That’s the whole story in one line.

This post is about a fresher Aman who went from “I’ve never worked in analytics” to a
Data Analyst job offer in 90 days—by treating a Data Analytics Course like a job, not like a hobby.

Meet the fresher (Day 0): where things were stuck

Let’s call him Aman.

Aman had a regular graduation background. No internships. No analytics experience. He had watched random Excel and SQL videos earlier,
but everything felt disconnected. That’s where a structured Data Analytics Course changed the game.

Why a structured Data Analytics Course works better than random learning

YouTube can teach you concepts. It rarely gives you the right order, weekly accountability, project feedback, and interview-ready output.
A good Data Analytics Course forces the one thing freshers avoid: practice with deadlines.

At Ascents Learning, we usually tell freshers something simple:
Tools are not the goal. Work samples are the goal.

Aman approached his Data Analytics Course with one clear outcome:
“Every week, I will produce something that can go into my portfolio.”

The 90-day plan (high level): what Aman followed during the Data Analytics Course

He treated the Data Analytics Course like a 6-day work week.

Daily routine (2.5–4 hours)

• 60–90 mins learning (topic for the day)
• 60 mins practice (questions/tasks)
• 30–60 mins portfolio work (notes, screenshots, clean outputs)
• 10 mins revision (same day)

Weekly rhythm

• Mon–Fri: skills + practice
• Saturday: project build + cleanup
• Sunday: revision + mock questions + LinkedIn update

Days 1–15: Fundamentals that actually show up in entry-level jobs

The first two weeks were all about building comfort with data.

What he learned inside the Data Analytics Course

• Excel (core): cleaning, sorting, filtering, IFs, XLOOKUP/VLOOKUP, pivot tables, slicers
• Data basics (practical): mean vs median, outliers, correlation (simple interpretation)
• Mini tasks: clean a messy sales sheet, create pivot summary, build a simple KPI table

What he produced (proof, not notes)

• One cleaned dataset (messy-to-cleaned file saved)
• One pivot-based KPI summary
• A short 1-page write-up: what he cleaned, what changed, what insights appeared

Days 16–30: SQL became the turning point

Most entry-level interviews lean heavily on SQL because it’s the fastest way to separate “watched videos” from “can work.”
This is where the Data Analytics Course started producing job-ready confidence.

SQL topics covered

• SELECT, WHERE, ORDER BY
• GROUP BY, HAVING
• JOINs (inner, left) with real examples
• CASE WHEN
• Subqueries (basic)
• Window functions (only what’s needed: ROW_NUMBER, RANK)

How he practiced

• One dataset for 10–12 days (same data, increasing difficulty)
• Daily target: 15 questions + review 5 wrong answers

What he produced (SQL case studies)

• Two SQL case files with business question → query → output → plain-English logic

Days 31–45: Power BI dashboards that look like real reporting

This is where many freshers go wrong: they build dashboards that look pretty but say nothing.
Aman’s rule during the Data Analytics Course was simple:
Every dashboard needs a business question.

What he learned (Power BI)

• Importing + cleaning basics
• Data modeling (relationships)
• Measures (basic DAX as required)
• Visual selection (why this chart, not 10 charts)
• Filters/slicers and basic drill-through

His dashboard output

• Sales KPIs: revenue, orders, AOV
• Monthly trend line
• Top categories and regions
• 5 insights + 3 action suggestions

Days 46–60: Project phase (the part that gets you shortlisted)

If you want interviews as a fresher, your Data Analytics Course must end with projects that look like work output.
Aman built two projects (not six half-baked ones).

Project 1: E-commerce Sales Performance (Excel + SQL + Power BI)

• Business questions: top categories, regional performance, seasonality and trends
• Deliverables: cleaned dataset, SQL query pack, Power BI dashboard, 1-page insights summary

Project 2: Customer Churn / Retention (SQL + Dashboard)

• Business questions: churn segments, likely drivers, practical retention actions
• Deliverables: churn segmentation, driver summary, KPI dashboard

During the Data Analytics Course, mentors reviewed his outputs like a workplace review:
metric definitions, defendable insights, and next steps.

Days 61–75: Resume + LinkedIn + Portfolio (where most people still waste time)

He didn’t wait for rejection. He fixed his profile before applying seriously.

Portfolio checklist

• 2 dashboards (links/screenshots + explanation)
• 2 SQL case studies
• 2 project summaries (1 page each)
• All links organized in one place (Drive/Notion/GitHub)

Resume changes that mattered

• Projects moved above education
• Bullets written like output: KPIs, tools, insights, actions

LinkedIn updates

• Headline: “Aspiring Data Analyst | SQL | Power BI | Excel | Projects”
• Featured section: dashboards + project links

Days 76–90: Interview prep + applying smart

The last 15 days were focused. Not “more learning”—but better communication and targeted applications.
This is where the Data Analytics Course becomes job conversion.

Application strategy

• Target roles: Junior Data Analyst, MIS Analyst, Reporting Analyst, data-heavy Junior Business Analyst
• Tailor keywords per job description
• Attach proof-first: project link relevant to the role

Mock interviews (twice a week)

• Explain a dashboard: what it tells the business, what action it suggests
• SQL basics: WHERE vs HAVING, JOIN logic, CASE WHEN
• Data handling: missing values, duplicates, sanity checks
• Decision-making: why this KPI, why this chart

The outcome: what got him hired (and what didn’t matter)

He didn’t get hired because he learned “everything.” He got hired because he showed proof and explained it well.
The hiring manager cared about clarity, structured thinking, and visible work.

What you can copy from this Data Analytics Course success story

If you have 2 hours/day

• 45 mins learning
• 60 mins practice
• 15 mins portfolio notes/screenshots

If you have 4 hours/day

• Add one extra project
• Increase SQL volume and revision
• Start applying from Day 60

If communication is your weak point

• Daily 10 minutes: explain one chart in simple words
• Daily 10 minutes: explain one SQL query in simple words

Common mistakes freshers make during a Data Analytics Course

• Learning tools but not building projects
• Dashboards that look nice but don’t answer a business question
• Copy-paste portfolios with no explanation
• Applying without matching keywords and basics
• Ignoring mock interviews until the last week

How Ascents Learning supports the journey

Freshers don’t need motivational quotes. They need structure and feedback.
That’s what we focus on during the Data Analytics Course.

• Practical assignments that force daily practice
• Mentor reviews that improve project quality
• Resume + LinkedIn support focused on shortlist signals
• Mock interviews that train clear explanations
• Placement support aligned to readiness and eligibility