STEP 01
Live session
A working session with your mentor — not a recording. You ask questions as you hit them, and leave with the week's problem defined.
01
Live, not recorded
Become a Data Analyst
Data Analytics Mentorship Program
PrimrIQ AI Services LLP runs this programme from Noida, India. Work through 12 real Indian business projects across FMCG, e-commerce, fintech, retail, and agriculture. The data is messy. The questions are open-ended. You decide the approach.
8 months12 Projects + 2 CapstonesMentor-reviewed
Tools covered
ExcelPower QuerySQLPythonPandasNumPySeabornMatplotlibStatisticsPower BITableau
Roles you’ll be ready for
Data AnalystBusiness AnalystBI AnalystReporting AnalystMIS Analyst
DATA ANALYTICSQuery the raw sales drop where it lands.
Duration
8 months
Modules
10
Projects
12 Projects + 2 Capstones
Format
Live + labs
Level
Beginner to intermediate
The lab
This is the environment you work in.
Real consoles, a live brief, and a mentor reading what you submit. Not a video you watch.
Athena
DATA ANALYTICSQuery the raw sales drop where it lands.
What a week actually looks like
Practice-first is easy to claim. Here's the machinery behind it.
STEP 02
Lab work
You open the browser lab and work on real, messy data. No setup, no environment config. Just the problem.
02
Real, messy data
STEP 03
Submit
You push your work — notebook, query, pipeline, dashboard — for review. Every submission, every week.
03
Every week
STEP 04
Mentor review
Your mentor reads it, grades it, and tells you what a senior would have done differently. That feedback is the actual product.
04
The actual product
10 modules. Every one ends in a project.
Each module includes a hands-on project. The final module contains your capstone assignments.
🚀 Opening weeksOpening Weeks — Python & SQL Foundations18 topics
No prior coding knowledge assumed. These four weeks build the Python and SQL foundation the entire track runs on.
Installing Anaconda and launching Jupyter Notebook — the analyst's working environment
Variables — integer, float, string, boolean — and why data types matter for data work
if, elif, else — conditional logic; comparison operators: ==, !=, >, <, >=, <=
for loop, while loop, range(), break, continue — repeating operations across data
Defining functions with def, parameters, return, default argument values
Lists — indexing, slicing, append(), remove(), len(), list comprehensions
Dictionaries — key-value access, nested dicts, iteration
File I/O — reading a CSV with open(), writing output to a file
pip install, import — installing and using libraries; try/except for error handling
What is a relational database — tables, rows, columns, primary and foreign keys
SELECT, FROM, WHERE — filtering with all comparison and null operators
ORDER BY, LIMIT, DISTINCT; COUNT, SUM, AVG, MIN, MAX
GROUP BY, HAVING — and the common mistake of using WHERE when you need HAVING
INNER JOIN, LEFT JOIN — understanding the ON clause and what each row represents
CASE WHEN — conditional logic inside a query; COALESCE for null handling
Subqueries in WHERE; WITH clause (CTE) — naming a subquery and reusing it
Top N per group pattern, period comparison patterns
Connecting Python and SQL — sqlite3, loading query results into a DataFrame
Module 01Foundations of Data & Excel
The foundation every analyst uses on day one. Covers data literacy and Excel from core functions through Power Query — the skill that separates manual work from automated workflows.
