Specialist Preparation Program in Business Data Analytics

Specialist Preparation Program in Business Data Analytics is a free program on ARPS, the Arab Platform for Professional Studies. A bilingual applied program for turning business questions into calculable, reviewable analysis. The program moves from decision framing and data grain through quality, descriptive analysis, experiments, forecasts and a reproducible decision memo.

A data dashboard interface Studying is completely free
Estimated time
15.5 hours
subjects
5
lessons
20
Study language
Arabic and English

Optional certificate fee 50% launch discount

$90 instead of $180

331 AED or 338 SAR

Start the first subject free Fees by route

Program facts

Study fee
Free
subjects
5
lessons
20
Estimated time
15.5 hours
Content last reviewed
Access period
30 days
Study language
Arabic and English

About the program

The program moves from decision framing and data grain through quality, descriptive analysis, experiments, forecasts and a reproducible decision memo.

Practical relevance

Lessons use fictional cases, calculations and exercises with explicit outputs and checks.

Why this program

Sound decisions require stable metric definitions, reconciled data and explicit inference limits.

Career relevance

Supports analytics work in operations, product, finance and service without claiming external professional accreditation.

What you will learn

  • Formulate an analytical question specifying the decision, population, period, comparison and limits of inference.
  • Construct a plan linking each hypothesis to data, checks and decisions, and state conclusions proportionate to evidence.
  • Produce a quality profile distinguishing defect types and their effects, and reconcile counts and values before and after treatment.
  • Build an ordered transformation sequence and test data types, aggregation and reproducibility.
  • Calculate and select measures of center and spread suited to the decision, explaining tails and extreme-value effects.
  • Decompose value change into volume, price or cost, and mix effects, reconciling components to the total difference.
  • Compute an approximate interval for a mean and explain how sample size and spread affect it.
  • Create a temporal split and baseline, compare MAE, and avoid future leakage.
  • Choose a visual encoding for the question and write a message linking evidence to a bounded decision.
  • Integrate question, quality, analysis, uncertainty, recommendation and monitoring in a defensible memo.
Learning outcomes (20)
  1. Formulate an analytical question specifying the decision, population, period, comparison and limits of inference.
  2. Build a metric specification, calculate a weighted rate, and distinguish percentage change from percentage-point change.
  3. Identify row grain, keys and time units, and detect incompatible levels before aggregation.
  4. Construct a plan linking each hypothesis to data, checks and decisions, and state conclusions proportionate to evidence.
  5. Produce a quality profile distinguishing defect types and their effects, and reconcile counts and values before and after treatment.
  6. Compare treatments for missing and extreme values, preserving their effects on measures rather than using unjustified deletion or imputation.
  7. Choose a join type, inspect multiple and missing matches, and reconcile the result before use.
  8. Build an ordered transformation sequence and test data types, aggregation and reproducibility.
  9. Calculate and select measures of center and spread suited to the decision, explaining tails and extreme-value effects.
  10. Separate within-segment performance changes from mix changes using fixed weights and reconciled rates.
  11. Build cohort and funnel tables preserving start dates, maturity windows and transition denominators.
  12. Decompose value change into volume, price or cost, and mix effects, reconciling components to the total difference.
  13. Compute an approximate interval for a mean and explain how sample size and spread affect it.
  14. Design a simple randomized experiment and compute an effect difference while checking balance and interference.
  15. Interpret correlation and simple regression without turning association into causality or unsupported extrapolation.
  16. Create a temporal split and baseline, compare MAE, and avoid future leakage.
  17. Choose a visual encoding for the question and write a message linking evidence to a bounded decision.
  18. Create a run record and layered reconciliation that reveals where counts or amounts changed.
  19. Design a handoff package with minimum necessary data, access, retention and clear responsibilities.
  20. Integrate question, quality, analysis, uncertainty, recommendation and monitoring in a defensible memo.

Who it is for

  • Business and operations practitioners who need disciplined analysis that supports specific decisions rather than generic reports.
  • Professionals in finance, sales, human resources and services who need reproducible metrics and evidence-proportionate interpretations.
  • Early-career analysts who want a method linking question, quality, analysis and recommendation in a single piece of work.

Prerequisites

  • Basic familiarity with percentages, averages, reading tables and calculating simple differences.
  • Practical use of spreadsheets, including sorting, filtering, basic formulas and pivot tables.
  • General understanding of the business processes whose data will be analysed, and willingness to document assumptions, inference limits and data protection.

