffle operations identified through joins, groupings and window calculations

Participants can diagnose distributed processing bottlenecks and improve workflow performance and reliability.

• Partition sizing balances parallelism, scheduling overhead and executor workload
• Data skew detected through uneven partition sizes and long-running tasks
• Broadcast joins reduce movement when reference tables are small
• Columnar storage improves scan performance for selective analytical queries
• Checkpointing protects long pipelines from repeated lineage recomputation
• Execution plans guide tuning of joins, filters and aggregations

Participants can train and compare classification models for operational decision problems.

• Binary target definitions align model labels with actual business outcomes
• Logistic regression establishes interpretable baseline classification performance
• Decision trees expose rule-based segments and nonlinear predictor effects
• Random forests reduce variance through aggregated decorrelated decision trees
• Cross-validation estimates generalisation performance across repeated data splits
• Confusion matrices quantify correct classifications and decision error types

Participants can build regression and forecasting models for numeric prediction and time-based planning.

• Linear regression models continuous outcomes with interpretable coefficient estimates
• Regularisation methods control overfitting through Lasso and Ridge penalties
• Residual diagnostics test linearity, heteroscedasticity and influential observations
• Time-series splits preserve chronological order during model evaluation
• ARIMA models represent autoregression, differencing and moving-average error structures
• Forecast intervals communicate uncertainty around projected operational demand

Participants can identify meaningful segments, unusual records and emerging patterns without labelled outcomes.

• K-means clustering groups records using scaled distance-based similarity measures
• Hierarchical clustering reveals nested segment structures through dendrogram inspection
• Principal component analysis reduces correlated variables into interpretable components
• Isolation Forest identifies rare observations through random partitioning
• Cluster profiling translates mathematical groups into business-relevant descriptions
• Anomaly review separates data errors, unusual behaviour and genuine risk signals

Participants can evaluate model fitness, select decision thresholds and define monitoring controls.

• Train, validation and test splits protect against optimistic performance estimates
• ROC curves compare classifier discrimination across possible decision thresholds
• Precision-recall curves assess rare-event detection performance under class imbalance
• Calibration plots test whether predicted probabilities match observed frequencies
• Cost matrices select thresholds using false-positive and false-negative consequences
• Model monitoring tracks drift, performance decay and retraining triggers

Participants can apply governance, privacy and ethical controls to analytical data and model outputs.

• Data classification identifies public, internal, confidential and restricted information
• Purpose limitation links personal data use to approved analytical objectives
• Pseudonymisation reduces direct identification risk within analytical datasets
• Access controls apply least privilege to data, code and outputs
• Fairness checks compare model errors across relevant population groups
• Model documentation records intended use, limitations, assumptions and approvals

Participants can communicate analytical findings through focused visuals, uncertainty statements and decision recommendations.

• Chart selection matches comparison, distribution, composition and trend questions
• Annotation explains material changes, thresholds, outliers and decision implications
• Dashboard measures distinguish leading indicators from lagging operational results
• Uncertainty statements qualify estimates, forecasts and model-generated predictions
• Executive summaries state evidence, implications, options and recommended action
• Technical appendices preserve methods, data definitions and reproducibility details

Participants can design maintainable analytical pipelines with testing, scheduling and operational controls.

• Pipeline stages separate ingestion, validation, transformation, modelling and reporting
• Unit tests verify reusable functions against expected inputs and outputs
• Data tests detect schema changes, null spikes and duplicate records
• Logging captures run times, row counts, warnings and failure messages
• Scheduled jobs define dependencies, refresh windows and escalation procedures
• Runbooks document recovery steps, ownership and operational service levels

Participants can build, review and present a complete large-scale analytical solution from raw data to decision recommendation.

• Problem statement defines decision owner, objective, constraints and success measures
• Source data assessed through schema review, profiling and quality controls
• R or Python pipeline transforms data into documented analytical features
• Spark processing scales required joins, aggregations and model preparation
• Model results evaluated using relevant metrics, thresholds and limitations
• Findings presentation recommends actions supported by traceable analytical evidence