Data Analyst

Program

Data Analyst

Turn spreadsheets and queries into clear answers

Learn Excel, SQL, and visualization fundamentals to analyze data and communicate findings.

8

Weeks

40

Chapters

4

Practice formats

1

Portfolio artifact

8 weeks — one module per week (8 modules, 40 chapters), each week ending with a live class every Saturday, plus a module exam before the next module opens.

Curriculum

40 chapters across 8 modules — each with guided notes, a live example, a quiz, an exercise, a challenge, and class practice.

Module 1 · DATA ANALYTICS FOUNDATIONS

  1. 1Introduction to Data AnalyticsWhat data analysts do, the six phases of analysis, the analyst's toolkit, and thinking like an analyst.Learn →
  2. 2Data Types, Sources & QualityStructured and unstructured data, data types, first/second/third-party sources, data quality dimensions, and privacy.Learn →
  3. 3Business Questions, Metrics & KPIsAsking SMART questions, metrics versus KPIs, dimensions and measures, and planning an analysis with stakeholders.Learn →
  4. 4Analytical Thinking & Metric TreesBreaking big questions into smaller ones, hypothesis-driven analysis, metric trees that decompose a KPI into drivers, and explaining why a number changed.Learn →
  5. 5Data Ethics, Privacy & Responsible AnalysisPersonal data and privacy laws, anonymisation and aggregation, bias in data and metrics, honest presentation, and ethical decisions analysts face.Learn →

Module 4 · VISUALIZE & COMMUNICATE

  1. 16Data VisualizationChoosing the right chart, design principles, color and accessibility, and avoiding misleading visuals.Learn →
  2. 17Dashboards with Power BIThe Power BI workflow, Power Query, data models and relationships, DAX measures, and designing effective dashboards.Learn →
  3. 18Data Storytelling & PresentingKnowing your audience, structuring a data story, writing insights and recommendations, and presenting with confidence.Learn →
  4. 19Building KPI Dashboards in ExcelDashboard layout, KPI tiles, target vs. actual, RAG status, year-to-date calculations, dynamic labels, and keeping dashboards maintainable.Learn →
  5. 20The BI Tools Landscape: Power BI, Tableau & Looker StudioHow the main BI tools compare, shared concepts (data sources, models, measures, filters), choosing a tool, and transferring skills between them.Learn →

Module 5 · ON THE JOB: SALES & MARKETING ANALYTICS

  1. 21On the Job: Sales Performance ReviewAnswer "why are sales down?" — year-over-year comparisons, price/volume/mix decomposition, contribution analysis, and a one-page sales review.Learn →
  2. 22On the Job: Campaign & Funnel AnalysisEvaluate a marketing campaign — funnel conversion rates, channel comparison, CAC and ROAS, attribution caveats, and a campaign post-mortem.Learn →
  3. 23On the Job: Customer Segmentation with RFMGroup customers by recency, frequency and monetary value to target retention and loyalty campaigns — scoring, segment naming, and actions per segment.Learn →
  4. 24On the Job: Pricing & Discount AnalysisMeasuring discount depth and its effect on revenue and margin, price bands with CASE, list vs. net price, and recommending discount guardrails.Learn →
  5. 25On the Job: Marketing ROI & AttributionCPA, ROAS and blended metrics, attribution models (last-click, first-click, linear, data-driven), their biases, and recommending budget shifts.Learn →

Module 6 · ON THE JOB: PRODUCT & OPERATIONS ANALYTICS

  1. 26On the Job: Cohort & Retention AnalysisInvestigate "are we keeping our customers?" — cohorts, retention curves, churn rates, and turning a retention table into decisions.Learn →
  2. 27On the Job: Reading an A/B TestAnalyse an experiment the product team ran — hypotheses, sample size, conversion lift, confidence intervals, significance, and the pitfalls that fake a win.Learn →
  3. 28On the Job: Operations & Supply Chain AnalyticsSupport an operations team — inventory turnover and days of stock, stock-outs, on-time delivery, service levels, forecasting demand, and an exceptions report.Learn →
  4. 29On the Job: Product Usage & Engagement MetricsEvent data, DAU/WAU/MAU and stickiness, feature adoption, activation, and turning usage data into product decisions.Learn →
  5. 30On the Job: Demand Forecasting BasicsNaive and moving-average forecasts, seasonality, forecast error (MAPE), choosing a method, and communicating forecasts with ranges.Learn →

