AI assistants can now write your formulas, clean your exports and summarise a quarter in seconds. They also get totals, averages and causes wrong, and sound sure about all three. This self-paced course teaches the analysis behind the answer: define the metric, clean the data, write and test formulas, summarise with PivotTables, model in Power BI, design an honest dashboard and check every AI claim before it reaches a meeting. About 18 hours over 6 weeks, no coding.
Bodhih Data Analytics with AI: Practitioner is a self-paced online certification that teaches working professionals to turn business questions into trustworthy answers with Excel or Google Sheets, Power BI and AI assistants. In about 18 hours you practise in the browser on one company’s realistic data: defining metrics, cleaning data, writing formulas, building PivotTables, modelling a star schema with DAX, designing dashboards and checking AI analysis for common traps. A graded capstone on your own data earns the credential.
Facts on this page last checked by Siddharthan VGJ, Director, Bodhih Training.
Duplicated rows, dates stored as text, three spellings of the same store. Most people clean data by hand, once, with no record, so next month starts from zero and nobody can say why the total moved. Notice how much faster a review goes when the totals reconcile the first time.
One flat table, measures nobody reviewed and three reports with three revenue figures. The tool works; the model and the metric definitions do not. AI assistants inside the tools inherit the same problem, because they can only be as right as the model they read.
An AI summary says a region has the worst returns, a campaign caused the rise and a new market is failing. Each claim sounds right and each can hide a classic trap: a mix effect, a closed store missing from the comparison, a handful of orders. You still sign the report, so you need a checking habit.
HR, finance, sales operations, marketing, operations and customer service people who build reports and track KPIs. The Role Lens puts your function’s data and KPIs into the exercises and your at-work plan.
You want a structured, current foundation: modern Excel formulas, Power Query steps, a Power BI star schema with DAX measures, and AI-assisted analysis you can check.
You ask for the numbers and decide on them. Modules 1, 6, 7 and 8 focus on asking the right question, reading dashboards, judging AI analysis and turning findings into a decision.
One course for every function, with a baseline and a final on parallel forms and company reporting by role, so you can see the before and after across a team.
This course is not for you if you want PL-300 exam preparation, SQL or Python programming, or a career-change programme of 150 hours or more: it is a practitioner course for people who already work with data.
Four dated figures, each linked to its source.
US employment of data scientists is projected to grow 35% from 2025 to 2035, with about 24,800 openings a year on average (a projection).
US Bureau of Labor Statistics, Occupational Outlook Handbook: Data Scientists, Projections 2025-35, checked 6 November 2026Of employers see analytical thinking as the core skill for their workforce in 2025, and AI and big data are the fastest-growing skills, as reported.
World Economic Forum, Future of Jobs Report 2025 (as reported by HRKatha), 21 January 2025Of 517 US and UK business leaders rate basic data literacy as important or very important, yet fewer than half say their organisation provides basic data or AI literacy training.
DataCamp with YouGov, State of Data & AI Literacy 2026 (vendor research), 26 February 2026In a study of 758 BCG consultants, people using AI on a task outside its frontier were 19 percentage points less likely to be correct.
Dell’Acqua and colleagues, Harvard Business School working paper 24-013, 2023Imagine walking into Monday’s review with numbers you have reconciled yourself, a dashboard that leads with the decision and an AI summary you have already checked line by line. That is what the course builds: you measure yourself at the start, practise every step in the Data Lab on one retailer’s data, catch an AI’s mistakes in the Analysis Inspector and submit a capstone that a reviewer grades against a five-criterion rubric.
Turn a vague request into a question card with defined metrics (numerator, denominator, filters, period) and the threshold that would change the decision.
Profile, fix and reconcile messy data with repeatable steps, and keep a cleaning log that shows rows and totals before and after.
Use XLOOKUP, SUMIFS, IFS, FILTER, UNIQUE, LET and date and text functions, and test any formula, including an AI-written one, against known cases.
Build PivotTables, use medians, growth and segment cuts, and spot outliers, mix effects and closed-store gaps before you conclude.
Shape data into a star schema with the right relationships and filter direction, and write DAX measures with CALCULATE, DIVIDE and time intelligence.
Choose the chart that fits the question, fix charts that mislead and design an accessible one-page dashboard that leads with the decision.
Use AI assistants to plan, run and explain analysis with safe data, then check every claim with the 5 Rs: read the method, reconcile, recompute, re-cut and test rival explanations.
