AI Data Analytics & Business Intelligence Training in Vizag
AI Data Analytics and Business Intelligence training in Vizag at Softenant Technologies connects Excel, SQL, Python-assisted analysis and Power BI dashboards with responsible AI support. Learn to turn business questions into traceable metrics, validate generated suggestions and explain the evidence behind a report. The course serves learners in Visakhapatnam who want to combine analytical judgement with practical reporting workflows.
AI-assisted analysis that you can validate
Learners distinguish an AI-generated draft from a validated analysis: they check sources, calculations, filters, KPI definitions and dashboard refresh logic before presenting a conclusion.
AI analytics and business intelligence syllabus
- Excel and SQL analysis workflow. Organise source tables, filter and group records, calculate summaries and compare totals. Check row counts and keys before combining datasets so duplicated records do not inflate a result.
- Python data-cleaning and exploration concepts. Inspect data types, missing values and duplicates; document the cleaning choices. Compare groups and distributions, then confirm that the transformed dataset still represents the business question.
- Power BI modelling, measures and dashboards. Connect tables through appropriate relationships and define measures with a clear business meaning. Test how filters change reported totals and choose visuals that make comparisons easy to read.
- AI-assisted analysis with validation. Use AI support to draft questions, explanations or analytical steps, then inspect the proposed logic. Recalculate a known example and record corrections before presenting generated text as a finding.
- KPIs, storytelling and stakeholder questions. Define each metric, its reporting period and the decision it supports. Explain changes with supporting evidence, distinguish observations from possible causes and state limitations clearly.
Project evidence
How practical work is assessed
A useful analytics portfolio makes the calculation reviewable. Include a data dictionary, a diagram of the reporting tables, named measures and a short validation log. For each KPI, state the unit, reporting period, filters and business definition. Separate an observed change from a possible explanation: a dashboard can show lower sales without proving why customers bought less. Keep AI-generated commentary alongside the corrections you made so the final narrative can be traced to evidence.
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Read Our Google ReviewsChoose the right analytics learning path
This course focuses on validating AI-assisted analysis across the reporting workflow. For a broader starting point, explore the Data Analytics course. For focused dashboard-tool study, see Power BI training in Vizag. Each path supports a different learning goal; discuss your current skills before choosing.
Suggested sales-dashboard practice
Use fictional order-line data with order ID, line ID, order date, product, customer, quantity and net sales. State that one row represents one order line: counting rows is therefore not the same as counting orders. Keep order-line observations in a fact table and use product, customer and date tables for filtering. Before joining them, check that each lookup key is unique and identify sales rows with no matching customer.
For a small acceptance test, give order A two lines worth ₹1,000 and ₹500, and order B one line worth ₹2,000. The expected result is three lines, two distinct orders and ₹3,500 net sales. A duplicated customer lookup must not silently increase that total. Compare the Power BI result with an independent SQL or spreadsheet calculation, then repeat the comparison for one date and one product filter. Document whether cancelled orders and returns are included; otherwise two correct-looking reports can answer different questions.
Ask an AI assistant to draft a short explanation using only the validated totals and metric definitions. Reject any invented reason for the change. Keep the prompt, source extract, calculation, corrections and final chart together. A convincing demonstration reproduces the same totals after refresh, explains a missing-key exception and shows how a filter changes the answer. This is a suggested portfolio exercise using sample data; it does not require access to a real company dataset.
Read more about AI-assisted reporting
The guide to conversational analytics with Microsoft Fabric data agents provides a related example of asking questions about business data. Use it as further reading about permissions, source checks and validation; product availability and licensing depend on the environment.
AI analytics course FAQs
What is AI data analytics and business intelligence?
It combines data preparation, analysis, business metrics and dashboards with AI assistance. An analyst still checks source data, calculations, filters and conclusions before using an AI-assisted result in a business report.
Which tools and skills are included in the syllabus?
The syllabus covers Excel and SQL analysis workflows, Python data-cleaning and exploration concepts, Power BI modelling and dashboards, AI-assisted analysis with validation, and KPI storytelling.
How does this differ from a dedicated Power BI course?
This course connects multiple steps in an analyst workflow, including AI-assisted drafts and their validation. A dedicated Power BI course focuses more directly on the reporting tool, models, measures and dashboard development.
What background is useful before starting?
Comfort with spreadsheets, percentages and basic business questions is useful preparation. SQL queries and Python fundamentals support the technical exercises. Discuss your current experience so the learning sequence and project scope can be matched to your starting point.
How should an AI-generated analysis be checked?
Confirm the source and reporting period, compare totals with a trusted calculation, inspect filters and missing records, and test the result against a known example. Treat explanations as drafts until the underlying numbers support them.
What can an analytics portfolio project include?
The listed projects include a sales-performance Power BI dashboard, customer-segment analysis, an AI-assisted report with source checks and an executive KPI dashboard. Include the question, source description, metric definitions, validation checks and limitations in the project write-up.
Discuss your learning goals
Contact Softenant Technologies about the syllabus, your current skills and suitable project goals. Call +91 9393969628 or send a WhatsApp enquiry.
Visit: Flat No. 101, Geetha Mansion II, Junction, opposite Andhra Bank, Akkayyapalem, Visakhapatnam, Andhra Pradesh 530016, India.