Data Analytics Lab
A structured program that turns beginners into entry-level data analysts.
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Student Stories
What People Say
What our beta testers say
“I had tried learning analytics from YouTube before, but I never knew what order to follow. The lab format forced me to actually think instead of just copying queries. The case clinics were the most useful part for me. By the end, I finally felt like I understood how analysts work in real companies.”
“The course is not easy, but that's exactly why it worked for me. Every week we had to solve real problems, not just watch lectures. I liked that mentors didn't give direct answers, they made us figure things out. I still have to improve a lot, but now I know what to practice.”
“I joined because I wanted to switch to analytics but didn't have real project experience. The portfolio we built here actually helped me explain my work in interviews. Mock interviews were very close to real ones. It felt more like training for a job than a course.”
What industry experts say
“I reviewed the curriculum and session structure, and what stood out was the focus on problem-solving rather than tools. Most entry-level programs overemphasize syntax, but this one forces students to work through ambiguous, business-style cases. That's much closer to what analysts actually deal with in the industry.”
“I interacted with a few of the projects and case assignments from the lab, and they are far more realistic than what I usually see in fresher portfolios. The emphasis on explaining decisions, not just showing dashboards, is a strong differentiator. If students complete the program seriously, they should be better prepared than most entry-level applicants.”
“What I liked about the platform is that it doesn't promise placements first — it focuses on building thinking ability. The case clinics and weekly problem-solving format are very similar to how analysts learn on the job. The structure is demanding, but that's exactly what makes it useful for fresh graduates.”
Placement Support
We don't just teach. We place.
Job-Ready Portfolio
- ●3 real analytics projects and 9 structured case clinics solving real business problems
- ●GitHub portfolio, dashboards, and project documentation prepared before Week 10
- ●Resume and portfolio reviewed so your work speaks for you in hiring processes
Interview Prep & 12-Month Support
- ●Mock interviews across SQL, Python, Excel, and business case formats
- ●Feedback on communication, problem-solving, and explanation of insights
- ●Continued support for 12 months — mock interviews and job application guidance
Direct Hiring Access
- ●Hiring opportunities shared from startups, analytics firms, and growing companies
- ●Focus on companies actively hiring entry-level analysts, not unrealistic senior roles
- ●Resume circulation and placement updates shared during the hiring cycle
The Science of Learning
Structured Learning vs. Unstructured Chaos
Unstructured Learning
- Information Overload
Endless tutorials leading to "Tutorial Hell" with no clear path to mastery.
- Isolation & Burnout
74% of self-learners quit within 3 months due to lack of accountability.
- Weak Portfolio Score
Generic projects that recruiters have seen 1000 times before.
Structure + Community
- 3x Higher Retention
Peer-to-peer learning increases knowledge retention by 90% (National Training Laboratories).
- Industry-Grade Portfolio
Build projects that solve real business problems, increasing resume shortlisting by 4.5x.
- Job Readiness Metrics
Science-backed curriculum focused on high-demand skills like SQL & Business Context.
"Research shows that structured environments with social accountability lead to a 2.5x increase in program completion rates compared to solo learning."
Program Roadmap
10-Week Intensive Lab
A rigorous roadmap from fundamentals to industry-level analytics.
Designed to build problem-solving ability, not just tool knowledge.
