Training course · Perplexity · Data Analysis & Excel

Perplexity for Data Analysis and Excel

AI-powered research with Perplexity: cited answers, source control, Spaces, Deep Research and systematic fact-checking.

Duration
1 day (7 hrs)
Format
Online or on-site
Language
German or English
Participants
open dates or exclusive for your team
Certificate
Certificate of attendance

Overview

This course connects Perplexity with the everyday processes of Data Analysis & Excel. Participants work on realistic scenarios and leave with prompts, workflows and checklists they can use straight away.

Perplexity combines language models with live web search and delivers answers backed by numbered citations. That makes it particularly suited to work where currency and traceability matter: market and competitor monitoring, research into regulation and standards, supplier and partner due diligence, or preparing decision papers. In this practical day you learn to phrase research questions precisely, steer the choice of sources deliberately and use follow-up questions to turn an initial answer into a robust research result.

You work with Spaces, where you group research on a topic, add your own files and set instructions for the team. With Deep Research you produce extensive reports that evaluate many sources and bring them together in a structured way. Quality is a key focus: you learn to assess sources, cross-check claims and recognise typical failure patterns such as outdated or misattributed information. We also address data protection and licensing questions for business use. You leave with a reusable research process tailored to your own function.

In almost every department, decisions are made on the basis of spreadsheets, exports and reports – and much working time goes into cleaning, combining and analysing that data. Generative AI dramatically lowers the barrier to sophisticated analysis. It explains and writes Excel formulas, builds Power Query steps and macros, suggests pivot analyses and charts, generates Python or SQL code for larger datasets and drafts commentary on KPIs. AI agents can prepare recurring reports automatically. This empowers business users to answer questions themselves that once meant waiting for IT or an analytics team. What remains essential is checking results rigorously and keeping an eye on data quality.

The course is delivered live – either online in a virtual classroom or on-site in our training rooms in Munich, Salzburg, Berlin, Augsburg, Hanover, Bremen, Stuttgart and Kaiserslautern. We can also run it exclusively for your team.

Who is this course for?

  • Business users who work regularly with Excel and reports
  • Controllers and finance staff
  • Sales and marketing analysts
  • Business analysts and reporting teams
  • Team leads responsible for KPIs

Typical challenges we address

  • Time-consuming manual data cleaning and copy-and-paste processes
  • Complex formulas and macros that only a few people understand
  • Data from different systems in inconsistent formats
  • Error-prone spreadsheets without traceable calculation logic

What you will be able to do

  • You phrase precise research questions and steer the choice of sources deliberately.
  • You assess sources by origin, currency and reliability and trace claims back to the original.
  • You organise research in Spaces and combine web sources with your own documents.
  • You use Deep Research to produce structured reports and decision support.
  • Create and understand complex Excel formulas, Power Query steps and macros with AI.
  • Clean, analyse and visualise data faster and more transparently with AI support.
  • Automate recurring reports and check AI results methodically for accuracy.

Course outline

Module 1 Foundations of AI-powered research
  • How it works: language model, live web search and citations
  • How it differs from a classic search engine and a general chat assistant
  • Framing research questions precisely: time frame, region, source type
  • Follow-ups and drilling down instead of one-off queries
Module 2 Steering and assessing sources
  • Narrowing sources: web, academic literature, your own files
  • Targeting primary sources, official websites and specialist publications
  • Assessing source quality: origin, currency, vested interests
  • Tracing quotes and figures back to the original source
Module 3 Spaces and teamwork
  • Setting up Spaces for topics, projects or client requests
  • Uploading your own documents and combining them with web sources
  • Storing instructions for consistent output formats
  • Sharing research and turning it into pages or reports
Module 4 Deep Research and structured reports
  • Tackling broad questions with Deep Research
  • Producing market overviews, competitor comparisons and topic briefs
  • Turning results into tables, side-by-side comparisons and executive summaries
  • Recognising the limits of in-depth research and refining results
Module 5 Fact-checking, data protection and workflow
  • Systematic fact-checking: counter-research and spotting contradictions
  • Typical failure patterns: outdated, misattributed or fabricated details
  • Data protection, enterprise licences and handling confidential queries
  • A reusable research process and source documentation
Module 6 Practice lab: Data Analysis & Excel
  • Writing and explaining formulas
  • Cleaning and combining data
  • Analyses and charts
  • Python and SQL for large datasets
  • Commenting on KPIs
  • Automating reports
Module 7 Data protection, quality & transfer to daily work
  • Spreadsheets often contain personal data about customers or employees as well as confidential business figures, which may only be processed in approved AI environments and ideally anonymised or aggregated. Analyses relating to employees may be subject to works council rights, and responsibility for the accuracy of reports remains with the business unit.
  • Ground rules for safe AI use in your team
  • Checking results: facts, sources, tests and the four-eyes principle
  • Personal action plan for the next 30 days
  • Open questions, outlook and resources for further learning

Practical scenarios from your field

Writing and explaining formulas

AI creates formulas such as XLOOKUP, SUMIFS or dynamic arrays from a plain-language description and explains existing nested formulas step by step. Common pitfalls are pointed out as well.