Write a one-page decision memo with confidence and risks, handle stakeholder challenge with evidence and apply data governance and privacy basics.
The pathway runs in order: a baseline assessment, eight modules of 75 to 90 minutes (each with a hands-on Data Lab, Inspector or simulation, an at-work task and a retrieval check, with boosters two and seven days later), the capstone analysis project and a final quiz on a parallel form. Every module uses the same fictional retailer, Kestrel Home & Garden: twelve stores and an online channel, with a returns spike, an outlier order, a closed store and a campaign to find in the data.
Measured before you start and again on the same instrument at the end, so the change is recorded rather than claimed.
The baseline assessment, Form A: 20 scenario and calculation questions on all eight competencies, about 25 minutes. It is not pass/fail; it sets your baseline, and you take the parallel form at the end.
From a vague request to a decision-linked question, metrics, KPIs and the metric tree, data literacy basics and your AI analyst partner. Data Lab: the question behind the request.
Tidy data and types, profiling, duplicates and missing values, repeatable steps in Power Query or formulas, and AI-assisted cleaning with checks. Data Lab: the Pune March export.
Lookups and Tables, conditional totals and logic, text, dates and dynamic arrays, and testing AI-written formulas. Data Lab: the Chennai May orders.
PivotTables in five moves, percentages, growth and trends, describing a distribution, and cutting before you conclude. Data Lab: H1 in eight cuts.
From flat table to star schema, relationships, cardinality and filter direction, DAX measures and filter context, and time intelligence. Data Lab: the Kestrel semantic model.
Choosing the chart, charts that mislead, dashboard design and storytelling with data. Data Lab: a one-page dashboard that leads with the decision.
What AI can do in analysis, prompt patterns, the statistical traps AI falls into, and the 5 Rs of verification with safe data. Labs: the Analysis Inspector and a recompute in the Data Lab.
From findings to a recommendation, presenting and handling challenge, data governance and privacy basics, and what to automate next. Scenario Sim: The H2 planning meeting.
Answer one real business question with data (or use the Kestrel capstone pack) and submit a question card, cleaned data and log, analysis and model, a one-page dashboard, an AI verification log and a decision memo. Reviewed against the five-criterion rubric.
The final assessment, loaded from Form B: 20 parallel scenario and calculation questions, about 25 minutes. Score 70% or more (3 attempts), with your capstone accepted, for the credential.
Finishing this pathway issues a credential backed by the evidence you produced along the way. It is verifiable by link and is also issued as an Open Badge 2.0, so an employer can check it without taking your word for it.
Attrition and early-leaver analysis by store and tenure, with small groups kept private.
Budget-variance dashboards with like-for-like comparisons and margin after returns.
Win rate, deal size and discount analysis by segment, without averages hiding the story.
Supplier on-time-in-full and defect-rate tracking that catches a bad batch early.
Campaign results compared with the season and a holdout, not just “sales went up”.
First-contact resolution and return-reason analysis that feeds product fixes.
What comes with the course, and what makes the practice different.
Six labs on Kestrel’s data: Ananya’s question, the Pune March export (41 messy rows to clean and reconcile), the Chennai May formula tasks, H1 in eight cuts with PivotTables, the Kestrel semantic model with DAX-style measures, and Ananya’s dashboard. Every answer is graded, so a company sees real scores.
Check Sofia’s AI summary of H1 2026 sentence by sentence and flag each trap: a wrong number, a cause not shown, a misleading average, too few cases, an unfair comparison or a privacy risk. Then present your findings to six stakeholders while Stakeholder trust, Decision quality, Data risk control and Time react to your choices.
Spreadsheet Superpowers, The AI-Fluent Professional, Think Clearly When Machines Think Fast, The Team Goals and OKR Cycle Kit and The Data Privacy Compliance Starter: a practice workbook, a dashboard template, a formula guide, an output verification checklist and a one-page decision memo among them.
Company seats with reporting on each learner’s progress, before-and-after scores and capstone status. Each learner picks the Role Lens for their function.
