IGNOU Data Analytics Career Path (Intermediate Track)

Jump to

Who this is for

• IGNOU learners and working professionals who already know basic SQL/BI and want to level up into higher-signal analyst work.
• Analysts who want product/business credibility (cohorts, funnels, retention, consistent KPI definitions).
• BI-focused learners who want to move beyond “charts” into semantic modeling, governance basics, and performance hygiene.
• Analytics-engineering curious learners who want modeling + dbt-style thinking without switching fully into data engineering yet.

Time required (realistic estimates)

• Fast track: 4–6 months (10–12 hrs/week) — focus on core deliverables and one BI tool.
• Standard pace: 6–9 months (6–10 hrs/week) — best for most learners.
• Busy schedule: 9–12 months (4–6 hrs/week) — steady progress without burnout.

Optional add-ons (only if they match your target roles)

• Power BI PL-300 prep: +4–8 weeks
• Extra BI tool (not recommended): +4–8 weeks
• Deeper statistics: +3–6 weeks (focus on practical experimentation)

Outcomes (what you can do after this path)

• Write advanced SQL using window functions and reusable patterns to support cohorts, funnels, and time comparisons.
• Deliver cohort + retention outputs with clear definitions that can be re-run and audited.
• Build a small analytics-ready model (facts/dimensions) with documented grain and relationships.
• Create a BI semantic model and dashboards that stay correct as filters and segments change.
• Produce a reproducible Python/R analysis (notebook/script + README) that re-runs on new data end-to-end.
• Design and interpret basic experiments (hypothesis, primary metric, guardrails, decision framing).
• Explain modern data stack concepts (warehouse + ELT + dbt-style transformations, tests, documentation).
• Apply confidently to intermediate analyst roles with a proof-based portfolio.

Roadmap

Portfolio (Intermediate Analytics Proof Pack)

Keep it simple: one featured project + 5 deliverables. This is enough to prove intermediate capability.

1) Featured Case Study (1 page)

• Business question: what you are trying to understand or improve
• Definitions: primary metric(s), timeframe, key segments
• What you built: dataset model + SQL analyses + dashboard + notebook
• Insights: 3–5 key findings (with numbers)
• Recommendation: 2–3 actions + risks/limitations
• Links: link to each deliverable below

2) Deliverables (5 total)

• Cohort retention analysis (SQL): cohort table + retention calculation + short notes on definitions.
• Funnel analysis (SQL): conversion steps + drop-off by segment (at least 1 segmentation).
• Small star schema: fact_events (or fact_orders) + 2–3 dimensions (dim_date, dim_user, dim_product).
• One dashboard page: KPIs + trend + one cohort view or one funnel view (keep it clean, not a multi-page report).
• Reproducible notebook/script: loads data → runs analysis → outputs tables/charts, with a short README.

Optional add-ons (only if your target roles need them)

• Performance refactor: improve one slow SQL query and explain what changed.
• Experiment report template: hypothesis → metric → results → decision.

Portfolio Rubric (Quick Self-Check)

If you can tick most items below, your portfolio is interview-ready for intermediate analyst roles.

Featured Case Study (1 page)

• Business question is clear in 2–3 sentences
• Metric definitions are explicit (what counts / what doesn’t)
• Includes 3–5 quantified insights (numbers, not opinions)
• Ends with 2–3 recommendations plus limitations
• Links to all 5 deliverables

1) Cohort retention (SQL)

• Cohort definition is clear (first event/date logic)
• Retention is calculated consistently across time periods
• Query is readable (CTEs, naming, comments)

2) Funnel (SQL)

• Steps are clearly defined and ordered
• Shows drop-off and conversion rate
• Includes at least one segmentation (e.g., channel, device, plan type)

3) Small star schema

• Fact table grain is stated (one row per what?)
• Dimensions join cleanly with consistent keys
• Model supports cohort/funnel metrics without rewriting logic everywhere

4) Dashboard (single page)

• Includes KPIs + trend and one deeper view (cohort or funnel)
• Filters do not break metric definitions
• Readable layout (not too many visuals)

5) Reproducible notebook/script

• Runs end-to-end on new data without manual steps
• README explains inputs, steps, outputs, and assumptions
• Exports at least one output (table/CSV/chart)

Final “Interview Ready” Test

• You can explain the project in 90 seconds
• You can defend your metric definitions and edge cases
• You can walk through one cohort insight and one funnel insight
• Everything is in one folder/page with consistent naming

Proof-of-work templates

Use these mini-templates to package your intermediate Data Analytics proof pack for resumes, portfolios, and interviews. Fill the inputs, then copy the output.

Built a reproducible [cohort/funnel] analysis for [product/domain] using advanced SQL (window functions + reusable CTE patterns), documented metric definitions and edge cases, and refactored queries to improve [runtime/cost/readability] by [X%]; surfaced [key insight] that informed [stakeholder decision/initiative], resulting in [impact].

Project: [name]
Goal: Answer [business question] and ship a stable dashboard for [stakeholders]. Success = [metric 1], [metric 2].
Data: Sources [A/B]. Grain: [event/user/order/day]. Primary keys: [keys].
Model: fact_[x] + dim_[y/z] (document grain + relationships). Definition owner: [you/team].
SQL: Cohort definition = [rule]. Funnel steps = [step1→step2→step3]. Segments = [segment list].
BI: Exec view = [3–5 KPIs]. Analyst view = [diagnostic tabs: cohorts/funnel/segments]. Performance notes = [what you optimized].
Validation: Reconciled totals vs [source of truth]; edge cases handled = [returns/dupes/timezone/bots].
Outcome: Key insight = [insight]. Decision/action = [what changed]. Result = [what happened]. Lessons learned = [1–2].

I shipped a [cohort/funnel/dashboard] for [team/domain] where the goal was to answer [business question] and make metrics consistent across segments.
I started by locking definitions: the grain was [grain], the primary KPI was [definition], and I documented edge cases like [dupes/returns/timezones/bots].
I built the analysis in SQL using window functions and reusable patterns, then modeled a small star schema (fact + dims) so filters wouldn’t break metrics.
In BI, I split it into an executive KPI view and an analyst diagnostic view, and I improved performance by [refactor] to reduce [runtime/cost].
The key insight was [insight], which led to [decision/action], and the result was [impact]. The main lesson was [lesson].

Related learning paths

• Best Online Courses & Certificates for IGNOU Students