- 01Data analytics vs business analytics, data types, data formats, analytical thinking framework
- 02Excel functions: IF, SUMIFS, COUNTIF, text, date, logical formulas
- 03Pivot tables, INDEX-MATCH, conditional formatting, advanced Excel usage
- 04Case clinic: E-commerce sales RCA on messy dataset
- 05Project 1 briefing: Excel dashboard project
- 01Relational databases, SELECT, WHERE, ORDER BY, LIMIT
- 02Aggregations, GROUP BY, HAVING, date handling, business metrics
- 03JOINs, multi-table queries, subqueries, data validation after joins
- 04Case clinic: SaaS subscription analytics with RCA
- 05SQL optimization, debugging, interview-style queries
- 01Window functions, rankings, running totals, time-series analysis
- 02Python basics: variables, loops, functions, data structures
- 03NumPy & Pandas foundations, reading datasets, basic analysis
- 04Case clinic: Retail inventory analysis with RCA
- 05Project 1 presentation + feedback
- 01Pandas data cleaning, filtering, grouping, merging datasets
- 02Matplotlib & Seaborn visualization, choosing correct charts
- 03Case clinic: Customer segmentation with Python
- 04Git & GitHub basics, version control for analytics projects
- 05Project 2 briefing + GitHub portfolio setup
- 01Exploratory Data Analysis workflow, statistics, distributions
- 02Data cleaning, duplicates, outliers, fuzzy matching, architecture basics
- 03Case clinic: Financial data quality problem with RCA
- 04Power BI fundamentals, Power Query, relationships, visuals
- 05Dashboard practice + Project 2 work session
- 01Business metrics, CAC, LTV, funnels, unit economics
- 02Retention, churn, cohorts, activation metrics
- 03Case study: Fintech retention drop (student solve)
- 04Case discussion: expert RCA & solution breakdown
- 05RCA frameworks: 5 Whys, Fishbone, hypothesis-driven analysis
- 01A/B testing design, sample size, metrics, experiment planning
- 02Statistical testing, p-values, confidence intervals, decisions
- 03Case study: Marketing A/B test trade-off analysis
- 04Case discussion: expert decision framework
- 05Business case frameworks, MECE, hypothesis thinking
- 01DAX, advanced dashboards, filter context, time intelligence
- 02GenAI tools: ChatGPT, Claude, Gemini, prompt engineering
- 03AI for Excel, SQL, Python, Power BI workflows
- 04GenAI practice: automation, validation, tool comparison
- 05Project 3 briefing: AI-powered analytics project
- 01AI document analysis, long-context reports, data extraction
- 02AI automation workflows for analytics tasks
- 03Case clinic: Competitive intelligence using AI
- 04Automation exercises: reports, dashboards, validation
- 05Project 3 work session + AI office hours
- 01SQL, Python, Excel technical interview preparation
- 02Business case interviews, RCA, MECE, storytelling
- 03Resume, LinkedIn, GitHub, portfolio optimization
- 04Behavioral interviews, job search, salary negotiation
- 05Mock interviews + final project presentations
Why Grito Works
The 6 Pillars of Excellence
- 10-week live program with fixed weekly schedule
- 7 hours of live sessions per week
- Case clinics, tool practice, and project workshops built into curriculum
- No self-paced shortcuts — every week has deliverables
- 3 portfolio-level analytics projects
- 9 weekly case clinics based on real business problems
- Work with messy, human-entered, real-style datasets
- Build GitHub + dashboards + resume-ready portfolio before Week 10
- Mentor assigned team groups for collaborative environment
- Weekly live doubt clinic for every team
- Case submission scoring with written feedback every week
- Active community support throughout the cohort
- SQL, Excel, Python, BI & case interview prep
- Mock interviews in final phase of program
- Resume, portfolio & LinkedIn review before applications
- Practice with real interview-style questions, not theory
- Access to live job board tracking entry-level analyst roles
- Resume circulation to hiring partners & startups
- Private placement group with job updates
- Continued mock interviews & profile reviews after course ends
- Designed for Tier-2 / Tier-3 graduates & career switchers
- Focus on entry-level analyst roles, not experienced positions
- Step-by-step path: Excel - SQL - Python - BI - Business Thinking
- Emphasis on problem-solving, not just tools
Portfolio Focused
Build what gets you hired.
