Data Science Fundamentals for Business Professionals Training Course

Course overview

Business teams increasingly receive dashboards, predictive scores, customer segments and AI-generated recommendations, yet many professionals cannot test whether the underlying data is fit for purpose or whether a model answer supports a sound decision. This creates avoidable risk: leaders may act on biased samples, confusing metrics, weak forecasts or correlations presented as causes. This course gives business professionals a practical foundation for specifying, evaluating and using data science work without needing to become full-time data scientists.

Participants learn the end-to-end data science workflow, from translating a business question into measurable variables and success criteria through data preparation, exploratory analysis, basic statistical reasoning, modelling concepts and results communication. They use Excel, SQL, Python notebooks and Power BI to inspect datasets, calculate descriptive measures, identify missing and anomalous data, query relevant records, visualise patterns and interpret classification and forecasting outputs. The course also addresses model performance measures, data governance, privacy, bias and the questions business sponsors should ask before approving a data-driven initiative.

Teaching combines instructor-led explanation with guided analysis of a realistic commercial dataset. Participants work in small groups to frame a business problem, assess data quality, build an evidence pack and present a decision recommendation. They leave with a completed business data science project brief, an analysis workbook, a dashboard prototype and a model evaluation checklist that can be adapted for their own initiatives.

The course is suited to managers, analysts and subject-matter specialists who commission, consume or contribute to analytics and data science projects. It is particularly valuable for professionals who need to bridge conversations between operational teams, data analysts, data engineers and senior decision-makers.

Course objectives

By the end of this course, participants will be able to:

• Frame a business problem as a testable data science question with measurable success criteria
• Profile a dataset for completeness, validity, duplicates, outliers and potential sources of bias
• Query business data with SQL filters, joins, aggregations and grouped calculations
• Create exploratory charts and descriptive statistics that reveal patterns, variation and exceptions
• Interpret correlation, sampling, confidence intervals and hypothesis tests without overstating conclusions
• Evaluate classification and forecasting outputs using confusion matrices, precision, recall and error measures
• Communicate an evidence-based recommendation through a Power BI dashboard and executive narrative
• Produce a data science project brief covering scope, data requirements, risks, governance and expected value

Benefits of attending

Target competencies

Who should attend

• Business Analysts — who translate operational needs into data requirements and decision-ready insight
• Operational Managers — who need to challenge performance reports, forecasts and automated recommendations
• Product Managers — who prioritise data-enabled product decisions and define measurable customer outcomes
• Project Managers — who coordinate analytics initiatives and must manage scope, dependencies and delivery risk
• Marketing and Customer Insights Professionals — who use segmentation, campaign data and customer behaviour analysis
• Finance and Commercial Professionals — who assess forecasts, margin drivers and business cases supported by data

Requirements and prerequisites

Participants should be comfortable using spreadsheets for sorting, filtering and basic formulas, and should be able to discuss the measures used in their own business area, such as revenue, cost, service level or conversion rate. Familiarity with charts and percentages is helpful. No prior programming, SQL, statistics qualification, machine learning experience or Power BI expertise is required; coding activities are guided and use prepared notebooks. This is a fundamentals course at an intermediate professional level: complete beginners to data science should expect structured practice, while experienced analysts will gain a stronger business framing and governance lens.

Training methodology

Instructor-led sessions introduce each stage of the data science lifecycle using business decisions rather than abstract theory. Participants then work with a realistic commercial dataset in Excel, PostgreSQL, Jupyter Notebook and Power BI, completing guided data checks, queries, visual analysis and model-result interpretation. Short case discussions examine flawed metrics, biased data and poorly scoped analytics requests. Group workshops require participants to defend a recommendation to a business sponsor. On the final day, each participant adapts the project brief and evaluation checklist into an application plan for a current workplace opportunity.

Course outline

Day 1: Business questions, data and decision value

• The data science lifecycle from business problem to operational decision
• Problem framing with SMART analytical questions and decision statements
• Defining target variables, features, units of analysis and time horizons
• Selecting business KPIs, baseline measures and success thresholds
• Data sources, data ownership and data lineage mapping
• Structured, semi-structured and unstructured business data
• Data governance, privacy and responsible data use principles

Workshop: Participants convert a commercial problem into a data science project charter with decision owner, target measure, data sources, constraints and success criteria.

Day 2: Data preparation and exploratory analysis

• Dataset profiling for data types, ranges, formats and cardinality
• Missing-value patterns and practical treatment options
• Duplicate detection, invalid records and data consistency checks
• Outlier identification using box plots, z-scores and business rules
• Descriptive statistics including mean, median, spread and percentiles
• Exploratory visualisation with distributions, trends and category comparisons
• Correlation analysis and the distinction between association and causation

Workshop: Using Excel and a Jupyter Notebook, participants produce a data quality report and exploratory findings pack for a customer dataset.

Day 3: Querying and analysing business data

• Relational data concepts: tables, keys, relationships and grain
• SQL SELECT statements, aliases and calculated fields
• Filtering records with WHERE, IN, BETWEEN and date conditions
• Aggregation with COUNT, SUM, AVG, GROUP BY and HAVING
• INNER JOIN and LEFT JOIN techniques for combining business tables
• Cohort, segment and period-over-period analysis patterns
• Sampling, confidence intervals and hypothesis-testing logic

Workshop: Participants write SQL queries to identify customer segments, calculate retention measures and test a campaign performance claim.

Day 4: Models, predictions and responsible interpretation

• Supervised and unsupervised learning use cases in business
• Classification, regression, clustering and forecasting concepts
• Training, validation and test datasets
• Overfitting, underfitting and data leakage risks
• Confusion matrices, precision, recall, F1 score and ROC concepts
• Forecast error measures including MAE, RMSE and MAPE
• Bias, fairness, explainability and human oversight in model use

Workshop: Participants interpret two model scorecards, identify deployment risks and recommend the model approach that best fits a stated business decision.

Day 5: Communicating insight and planning application

• Selecting visuals for comparison, trend, distribution and relationship questions
• Building Power BI measures, slicers and interactive report pages
• Dashboard design for executive decisions and operational action
• Writing findings, limitations and recommendations for non-technical audiences
• Data storytelling with context, evidence, uncertainty and next actions
• Model monitoring triggers and post-deployment review measures
• Data science project scoping, stakeholder roles and delivery roadmap

Workshop: Participants create a Power BI decision dashboard and present a final recommendation, project brief and workplace application plan to a sponsor panel.

Tools & standards covered

Microsoft Excel, Jupyter Notebook, PostgreSQL, Microsoft Power BI

A typical training day

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

What the fee includes

How you can take this course

Certification

Participants who complete the full five days receive the Skillset Development Certificate of Completion, stating the course title, course code, dates and delivery format — suitable for professional-development records and employer reimbursement.

Frequently asked questions