Data Literacy Training Course

Duration 5 days 10 modules · 33 contact hours
Level Intermediate Certificate on completion
Who attends Managers and Team Leaders, Business Analysts and others
Fee from USD 1,100 KSh 95,000 for Kenyan organisations
Where 52 cities + online See dates by city
Next start 16 Nov 2026 Course code SD-DA-081

Before you join: Participants should be comfortable reading tables, percentages, charts and routine business reports.

Dates & prices

Two start dates a month in every city, and live online on weekdays or weekends. Fees are per participant; classroom fees include tuition, materials, lunch and refreshments, and the certificate.

Suggest a date or city

Showing the next six months. Later dates, other cities and private deliveries for your team are available on request.

Course overview

Organisations collect transaction, customer, operational, financial and service data faster than many teams can assess it. Managers and specialists are often asked to act on dashboards, reports and AI-generated summaries without being able to test source quality, interpret statistics, question assumptions or identify misleading visualisations. This creates costly risks: weak performance decisions, incorrect comparisons, poorly specified KPIs, privacy failures and unsupported claims presented as evidence.

This course builds the practical data literacy required to use data responsibly in day-to-day decisions. Participants learn how data is structured, where quality failures arise, how to interpret descriptive statistics and uncertainty, and how to define useful measures. They assess charts, dashboards and spreadsheet outputs; distinguish correlation from causation; identify bias; and communicate findings with clear limitations. The course also addresses data governance, access controls, personal data, fairness and responsible use of AI-generated analysis.

Teaching combines concise instructor briefings with realistic cases drawn from banking, public services, commercial operations, customer service and programme delivery. Participants examine flawed reports, test data claims, work with tables and dashboards, and discuss decisions with incomplete or conflicting evidence. They leave with a reusable data-literacy toolkit containing a question-to-metric canvas, data-quality issue log, dashboard review checklist, KPI specification template, evidence-based recommendation structure and personal application plan. The course is delivered over five days in the classroom in 50+ cities or live online, with approximately 6.5 contact hours each day. Participants receive a certificate of completion.

The course suits professionals who consume, commission, communicate or govern data rather than build advanced statistical models. It is particularly useful where teams need a shared standard for interpreting evidence before decisions are approved, reported or escalated.

What you will be able to do

  • Frame business questions using decision, measure, segment, period and comparison criteria
  • Assess data sources using metadata, granularity, lineage and quality dimensions
  • Apply data-quality checks for completeness, validity, consistency, duplicates and outliers
  • Interpret descriptive statistics, distributions, rates, correlations and confidence intervals
  • Specify KPIs using formulas, targets, owners, refresh cycles and documented caveats
  • Evaluate charts and dashboards for accuracy, accessibility, relevance and misleading design
  • Communicate evidence-based recommendations using assumptions, limitations and uncertainty statements
  • Create a workplace data-literacy action plan for a priority decision or reporting process

Benefits of attending

For you

  • Greater confidence challenging unsupported figures, misleading charts and vague data claims
  • A repeatable method for turning business requests into measurable analytical questions
  • Stronger ability to interpret dashboards before acting on reported performance changes
  • Practical language for discussing uncertainty, bias, assumptions and data limitations
  • A documented plan for improving data use in a current workplace decision

For your organisation

  • Fewer decisions based on incomplete, inconsistent or poorly defined management information
  • More consistent KPI definitions across functions, reports and performance reviews
  • Earlier identification of data-quality defects before they affect reporting or customer decisions
  • Improved governance of personal, confidential and restricted data
  • Clearer recommendations that connect evidence, uncertainty and accountable actions

Course modules

10 modules over 5 days

Participants can frame a decision-focused data question and identify the evidence needed to answer it.

  • Data literacy roles: producer, steward, analyst, decision-maker and data consumer
  • Decision framing distinguishes descriptive, diagnostic, predictive and prescriptive analytics questions
  • Translate business objectives into measurable questions, decisions, and success criteria
  • Structured, semi-structured and unstructured data matched to appropriate uses
  • Data lifecycle from collection through retention, archival, and disposal
  • Common data misconceptions: correlation, averages, rankings, and single-source claims
  • Stakeholder data needs mapped using decision, audience, frequency, and format

Participants can explain how data is organised and assess whether sources are suitable for a stated question.

  • Relational tables, records, fields, keys, and relationships explained accurately
  • Primary and foreign keys tested for entity matching and joins
  • Data dictionaries document field definitions, units, formats, owners, and refresh cycles
  • Metadata distinguishes business definitions from technical schema and lineage
  • Granularity assessed before combining daily, monthly, customer, and transaction data
  • Master data and reference data separated from transactional records
  • Source systems assessed for collection method, coverage, and known limitations

Participants can profile common data-quality problems and record appropriate remediation actions.

