Python for Data Analysis

Programme Summary

Reproducible analysis requires clear code, reliable data preparation and checks that prevent misleading results. This programme develops practical Python skills for working with tabular business data. Participants clean and combine datasets, calculate meaningful summaries and create visual outputs, then deliver a documented notebook that explains assumptions, validates results and can be rerun consistently.

Training Objectives

1 Strengthen Python workflows for tabular business data; 2 Improve data cleaning joining and transformation skills; 3 Develop reliable aggregation and exploratory analysis; 4 Support clear visualisation and interpretation of results; 5 Build reproducible notebooks with validation checks.

Learning Outcomes

1 Import and inspect tabular data correctly; 2 Clean missing duplicate and inconsistent records; 3 Join datasets without unintended row multiplication; 4 Calculate and validate business analysis summaries; 5 Produce a clear reproducible analytical notebook.

Target Audience

Data Analysts; Business Analysts; Reporting Analysts; Operations Analysts; Finance Analysts.

Target Industries

Retail; Manufacturing; Logistics; Financial Services; Telecommunications; Professional Services.

Course Contents

• Module 1 Analytical Workflow: Business questions and expected outputs; Reproducible notebooks and code organisation; Data permissions and analysis boundaries.
• Module 2 Dataframes and Import: Tabular structures indexes and data types; Reading common business file formats; Initial inspection and schema checks.
• Module 3 Selection and Filtering: Selecting rows columns and subsets; Boolean conditions and missing value behaviour; Copying modifying and preserving source data.
• Module 4 Data Quality Review: Duplicates missing values and invalid entries; Range consistency and category checks; Recording issues and treatment decisions.
• Module 5 Cleaning and Transformation: Type conversion text cleaning and dates; Derived fields and conditional calculations; Avoiding silent coercion and information loss.
• Module 6 Joining Data: Keys cardinality and relationship assumptions; Inner outer and left join behaviour; Validating matched unmatched and duplicate records.
• Module 7 Aggregation and Reshaping: Grouping summarising and pivoting data; Denominators counts and weighted calculations; Reshaping datasets for analysis requirements.
• Module 8 Time Based Analysis: Date parsing periods and resampling; Trends rolling measures and comparisons; Missing periods seasonality and context.
• Module 9 Visual Exploration: Selecting charts for analytical questions; Clear labels scales and uncertainty notes; Identifying patterns without unsupported causation.
• Module 10 Functions and Validation: Reusable analysis steps and helper functions; Assertions reconciliations and reasonableness checks; Handling errors and documenting assumptions.
• Module 11 Communication and Export: Clear findings caveats and business relevance; Exporting tables charts and reusable outputs; Protecting sensitive information in deliverables.
• Module 12 Analysis Notebook Workshop: Cleaning and combining supplied datasets; Producing validated analytical summaries; Presenting a reproducible notebook and findings.

Assessment

• 60 single best answer questions, including applied scenarios; one mark per question.
• 90 minute invigilated digital examination with identity verification. Approved accessibility adjustments preserve the assessed competence.
• Supplied reference pack permitted; external internet and AI assistance prohibited.
• Individual applied assessment (45 minutes): Clean and join supplied datasets, calculate validated business summaries and submit a reproducible notebook with a chart and concise interpretation.
• Assessment criteria: Accuracy of data preparation and joins; Correctness of calculations and validation checks; Reproducibility and clarity of analytical communication.
• Pass each component separately: 45/60 written marks (75%) and at least 70% on the applied task.
• Individual results and feedback are provided. One reassessment may follow coaching under the published enrolment terms.

Certification

• Successful learners receive a TQCSI Arabia digital Certificate of Successful Completion.
• The certificate records the learner name, exact programme title, teaching hours, completion date and unique verification reference.
• Verified identity, full attendance and separate written and applied passes are required; all mandatory critical criteria must be met.
• Proposed issue target: within 30 calendar days of approved results and settlement of applicable fees.
• Records assessed Python data analysis skills; it does not confer software vendor certification or authority to access restricted data.
• Attendance confirmation may record completed hours for unsuccessful learners; it does not certify successful completion.

Programme Profile

Number of daysNumber of daysNumber of days

333

Total teaching hoursTotal teaching hoursTotal teaching hours

18 hours; breaks, pre course work and formal final assessments excluded.18 hours; breaks, pre course work and formal final assessments excluded.18 hours; breaks, pre course work and formal final assessments excluded.

Delegates allowedDelegates allowedDelegates allowed

Maximum 16 delegates.Maximum 16 delegates.Maximum 16 delegates.

Training prerequisitesTraining prerequisitesTraining prerequisites

Basic Python syntax, variables, functions and simple loops are required. Spreadsheet analysis experience is recommended; introductory programming is not covered in full.Basic Python syntax, variables, functions and simple loops are required. Spreadsheet analysis experience is recommended; introductory programming is not covered in full.Basic Python syntax, variables, functions and simple loops are required. Spreadsheet analysis experience is recommended; introductory programming is not covered in full.

Pre course assignment and assessmentPre course assignment and assessmentPre course assignment and assessment

Complete a 30 minute Python diagnostic and check access to the supplied notebook environment.Complete a 30 minute Python diagnostic and check access to the supplied notebook environment.Complete a 30 minute Python diagnostic and check access to the supplied notebook environment.

End of course assessmentEnd of course assessmentEnd of course assessment

60 question written exam (90 minutes) and individual applied task (45 minutes).60 question written exam (90 minutes) and individual applied task (45 minutes).60 question written exam (90 minutes) and individual applied task (45 minutes).

Passing criteriaPassing criteriaPassing criteria

75% written and 70% applied, passed separately; full attendance.75% written and 70% applied, passed separately; full attendance.75% written and 70% applied, passed separately; full attendance.