What you cover
What data actually is: observations recorded in a structured form
Why real-world data is almost never clean: manual entry, system exports, format inconsistency
File types you will encounter: CSV, XLSX, JSON — when each appears in Indian business contexts
Relative, absolute, and mixed cell referencing — when each breaks your formula and why
INDEX-MATCH over VLOOKUP — why INDEX-MATCH is the production-grade choice
SUMIFS, COUNTIFS, AVERAGEIF — multi-condition aggregation
IF, IFS, AND, OR, nested IFs — conditional logic without code
IFERROR, IFNA — graceful error handling in live dashboards
LEFT, RIGHT, MID, LEN, TRIM, CLEAN, SUBSTITUTE, TEXTJOIN — text manipulation for messy exports
VALUE, TEXT — converting between text-stored numbers and actual numbers (the most common Indian ERP export problem)
YEAR, MONTH, DAY, DATE, EDATE, EOMONTH, NETWORKDAYS, DATEDIF — date arithmetic
OFFSET, INDIRECT — dynamic range references for self-updating dashboards
Pivot Tables — field placement, value settings, date grouping, calculated fields, slicer connections
Conditional formatting — highlight rules, data bars, colour scales, custom formula rules
Data validation — dropdown lists, input messages, error alerts
Flash Fill — pattern-based column splitting without formulas
Power Query — connecting to CSV, Excel, SQL databases, and entire folders
Changing data types — why type mismatches break every downstream formula
Group By, Merge Queries, Append Queries — automating stacking of monthly exports
Dashboard construction — dynamic chart titles, sparklines, connected slicers
Module 02SQL for Data Analysis
Business-driven SQL from fundamentals through window functions and CTEs. Covers the exact interview patterns that appear at Indian analytics companies.
What you cover
SELECT, WHERE, ORDER BY, LIMIT, OFFSET, DISTINCT, CASE WHEN — all filtering operators
COUNT(*) vs COUNT(column) — how NULLs affect the count
GROUP BY, HAVING — conditional aggregation
INNER JOIN, LEFT JOIN, RIGHT JOIN, FULL OUTER JOIN, self join, multi-table joins
Scalar subqueries in SELECT, subqueries in WHERE, derived tables in FROM
Correlated subqueries; EXISTS and NOT EXISTS; UNION, UNION ALL, INTERSECT, EXCEPT
OVER clause, PARTITION BY, ORDER BY, ROWS/RANGE BETWEEN
ROW_NUMBER(), RANK(), DENSE_RANK(), NTILE(n)
LAG(), LEAD(), FIRST_VALUE(), LAST_VALUE()
Running totals, moving averages, percentage of partition total
Month-over-month change using LAG with date partitioning
WITH clause — multiple CTEs, chaining sequential transformations
Recursive CTEs — date spines, category trees
EXPLAIN and EXPLAIN ANALYSE — reading execution plans
Indexes — B-tree, write overhead tradeoff; filtering early; avoiding functions on indexed columns
Top N per group, period comparison, funnel analysis, retention calculation
Module 03Python for Data Analysis
Python built for real analysis work. Dedicated focus on data cleaning — the most valued and most lacking skill in fresher DA hires.
What you cover
Jupyter environment — notebooks for analysis, scripts for pipelines; virtual environments
NumPy and Pandas — installing, importing, understanding the relationship between them
pd.read_csv(), pd.read_excel(), .head(), .info(), .describe(), .shape, .dtypes
Selecting columns — single, multiple, conditional; loc and iloc
merge(), concat(), pivot_table()
Null detection — .isnull(), .sum(), .isnull().mean()
Null handling — fillna(), ffill(), bfill(), dropna() — choosing the right approach
Type conversion — astype(), pd.to_numeric(errors='coerce'), pd.to_datetime()
Deduplication — duplicated(), drop_duplicates()
String cleaning — .str.strip(), .str.lower(), .str.replace()
Indian data patterns — mobile numbers, pincodes, GST numbers, PAN — cleaning and validating
Outlier detection — IQR method and z-score — cap, remove, or flag strategies
Repeatable EDA sequence: shape → dtypes → nulls → distributions → correlations → business framing
.value_counts(), .groupby(), .corr(), pd.crosstab()
Matplotlib — line, bar, scatter, histogram; subplots; tight_layout(); saving to file
Seaborn — heatmaps, pairplot, boxplot, violin plot — styled charts for reporting
Time series — pd.read_csv() with parse_dates, resampling, rolling statistics
Module 04EDA, Statistics & Business Testing
Statistics applied directly to business decisions. A/B testing, cohort analysis, and RFM segmentation — the three frameworks that appear in most Indian DA interviews.
