Build a data analytics portfolio through a free virtual internship. Review practical milestones, submission evidence and the mentor feedback process.
Choose the spreadsheet or analysis tools required by your milestone. Keep an untouched source dataset and a separate working copy, and use public or properly anonymised data for shared portfolio evidence.
These are the published project topics and deliverables. Your selected duration determines the required tasks in your workspace; use each task brief for its materials and evidence requirements.
Download your official Offer Letter from your workspace, format a professional LinkedIn announcement tagging #EdBytes, and submit your live public post URL for mentor verification.
Clean a raw business sales spreadsheet by eliminating duplicate rows, fixing date formats, calculating profit margins, and summarizing sales performance using Pivot Tables and Charts.
Analyze a consumer dataset in Python using Pandas, NumPy, and Matplotlib/Seaborn to inspect distributions, handle missing values, and visualize correlations.
Write 5 fundamental SQL queries on a simple sales table using basic SELECT, WHERE filtering, ORDER BY sorting, and simple COUNT/SUM functions.
Build a simple, interactive 1-page business dashboard by connecting a Google Sheet to Google Looker Studio (free & no-code) with 3 clear charts: Scorecard, Bar Chart, and Pie Chart.
After your required tasks are approved, your workspace shows the optional certificate and evaluation report, its saved price and download availability. Public verification distinguishes registration from an issued credential; it does not guarantee employment or accreditation.
A data analytics virtual internship should show how you move from a question to a supported conclusion. In this project track, organise your analysis so a reviewer can trace the source data, cleaning decisions, calculations, and charts. State the limits of your findings instead of treating every correlation as an explanation.
Choose the spreadsheet or analysis tools required by your milestone. Keep an untouched source dataset and a separate working copy, and use public or properly anonymised data for shared portfolio evidence.
This educational project track is for students and early-career learners who want to practise data analytics and document their own work. Check the milestone tools and prerequisites against your current skills. Participation is not employment or a promise of placement.
Registration offers 7-, 15-, 30-, 45-, and 60-day options. Your selected programme and active workspace determine the required tasks and deadline. The published milestones show the project topics; check your workspace for the tasks assigned to your enrolment.
Enrolment, learning materials, submissions, and mentor feedback are free. The certificate is optional. Its price is saved when you register; a saved ₹0 price requires no certificate payment. Check its current price in your workspace; see the terms and refund policy before purchasing.
Submit the evidence named in the task brief, such as a repository, prototype, report, public post, or walkthrough. Open each link while signed out and document your own contribution. Follow the project proof checklist before sending it for review.