Data Analysis Project
Clean - Query - Visualize
- ●Data cleaning, joins, aggregations, and KPI building
- ●Dashboard design for business reporting
- ●Dataset size similar to real analyst tasks
Business Analysis Project
Metrics - Hypothesis - Decisions
- ●Root cause analysis on business metrics
- ●A/B testing and hypothesis validation
- ●Case-style problem solving used in interviews
Industry Capstone Project
Data + Business + Storytelling
- ●Work on large, unclean, multi-table datasets
- ●Build dashboard + analysis + final presentation
- ●Portfolio-ready project reviewed before placements
The Curriculum
Skills and Tools for the Modern Analyst
Python Programming
Write clean, efficient code for data analysis and automation
SQL & Databases
Query, join, and manipulate data across relational databases
Pandas & NumPy
Wrangle messy datasets into structured, analysis-ready formats
Power BI & Tableau
Build interactive dashboards that drive business decisions
Excel & Google Sheets
Master advanced formulas, pivot tables, and data modeling
Data Visualization
Create compelling visual stories with Matplotlib and Seaborn
Statistics & Analytics
Apply statistical methods to extract insights from data
Git & Version Control
Collaborate on code and track changes like a professional
Ready to enter the Lab?
Join the next cohort starting May 16.
Secure your spot today.
Clarifications
Frequently Asked Questions
Refund requests must be submitted at least 7 days before the cohort start date. Requests received within this period will be reviewed and, if approved, processed to the original payment method.
No refunds are issued within 7 days of the cohort start, after the cohort begins, or after access to program materials is granted.
This policy exists because seats are limited and each cohort is planned in advance with fixed capacity. We strongly recommend enrolling only if you are sure you can commit to the program.
Sessions are taken by working industry professionals with experience in data analytics, business analytics, and product analytics. Instructors are selected based on real industry experience, ability to teach clearly, and familiarity with entry-level hiring standards.
In addition to instructors, you will also be supported by mentors who help with doubt sessions, case clinics, and interview preparation.
We do not use recorded-only teaching. All core sessions are live.
We provide Grito Labs Proof of Work Certificate to students who complete the program and submit all required projects. It confirms completion of the full 10-week lab, case clinics, projects, and final portfolio review.
However, the focus of the program is not the paper—your portfolio, projects, and interview performance matter far more in hiring.
You do not need prior experience in data analytics. The program is designed for engineering/BCA/BSc/commerce graduates, final year students, and professionals with 0–3 years of experience.
You should have basic computer familiarity, a willingness to learn, and the ability to commit 8–10 hours per week. Hard work matters more than background.
What we provide are real projects, interview preparation, resume & portfolio review, hiring partner introductions, and 12 months of placement support.
We do not guarantee placements, and we do not believe any honest program should.
Your outcome depends on your effort, consistency, and performance.
Students who complete the program seriously are significantly better prepared for entry-level analytics roles.
Yes — the program is built specifically for this. Most hiring decisions at entry level are based on skills, projects, communication, and interview performance—not your college name.
The lab focuses on building the things that actually matter in interviews.
Expect around 10–12 hours per week. This includes live sessions, case clinics, project work, practice, and interview prep.
This is not a passive course. It is designed to feel like training.
Yes, recordings are provided for revision. However, attending live sessions is strongly recommended because doubt solving happens live and case discussions are interactive.
Students who attend live usually perform better.
You will work on real-style datasets across different industries. Projects include sales analysis dashboards, SQL business metrics analysis, Python EDA projects, and AI-assisted business analysis.
The goal is to build a portfolio you can show in interviews.
Most courses teach tools; we train problem solving. The lab includes weekly case clinics, real datasets, project workshops, mock interviews, and portfolio reviews.
It is designed to simulate real analyst work, not just syllabus completion.
You continue to get placement support for up to 12 months, including mock interviews, portfolio feedback, and hiring partner updates. Your cohort ends, but your preparation does not.
In some cases, yes. If installment options are available, they will be shown during checkout or shared by the team. Seats are confirmed only after payment is completed.
Cohorts are kept limited to maintain quality. This allows for proper doubt solving, project feedback, mentor support, and personalized interview preparation.
Yes. We start from the basics, but the pace is serious. Students who attend regularly and complete assignments are able to keep up; those who skip practice usually struggle.
Yes. Mentors help with doubts, case clinics, feedback, interview prep, and project guidance throughout the cohort.