Data Analytics for Managerial Decision-Making Training

  • Data Analytics for Managerial Decision-Making Training
  • Spain - Barcelona

Data Analytics for Managerial Decision-Making Training

Course Category : Administrative Development

This course enables participants to turn data into insights, insights into decisions, and decisions into organisational value by leveraging data analytics as a decision-support tool for management.
Duration: 5 Training Days
Level: Intermediate–Advanced.

Starts On

1 - June - 2026

Ends On

5 - June - 2026

Location

Spain - Barcelona

Language

English

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View Course Introductory PDF

Targeted Audience

  • Professionals in management support roles.
  • Business and data analysts in corporate and public sectors.
  • Managers and supervisors who work with periodic reports and metrics.
  • Planning, quality, performance management, and KPI officers.
  • Anyone seeking to derive more decision-making value from data analytics.

Targeted Skills

  • Managerial data analysis skills.
  • Statistical thinking linked to KPIs.
  • Interpreting statistical evidence for decisions.
  • Applying descriptive, inferential, and predictive analytics.
  • Producing tables, dashboards, and visual summaries.
  • Integrating quantitative reasoning into managerial decision-making.

Expected Outcomes

  • Recognise the value of data analytics as a managerial decision-support tool.
  • Distinguish between different data types and prepare data for analysis.
  • Apply descriptive analytics to profile business/managerial data clearly.
  • Interpret statistical evidence (averages, variability, relationships) for decisions.
  • Understand the foundations of statistical inference and sampling.
  • Use hypothesis testing to compare groups and support managerial options.
  • Explore predictive modelling and data mining for management applications.
  • Integrate statistical thinking into regular management reports and KPIs.

Training Topics Index

  • The quantitative landscape in management.
  • Thinking statistically about management applications (identifying KPIs).
  • Integrative elements of data analytics.
  • Data as the raw material – types, quality, preparation.
  • Exploratory data analysis using Excel (pivot tables).
  • Using summary tables and visual displays to profile data.

  • Numerical descriptors for profiling sample data.
  • Measures of central and non-central location.
  • Measuring variability and dispersion.
  • Examining distributions (skewness, bimodal patterns).
  • Analysing relationships between numerical measures.
  • Breakdown analysis of key managerial indicators.

  • Foundations of statistical inference.
  • Quantifying uncertainty – the normal distribution.
  • The importance of sampling and sampling methods.
  • Understanding the sampling distribution.
  • Confidence interval estimation and interpretation.
  • Applied example.

  • Rationale and process of hypothesis testing.
  • Type I and Type II errors.
  • Single-population tests (single mean tests).
  • Two independent population mean tests.
  • Matched-pairs test situations.
  • Comparing means across multiple populations (managerial ANOVA).

  • Exploiting statistical relationships for prediction.
  • Building regression-based models.
  • Model evaluation and interpretation.
  • Overview of data mining – descriptive vs. predictive.
  • Managerial applications of data mining.
  • Reporting analytical insights for management action.