Cleaning and combining data

For inconsistent exports, AI suggests cleaning steps, builds Power Query queries or scripts for merging, de-duplicating and normalising, and documents the transformations. The steps can be repeated with one click next time.

Analyses and charts

From a business question, AI derives suitable pivot tables, metrics and chart types and helps present results clearly and accurately. Misleading displays such as truncated axes are avoided.

Python and SQL for large datasets

When Excel reaches its limits, AI generates Python or SQL code for analysing larger datasets, which business users run, understand and adapt step by step.

Commenting on KPIs

Variances, trends and anomalies in reports are translated into clear written commentary for management and departments; hypotheses about causes are checked by experts. Reports become self-explanatory in meetings.

Automating reports

Recurring monthly or weekly reports are automated with macros, scripts or AI agents so that data is retrieved, prepared and distributed as a report. Manual steps and transfer errors largely disappear.

Prerequisites

Experience with online research in a professional context; no prior AI knowledge is required. Confident everyday use of Excel; no programming skills required.

Technical requirements

A laptop with an up-to-date browser and a Perplexity account; we recommend Pro or Enterprise access to use Spaces with files and Deep Research in full.

Data protection & compliance

Spreadsheets often contain personal data about customers or employees as well as confidential business figures, which may only be processed in approved AI environments and ideally anonymised or aggregated. Analyses relating to employees may be subject to works council rights, and responsibility for the accuracy of reports remains with the business unit.

Suggested dates

All dates are suggestions – your preferred date is always possible.

  • Wed, 25 November 2026 Online Request
  • Thu, 3 December 2026 Munich Request
  • Fri, 11 December 2026 Online Request
  • Thu, 7 January 2027 Hanover Request
  • Thu, 14 January 2027 Online Request
  • Wed, 27 January 2027 Berlin Request

Frequently asked questions

How does Perplexity differ from ChatGPT or a search engine?

A search engine returns a list of links, and a general chat assistant often answers from its training data. Perplexity searches the web at the moment you ask, summarises what it finds and backs each statement with citations. That makes it particularly well suited to current, verifiable research – although you still need to assess the sources yourself.

Can I rely on Perplexity's citations?

Citations make verification much easier, but they do not replace it. Occasionally a statement is attributed to a source that does not say quite that, or an outdated page is used. In the course you learn a lean fact-checking routine that lets you quickly verify critical statements against the original source.

Is Perplexity suitable for confidential research within a company?

Enterprise plans offer additional commitments on data use and administration. Even so, you should not put trade secrets or personal data into queries unless your internal policy explicitly allows it. We discuss how to phrase queries so that sensitive details stay protected while the research remains useful.

Do I need programming skills for data analysis with AI?

No. Good Excel skills are enough to start. AI generates formulas, Power Query steps and, where needed, Python code that you learn to follow step by step. What matters is being able to sanity-check results. The course focuses on exactly these checking techniques alongside hands-on practice.

How reliable are AI-generated formulas and analyses?

Often very good, but not flawless: references, edge cases or wrong assumptions about the data structure lead to silent errors. That is why you verify results with spot checks, control totals and cross-calculations. The course teaches a simple checking routine to apply to every AI-assisted analysis.

How much does “Perplexity for Data Analysis and Excel” cost?

The course costs a flat €949 plus VAT per participant – whether online or on-site. This includes 1 training day(s) with a live trainer, course materials, a lab environment and a certificate of attendance.

Can I suggest my own date?

Yes. Our suggested dates are just a guide. In the request form you can always describe your preferred date or period in free text – we check availability and confirm within one working day.

Is the course online or on-site?

Both are possible: live online in a virtual classroom or on-site in Munich, Salzburg, Berlin, Augsburg, Hanover, Bremen, Stuttgart or Kaiserslautern. We can also run the course exclusively for your team.

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