What you cover
What statistics is for: making decisions under uncertainty
Mean, median, mode — when each one misleads; IQR, skewness, kurtosis, coefficient of variation
Probability — addition rule, multiplication rule, conditional probability, Bayes' theorem
Normal distribution, Central Limit Theorem, binomial, Poisson — when each applies
H₀ and H₁, p-value, significance level α, Type I and II errors, statistical power
scipy.stats — ttest_ind, ttest_rel, chi2_contingency in code
Practical vs statistical significance — a significant result can still be too small to act on
Minimum Detectable Effect, sample size calculation, the peeking problem
Writing the recommendation — a one-paragraph verdict the product team can act on
Acquisition cohorts — grouping by the month a customer first ordered
Retention matrix — tracking what percentage of each cohort is still active
Heatmap visualisation, drop-off identification
Recency, Frequency, Monetary — the three dimensions of customer value
pd.qcut() scoring, segment labelling, business action per segment
Revenue at stake — quantifying what losing the high-value segment costs
OLS regression — R-squared, coefficient interpretation in business language, residual analysis
Module 05Dashboard Design with Power BI
Full Power BI workflow from data modelling through Power BI Service publishing — not just building .pbix files but sharing work the way employers expect.
What you cover
Why dashboards exist — decision-makers need numbers without opening a spreadsheet
Installing Power BI Desktop; first bar chart, line chart, and card visual
Slicer — how it filters everything on the page; measures vs columns
Star schema — fact tables and dimension tables; relationships in the model view
Import vs DirectQuery vs Live Connection — performance and refresh tradeoffs
DAX context — filter context, row context, context transition via CALCULATE()
CALCULATE, FILTER, ALL, ALLEXCEPT, SUMX, AVERAGEX
DIVIDE, IF, SWITCH TRUE, RELATED, VAR … RETURN
Time intelligence — TOTALYTD, DATEADD, SAMEPERIODLASTYEAR
Matrix, scatter, map, treemap, KPI visual
Drill-through, bookmarks, custom tooltips, slicers
Power BI Service — publish to workspace, share links, scheduled refresh
Row-level security — static roles, dynamic RLS with USERNAME()
Module 06Data Visualisation with Tableau
Interactive Tableau dashboards published to Tableau Public — a live portfolio URL is the most-clicked element on a fresher resume.
What you cover
The five chart types covering 90% of business analytics — and when each one is wrong
Chart titles that state the finding, not the topic
Tableau Public — why a live URL matters on a resume
Dimensions vs measures; Marks card; aggregation defaults
Building bar, line, scatter, map, treemap, heatmap, dual axis, bullet charts step by step
Calculated fields — string, date, number, and logical functions
Table calculations — Percent of Total, Running Total, Moving Average, Rank
LOD Expressions — FIXED, INCLUDE, EXCLUDE with Indian business examples
Sets, parameters, parameter actions
Filter actions, highlight actions, URL actions
Viz in tooltip — mini chart shown on hover
Device designer — creating separate layouts for desktop, tablet, and phone
Publishing to Tableau Public — your portfolio URL for your resume
Module 07Working Smarter with Modern Tools
How analysts use current productivity tools to work faster — with honest verification practices built in from the start.
What you cover
What productivity tools do: generate syntax, surface patterns, draft summaries
What they do not do: understand business context, make judgement calls, take responsibility for output
The fabricated statistic problem — tools confidently reporting numbers that do not exist in the data
The verification rule: every tool-generated number validated against raw data before it leaves your hands
Copilot in Excel — formula generation, DAX assistance, data summarisation
When Copilot fails — unsupported functions, out-of-context requests, hallucinated formula names
Prompt construction for SQL — providing schema, sample rows, and the exact business question
Iterative prompting, debugging with error messages, optimisation requests
EXPLAIN on every tool-generated query; spot-check output count against expected
OpenAI API setup — API key management, python-dotenv
Generating insight summaries from DataFrames; automating report narration
Structured output from APIs; regex generation for Indian data formats
Raw CSV → Pandas clean → analysis → API summary → formatted output pipeline
Module 08Business Communication & Stakeholder Reporting
The module most courses skip — structuring insight narratives, writing executive summaries, and presenting to audiences who do not know what a p-value is.