  • Accuracy, completeness, consistency, timeliness, validity, and uniqueness quality dimensions
  • Profile distributions, null rates, duplicates, outliers, and invalid category values
  • Reconciliation checks compare totals, record counts, and control totals
  • Sampling bias, selection bias, survivorship bias, and measurement error identified
  • Root-cause analysis separates data-entry defects from process and system failures
  • Data-quality issue log records severity, owner, impact, and remediation date
  • Quality thresholds aligned with reporting purpose and decision risk

Participants can interpret common descriptive statistics and state the uncertainty behind reported results.

  • Mean, median, mode, range, variance, and standard deviation interpreted correctly
  • Distribution shape examined using histograms, skewness, and percentile measures
  • Rates, ratios, percentages, and index numbers calculated with valid denominators
  • Base rates and denominators checked before comparing groups or periods
  • Confidence intervals communicate uncertainty around estimates drawn from samples
  • Correlation coefficients interpreted without inferring causation or operational drivers
  • Statistical significance distinguished from practical significance and materiality

Participants can define KPIs that are traceable to decisions, formulas, data sources and accountable owners.

  • Analytical questions classified by decision, measure, segment, period, and comparison
  • KPIs specified with formula, target, owner, refresh frequency, and caveats
  • SMART measures tested against strategic objectives and controllable outcomes
  • Leading and lagging indicators balanced within performance measurement
  • Segmentation exposes meaningful differences hidden by enterprise-wide averages
  • Time-series comparisons separate trend, seasonality, cycle, and exceptional events
  • Metric trees connect operational drivers to outcome measures

Participants can select, review and challenge visualisations used in reports and dashboards.

  • Chart selection matches comparison, composition, distribution, relationship, or trend questions
  • Bar, line, scatter, histogram, and box plots used appropriately
  • Axis scales, zero baselines, and ordering checked for misleading effects
  • Colour, labels, annotations, and whitespace direct attention without distortion
  • Dashboard layouts prioritize decisions, exceptions, and drill-down paths
  • Small multiples reveal segment patterns while preserving comparable scales
  • Accessibility checks cover colour contrast, labels, and nonvisual interpretation

Participants can use common spreadsheet and dashboard features to inspect, summarise and validate business data.

  • Spreadsheet tables structured for filtering, sorting, validation, and reproducible analysis
  • PivotTables summarise measures by category, period, geography, and customer segment
  • XLOOKUP and INDEX-MATCH reconcile related tables using verified keys
  • Conditional formatting highlights thresholds, exceptions, duplicates, and missing values
  • Calculated fields document formulas, assumptions, and source references
  • Dashboard filters tested against intended audience questions and decision rights
  • Refresh routines record source date, transformation steps, and version status

Participants can identify governance, privacy, fairness and AI risks before data is used for decisions.

  • Data owners, stewards, custodians, and users assigned accountable responsibilities
  • Data classification distinguishes public, internal, confidential, and restricted information
  • Purpose limitation and data minimisation applied before collecting personal data
  • Role-based access controls restrict sensitive datasets according to business need
  • Retention schedules balance regulatory obligations, operational value, and disposal requirements
  • Fairness risks assessed in data collection, segmentation, and automated decisions
  • AI-generated analysis checked for provenance, privacy, bias, and factual errors

Participants can communicate data findings, limitations and recommendations clearly to decision-makers.

  • Claim-evidence-reasoning structure turns analysis into defensible decision recommendations
  • Executive messages state the decision, evidence, uncertainty, and requested action
  • Assumptions, limitations, and confidence levels disclosed without obscuring conclusions
  • Counterfactual questions test whether observed changes plausibly explain outcomes
  • Data storytelling connects audience context, visual evidence, and operational implications
  • Challenge questions probe source quality, definitions, comparators, and causal claims
  • Recommendations distinguish immediate actions, further analysis, and monitoring measures

Participants can create a structured plan for applying data-literacy methods to a workplace decision or report.

  • Priority decision identified with stakeholders, required evidence, and decision deadline
  • Data inventory maps sources, owners, access conditions, and quality risks
  • Question-to-metric canvas defines measures, segments, baselines, and thresholds
  • Analysis workflow sequences extraction, validation, interpretation, communication, and review
  • Stakeholder review plan assigns feedback points, approvals, and escalation routes
  • Personal data-literacy action plan sets workplace applications and milestones
  • Reusable checklist prepares future analyses for publication and decision use

Exercises and examples are adapted to each cohort's sectors and roles.