Ask about team seats| What is compared | A typical data analytics course | Data Analytics with AI at Bodhih |
|---|---|---|
| Scope | Usually one tool: spreadsheets, or Power BI, or AI prompting. | Question, clean, formulas, summarise, model, visualise, AI-assisted analysis and decision, as one workflow on one case. |
| Practice | Videos, or labs that need the software installed and licensed. | The Data Lab in your browser in seven of the eight modules, graded as you go, plus a stakeholder simulation; no licence needed. |
| AI | Shows what the assistant can do. | Trains what to check: the 5 Rs and the statistical traps where AI summaries go wrong, practised in the Analysis Inspector. |
| Feedback on your work | None, or an exam. | A capstone analysis project on your own data, graded by a reviewer on a five-criterion rubric. |
| Measurement | A completion certificate. | Baseline and final on parallel forms; your before and after is shown on the credential. |
| Time and cost | Long subscriptions of 150 hours or more, or live programmes over six months. | Self-paced, about 18 hours, $199. |
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Straight answers on time, price, tools and the credential. If yours is not here, ask us and you will have a reply the same working day.
It is for working professionals in any function who build reports, track KPIs or answer “what happened and why” with spreadsheets, including HR, finance, sales, operations, marketing and customer service; for new analysts who want a current foundation; and for managers who commission analysis and need to judge it. Companies use it to build data literacy across teams.
No. You need basic spreadsheet use: entering data, sorting and a simple SUM. The course uses Excel or Google Sheets formulas, Power Query steps and DAX measures, and builds them up from Module 1. No SQL or Python is needed.
Not to practise: the Data Lab runs in your browser and needs no licence. For the capstone, Excel or Google Sheets is enough, and Power BI Desktop is free on Windows if you want to build the model yourself. As of November 2026, Copilot in Power BI needs a paid Fabric capacity (F2 or higher) or Power BI Premium (P1 or higher), so the course does not depend on it.
No to both. The course teaches Excel and Power BI as tools for business analysis, and covers concepts that also appear in Microsoft’s PL-300 skills outline (Power Query, star schemas, DAX and time intelligence), but it is not exam preparation and it is not endorsed by or affiliated with Microsoft. If you want the Microsoft certification, add Microsoft’s free PL-300 learning path and take the exam. Excel, Power BI and Copilot are trademarks of Microsoft.
It is tool-agnostic. Examples use Copilot in Excel and Power BI, ChatGPT, Gemini and Claude. You learn what to ask, which data is safe to share with an approved tool, and how to check the answer in any of them with the 5 Rs: read the method, reconcile totals, recompute one number, re-cut the data and test rival explanations.
About 18 hours in total: eight modules of 75 to 90 minutes, a capstone of about five hours and two short assessments. Most people finish in about 6 weeks alongside a full-time job.
You receive the Bodhih Data Analytics with AI: Practitioner credential, with an Open Badge 2.0 digital badge, a public verify page and a PDF that show the skills and your before-and-after score. It is issued when your capstone is accepted and you pass the final quiz. It is a Bodhih credential, not a university degree or a vendor certification.
Yes, if your organisation allows it and you remove or pseudonymise personal data first; never upload confidential or personal data to an unapproved AI tool. If you cannot use work data, use the Kestrel capstone pack provided. You submit a question card, a cleaning log, your analysis, a one-page dashboard, a verification log of at least five AI-assisted steps and a decision memo.
Career-change certificates such as Google’s run to 180 hours or more and spread across many tools; live institute programmes run six to seven months on a fixed schedule. This course is about 18 hours, self-paced, and puts Excel, Power BI and AI-assisted analysis into one workflow on one case, with in-browser labs, an AI-checking module and a graded capstone. It does not teach SQL or Python.
Yes. Company seats cost $159 per seat, with reporting on progress, before-and-after scores and capstone status by role. Write to solutions@bodhih.com or call +91 99000 11601 for seats or an in-house rollout.
The principles transfer. The course notes where Google Sheets differs (for example its QUERY and AI functions) and where Tableau or Looker Studio use different names for the same ideas.
The course is $199 for an individual, or $159 per seat for companies. You can start today with the baseline assessment, then turn Ananya’s vague request into a question card in the first Data Lab. For a team, write to solutions@bodhih.com or call +91 99000 11601.
Starting records where you stand today. That baseline is what the re-measurement is compared against, and it is never rewritten afterwards.
Four hours of hands-on AI literacy for every employee, with a measured before-and-after score.
Run predictive, agile and hybrid projects with AI as your assistant, then lead one through the Project in Crisis simulation.
Lead your team’s AI use safely and lawfully: the 2026 rules, three Scenario Sims and a real AI register entry.
Measure your team first, plan each person’s growth after, or bring a trainer or coach in. Bodhih’s sister companies cover the rest of the journey.