Curriculum

  1. Subject 1 Open for free

    Decision framing and measurement

    Define questions, metrics, row grain and inference plans before analysis.

    4 lessons · 2.5 hours

    1. Turning a request into an answerable analytical question 40 min
    2. Defining metrics and their denominators 40 min
    3. Data grain and the data dictionary 40 min
    4. Designing an analysis plan and bounding conclusions 40 min
  2. Subject 2

    Preparing trustworthy data

    Check quality, missingness, joins and transformations with reproducible reconciliation.

    4 lessons · 2.5 hours

    1. Checking completeness, validity and duplication 40 min
    2. Handling missing values and outliers 40 min
    3. Joining data without inflating results 40 min
    4. Reproducible transformation and aggregation 40 min
  3. Subject 3

    Descriptive and diagnostic analysis

    Integrated application of the module skills with reviewable calculations and limits.

    4 lessons · 2.5 hours

    1. Center, spread and distribution shape 38 min
    2. Fair comparison and changing segment mix 38 min
    3. Cohorts and conversion paths 38 min
    4. Analyzing revenue and cost change 38 min
  4. Subject 4

    Inference, experimentation and forecasting

    Integrated application of the module skills with reviewable calculations and limits.

    4 lessons · 2.5 hours

    1. Estimating uncertainty from a sample 38 min
    2. Designing a fair experiment 38 min
    3. Reading correlation and fitting a bounded regression 38 min
    4. Evaluating a time forecast against a baseline 38 min
  5. Subject 5

    Communicating decisions and operating analytics

    Integrated application of the module skills with reviewable calculations and limits.

    4 lessons · 2.5 hours

    1. Choosing a chart and building a decision message 38 min
    2. Reconciling a reproducible result 38 min
    3. Handing off an analysis with data protection 38 min
    4. Integrated analytical decision case 38 min

Subjects unlock in sequence as you progress.

Strengths

  • Twenty lessons with solved cases and exercises
  • Reconciled calculations and bounded inference
  • Bilingual reproducible handover

Evidence basis

Primary sources support core principles; cases and calculations are original instruction.

Methodology

Primary sources support core principles; cases and calculations are original instruction.

Assessment

Assessments follow the program blueprint and may use module assessments, a final assessment, or a combination, under the approved attempt policy.

Assessment attempts: 2 per assessment, 7 days apart

The pass mark is announced before the program is published.

Certificate

The credential confirms completion and successful assessment; it does not represent professional licensure unless explicitly and lawfully stated.

Every certificate has a public verification code. Verify a certificate

The real design with fictional details; opens in a new window.

Verification example: a real issued certificate

Program questions

Do I need a paid account to study?

You can solve the cases and output-review exercises using the supplied data without sending information to an external service. Trying a live tool is optional and subject to organizational policy and the tool's terms.

Does the program require advanced programming?

This scope does not depend on building a model or training a neural network. You should understand work tasks, read simple tables, compare results and write clear instructions.

Does this authorize me to approve a system launch?

You learn to prepare evidence and a recommendation within a specialist role. Launch approval and legal, security and privacy decisions remain with authorized organizational roles; this program does not confer independent professional authority.

Does prompt wording guarantee a correct answer?

No. You practice checking outputs, handling missing or conflicting evidence and recording errors, while distinguishing improvement on a limited sample from evidence about a defined use case.

How can I practice without using workplace data?

Use the fictional cases and supplied excerpts. When transferring the method to work, obtain the necessary authorization, use only the data needed for the task and check the relevant service settings.

How do exercises differ from summative assessments?

In exercises, write specifications, prompts, test plans and a handover, then compare them with guided feedback; this is ungraded self-practice. Six knowledge checks accompany the lessons. Three module assessments each select two applied cases, one per lesson. Automated scoring measures selection of decisions and plans appropriate to the facts; it does not alone establish proficiency in a live operational system.

Is Specialist Preparation Program in Business Data Analytics free, and do I get a certificate?

Yes. Specialist Preparation Program in Business Data Analytics is free on ARPS: every lesson, exercise and assessment is free, the first subject is open to read without an account, and the program is available in Arabic and English. Issuing a certificate is optional; it is a training completion certificate showing you passed the program's assessments, not a university degree, a professional licence or an endorsement by an external body.

What other names is Specialist Preparation Program in Business Data Analytics known by?

The subject of this program is also known as: Business Analytics, Business Data Analysis, Data Analytics for Business. Learners usually find it by searching for: free business analytics course, learn data analysis online, business data analysis for decisions, framing analytical questions.