What you take back to work

  1. Question-to-metric canvas
  2. Data-quality issue log template
  3. KPI specification template
  4. Dashboard review checklist
  5. Claim-evidence-reasoning recommendation model
  6. Personal data-literacy application plan

Who should attend

  • Managers and Team Leaders — make operational decisions from reports, dashboards and performance measures
  • Business Analysts — translate stakeholder questions into reliable measures and defensible findings
  • Project and Programme Managers — assess delivery data, benefits measures and status reporting
  • Finance Professionals — test management information, assumptions and performance comparisons
  • Operations and Service Managers — identify data-quality issues affecting service and process decisions
  • Policy, Risk and Compliance Professionals — evaluate evidence, governance controls and data-related claims

Skills and tools covered

Data quality assessmentStatistical interpretationKPI specificationDashboard evaluationData governanceEvidence communication

Tools and standards

CRISP-DM, FAIR Data Principles, ISO 8000, Data Quality Dimensions

How the course runs

Training methodology

The course uses short instructor briefings to introduce methods, followed by case work, table reviews, chart critiques and facilitated group exercises. Participants assess realistic reports and dashboards, identify defects in source data and measures, and practise explaining what evidence does and does not support. Spreadsheet and dashboard examples demonstrate common analysis patterns without requiring programming. Simulations focus on challenging a data claim, agreeing a KPI definition and presenting a recommendation to decision-makers. Individual application planning connects course methods to participants' own reporting, service or operational decisions.

Requirements and prerequisites

Participants should be comfortable reading tables, percentages, charts and routine business reports. Experience using spreadsheets, dashboards, case-management systems, ERP reports or performance packs is helpful because course discussions use realistic workplace data. Participants should bring familiarity with one or two decisions or reports from their own role that they would like to examine. No programming, SQL, data science, advanced mathematics, statistical software or prior analytics qualification is required. The course explains statistical concepts at the level needed to interpret and challenge business analysis.

A typical training day

08:30 – 10:30First session
10:30 – 10:45Refreshment break
10:45 – 12:30Second session
12:30 – 13:30Lunch and networking
13:30 – 15:00Third session
15:00 – 15:15Refreshment break
15:15 – 16:30Workshop and daily review

Live online deliveries follow the same structure in East Africa Time, with shorter screen blocks and longer breaks.

What the fee includes

  • Instruction by a practitioner facilitator
  • Full course workbook and materials
  • Exercise files, templates and case studies
  • Refreshments and lunch on classroom deliveries
  • Certificate of completion
  • Post-course application plan

Frequently asked questions

You should be comfortable reading business reports, tables, percentages and charts. Programming, SQL, advanced statistics and data science experience are not required.

Bring a laptop if attending in person and any non-confidential report, dashboard or decision question from your role. Course materials and sample data are provided, so you do not need specialist software.

It is designed for managers, business specialists, project staff, finance professionals, service leaders and governance professionals who use or commission data. It is not limited to people with analyst job titles.

This course focuses on interpreting, questioning, communicating and governing data for better decisions. It does not train participants to build machine-learning models, write code or conduct advanced predictive modelling.

Participants use reusable templates to define a decision question, assess source quality, specify measures and review a dashboard or report. The final module produces an action plan linked to a current workplace decision or reporting process.

Each day runs from 08:30 to 16:30 and includes about 6.5 contact hours. Time is divided between instructor input, case analysis, group discussion, practical review of reports and dashboards, and application planning.

Questions about this course?

Ask about dates, fees, group bookings or running it for your team. Our training team replies by email.

Ask on WhatsApp info@skillsetdevelopment.com

More courses in Data Analytics

Data Analytics Data Cleaning and Validation Using SPSS Training Course 10 days · from USD 2,200 · next 16 Nov Data Analytics Data Governance Frameworks Training Course 10 days · from USD 2,200 · next 16 Nov Data Analytics Data Governance Training Course 10 days · from USD 2,200 · next 16 Nov Data Analytics Data Governance and Compliance Training Course 10 days · from USD 2,200 · next 16 Nov Data Analytics Data Management and Visualisation with Excel Training Course 5 days · from USD 1,100 · next 16 Nov Data Analytics Data Storytelling and Visualization Training Course 5 days · from USD 1,100 · next 16 Nov Data Analytics Data Strategy Development Training Course 5 days · from USD 1,100 · next 16 Nov Data Analytics Data Visualisation and Reporting Training Course 10 days · from USD 2,200 · next 16 Nov