Module 7 · ON THE JOB: WORKING AS AN ANALYST

  1. 31Handling Stakeholder Requests & RequirementsTurn ad-hoc requests into scoped work — intake questions, requirement documents, prioritising a request queue, managing expectations, and saying no well.Learn →
  2. 32Automating Recurring Reports with PythonReplace a manual weekly report — reproducible scripts, parameters, validation checks, scheduled refreshes, and exporting to Excel or email.Learn →
  3. 33Metric Definitions, Documentation & Data GovernanceStop "two numbers for the same thing" — metric definitions, data dictionaries, a single source of truth, access rules, and reconciling conflicting reports.Learn →
  4. 34Data Quality Checks & ReconciliationDimensions of data quality, automated checks (completeness, uniqueness, validity), reconciling a warehouse against the source system, and reporting data issues.Learn →
  5. 35On the Job: HR & Finance AnalyticsHeadcount, attrition and time-to-hire for HR; budget vs. actual and variance for finance; definitions that matter; and handling sensitive people data.Learn →

Module 8 · CAPSTONE & CAREER LAUNCH

  1. 36Capstone: Analytics Case StudyAn end-to-end case study: framing, cleaning, analysis with SQL/Excel/Python, a dashboard, and a presented recommendation.Learn →
  2. 37Portfolio & Interview PreparationLand the job — a focused portfolio, the analyst CV, SQL and case interviews, take-home assignments, and talking about your projects with impact.Learn →
  3. 38Job Search Strategy for Data AnalystsTarget roles and titles, reading job ads, tailoring CVs to keywords, networking, applications tracking, and turning rejections into feedback.Learn →
  4. 39Technical Interview Practice: SQL & ExcelWhat technical screens look like, thinking aloud, common SQL patterns (aggregation, HAVING, subqueries, joins), Excel tasks, and checking your own answers.Learn →
  5. 40Your First 90 Days as a Data AnalystStart strong in a new analyst role — learning the business and data, quick wins, building relationships, working habits, and planning your growth.Learn →
Start learning →

Delivery

Blended — self-paced lessons plus weekly live sessions

Portfolio outcome

A written analysis report answering a real business question, backed by SQL queries and charts.

Certificate

Certificate of completion, verifiable online with its credential code.

By the end of this program, you'll be job-ready to…

  • Turn a vague business question into a concrete data question
  • Query, clean, and summarize data with SQL and spreadsheets
  • Present findings clearly to a non-technical audience

Salary expectations

Estimated U.S. national average annual salary by experience level.

$60,000

Entry-level

$85,000

Mid-level

$115,000

Senior-level

Estimated figures for planning purposes — actual pay varies by location, employer, and experience.

Tools you'll master

ExcelSQLPower BIPython (pandas)

Practical training, every chapter

  • A live "try it yourself" example in every chapter you can run immediately
  • A hands-on exercise per chapter, with hints and a reveal-able answer
  • An open-ended challenge per chapter, graded against real success criteria
  • Class practice prompts for pairs or cohorts, not just solo homework

Why take this program

Interview- and job-ready

Built to the same bar Aicloud actually hires against — not a generic syllabus.

Tier 1 company partners

Program partnerships with leading employers are in progress — see Affiliation for status.

Job connection access

Graduates get access to Aicloud's own hiring pipeline and employer referral network.

Professional networking

Cohort discussion boards and live sessions connect you with peers and mentors, not just content.

A real portfolio

You leave with a concrete, explainable artifact — proof of knowledge, not just a certificate.