What you cover
The analyst's actual job: translating data into decisions, not just computing numbers
The insight gap — correct analysis the audience cannot act on
The four audiences — C-suite, functional heads, operations, data teams
What a stakeholder is; how to write a professional email about your findings
The structure of a slide — title (finding, not topic), visual, one takeaway
SCR framework — Situation, Complication, Resolution
BLUF — Bottom Line Up Front, leading with the finding not the methodology
Pyramid principle — conclusion first, supporting evidence second
The so-what rule and the now-what rule — implication and action attached to every metric
One insight per slide; chart titles that state the finding; semantic colour; whitespace
Executive summary — finding, magnitude, implication, recommendation, confidence; active voice; no jargon
Anchoring, analogy use, handling push-back; Q&A preparation
Email structure, Slack messages, dashboard annotations, asynchronous communication
Data dictionary, notebook annotation, README for analysis repositories
Module 09Capstone Projects
Two fully open-ended business assignments with no instructions and no answer key, simulating the first 90 days on the job.
What you cover
Capstone 1 — Six States, Two Years, One CFO Question — Where Is This FMCG Company Actually Making Money?
Capstone 2 — Who Stays, Who Leaves, and Who Was Never Worth Acquiring — Customer Intelligence for a Health Supplements Brand
Both capstones use real Indian business data with deliberately messy exports
Deliverables written for a business audience — not a data team
What you will learn
01Clean and transform messy real-world datasets using Excel Power Query and Python Pandas
02Write business-driven SQL queries — CTEs, window functions, query optimisation
03Perform structured exploratory data analysis and communicate findings in plain language
04Apply A/B testing, cohort analysis, and RFM segmentation to real business problems
05Build Power BI dashboards with DAX, data modelling, and Power BI Service publishing
06Create and publish Tableau dashboards with a live Tableau Public portfolio URL
07Use modern productivity tools within the analyst workflow accurately and responsibly
08Structure and deliver insights in executive summaries and stakeholder presentations
The questions people actually ask
Do I need prior coding experience?
No. The track starts from Excel basics. Students from commerce, arts, statistics, and non-engineering backgrounds complete this successfully. Analytical thinking matters more than a coding background.
Will I learn Power BI or Tableau — or both?
Both. Power BI is standard in Indian IT services and BFSI. Tableau is preferred at consultancies and product companies. Knowing both makes you hireable across a wider range of roles.
What kind of data will I work with?
Real Indian business data — FMCG distributor exports, EdTech revenue databases, D2C order records, APMC mandi price data, fintech loan portfolios. Not Titanic or Iris.
What jobs can I apply for after completing this track?
Data Analyst, Business Analyst, BI Analyst, Reporting Analyst, and MIS Analyst roles across IT services, e-commerce, BFSI, FMCG, healthcare, and startups. Fresher industry range: ₹4–8 LPA.
Is there a refund policy?
Yes. Attend the onboarding session and apply for a full refund within 7 days of purchase if you are not satisfied. No questions asked.
Published 14 August 2026 · Last updated 14 August 2026 · Written by Rishu Dwivedi, Founder, PrimrIQ
Do I need to know how to code before I start?
Every track opens with four weeks of Python and SQL foundations that assume nothing. If you have written code before, those weeks go quickly; if you have not, they are the reason the rest of the programme is reachable.
How much time should I plan for each week?
Eight to ten hours: one live session, lab work on your own schedule, and a submission your mentor reviews. Labs stay open, so a heavy week at college or work does not reset your progress.
What happens if I am not satisfied?
Attend, try the labs, and if it is not what you expected, apply for a full refund within seven days of purchase. No conditions beyond that.
Is the certificate worth anything to an employer?
The certificate is verifiable and states what you completed. What carries weight in an interview is the portfolio behind it — which is why every module ends in a project you can walk a hiring manager through line by line.
Do you guarantee a job at the end?
No, and we will not pretend otherwise. You get resume work, mock interviews and career guidance, plus real projects you can talk through in detail. Interviews are still yours to win.
Course fee
₹17,999 + GST
One-time · includes all projects, live sessions and mentor reviews
Attended and not satisfied? Apply for a full refund within 7 days of purchase.
Industry range
₹4–8 LPA
Fresher, India — market benchmark, not a guarantee
Ready to start your Data Analytics journey?
12 Projects + 2 Capstones to build, a mentor on every submission, and 8 months of live sessions.
See the curriculum
7-day full refund · No setup required · Career prep included