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Data Analytics Internship

Turn raw data into answers: Excel, SQL, Python and dashboards, with every task based on realistic business questions.

ExcelSQLPython (pandas)Power BI / Tableau

What you'll work on

Full syllabus: all 60 graded tasks

These are the actual tasks the internship portal assigns for this domain. You submit each one with proof (a GitHub link or document) and a mentor reviews it.

Level 1: Foundations (22 tasks)
  1. Excel essentials. Clean a messy sales sheet: remove duplicates, fix dates, split columns and use VLOOKUP/XLOOKUP. Share before/after files.
  2. Pivot tables. Answer 8 business questions (top products, monthly trend, region share) with pivot tables and charts.
  3. Descriptive statistics. Calculate mean, median, mode, standard deviation and percentiles for a dataset and explain what each tells you.
  4. SQL basics. Write 20 queries on a sample sales database: filters, sorting, GROUP BY, HAVING and aggregates.
  5. SQL joins. Answer 10 questions that need INNER, LEFT and self joins, and explain each result.
  6. Data cleaning checklist. Write a checklist for missing values, outliers, duplicates and inconsistent categories, and apply it to a public dataset.
  7. Charts that work. Make the same data into 5 chart types and explain which chart fits which question, and why pie charts often mislead.
  8. Business questions. For a retail shop, write 15 analysis questions and the metric (KPI) that answers each.
  9. Python for analysis. Load a CSV with pandas, clean it, group it and plot 3 charts in a Jupyter notebook.
  10. Google Sheets dashboard. Build a one-page dashboard in Google Sheets with a filter and 4 charts that update automatically.
  11. Survey analysis. Run a 10-question survey with 30+ responses and summarise the results with charts and 3 insights.
  12. Data ethics. Write a one-page note on privacy, consent and bias in analytics with 2 real-world examples.
  13. Excel functions drill. Use IF, SUMIFS, COUNTIFS, INDEX-MATCH, TEXT and date functions to answer 12 questions on one sheet.
  14. Conditional formatting. Highlight top/bottom performers, duplicates and targets with conditional formatting rules.
  15. Data validation and templates. Build an input template with drop-downs and validation so data entry stays clean.
  16. Percentages and growth. Calculate share, growth, CAGR and contribution for a sales dataset and explain each.
  17. Read a business report. Pick a public company report and extract 5 KPIs with how they are calculated.
  18. Spreadsheet best practices. Restructure a messy workbook into raw data, calculations and report sheets with named ranges.
  19. Charts in Excel. Build combo, waterfall and sparkline charts and explain when each is useful.
  20. Survey design. Design an unbiased 10-question survey with the right question types and scales.
  21. Sampling basics. Explain random, stratified and convenience sampling with examples and risks.
  22. Analytics glossary. Write definitions and formulas for 25 common business metrics.
Level 2: Building real features (20 tasks)
  1. Power BI or Tableau dashboard. Build an interactive sales dashboard with slicers, drill-down and a KPI row. Publish or share screenshots.
  2. Data modelling. Create a star schema (fact + dimension tables) for a sales dataset and explain the relationships.
  3. DAX or calculated fields. Write 10 measures: running total, YoY growth, % of total, moving average, top-N and more.
  4. Advanced SQL. Use window functions, CTEs and CASE to answer 10 harder questions (rankings, cohorts, retention).
  5. Cohort and retention analysis. Build a monthly cohort retention table from order data and explain what it shows.
  6. A/B test analysis. Analyse an A/B test dataset: conversion rates, significance test and a clear recommendation.
  7. Customer segmentation. Segment customers by RFM (recency, frequency, monetary) and suggest an action for each segment.
  8. Time-series trend. Analyse monthly sales: trend, seasonality and a simple forecast for the next 3 months.
  9. Automate a report. Write a Python script that pulls data, builds charts and emails or saves a weekly PDF/HTML report.
  10. Storytelling with data. Turn one analysis into a 6-slide story: question, method, 3 insights, recommendation.
  11. Power Query cleaning. Clean and combine 3 messy files with Power Query steps that refresh automatically.
  12. Python vs Excel analysis. Solve the same 5 questions in Excel and pandas and compare effort and reproducibility.
  13. Geographic analysis. Map sales by state or district and explain regional patterns.
  14. Customer lifetime value. Estimate CLV from order data and suggest how marketing could use it.
  15. Statistical significance. Run t-tests or chi-square tests on two groups and explain the p-value in plain words.
  16. Google Analytics exploration. Explore the GA4 demo account and answer 8 questions about traffic and conversions.
  17. Window functions practice. Solve 10 problems with LAG, LEAD, running totals and moving averages in SQL.
  18. Dashboard design critique. Critique 3 public dashboards for clarity and redesign one.
  19. Price and promotion analysis. Analyse how discounts affect units and revenue, and recommend a promotion strategy.
  20. Supply chain analysis. Analyse inventory and delivery data to find stock-outs and slow suppliers.
Level 3: Professional work and capstone (18 tasks)
  1. End-to-end analytics project. Pick a public dataset, define questions, clean, analyse, build a dashboard and write recommendations.
  2. Funnel analysis. Measure a sign-up or purchase funnel, find the biggest drop-off and propose 3 fixes with expected impact.
  3. Data pipeline to a warehouse. Load raw data into a database or BigQuery sandbox on a schedule and build your dashboard on top.
  4. Executive dashboard. Design a one-screen dashboard for a manager with KPIs, targets, alerts and comments.
  5. Market basket analysis. Find products often bought together and suggest bundles, with support/confidence numbers.
  6. Dashboard performance. Optimise a slow dashboard (aggregations, model, filters) and report load time before and after.
  7. Portfolio case study. Publish your best project with the dashboard link, notebook and a written case study.
  8. Present to stakeholders. Record a 5-minute presentation of your project for a non-technical audience.
  9. Marketing analytics dashboard. Track spend, clicks, leads and cost per lead by channel with drill-down.
  10. HR analytics. Analyse attrition data: who leaves, when and why, with recommendations.
  11. Automated data quality checks. Write checks for nulls, duplicates, ranges and freshness that run before the dashboard refreshes.
  12. Self-service analytics guide. Write a guide so a manager can filter and read your dashboard without help.
  13. Analytics interview prep. Solve 10 SQL and 5 case questions typical of data analyst interviews and record your answers.
  14. Forecasting with Excel and Python. Forecast monthly demand with Excel's forecast sheet and a Python model and compare accuracy.
  15. Churn analysis. Find drivers of customer churn and estimate the revenue saved by reducing churn by 5%.
  16. Data storytelling video. Record a 5-minute data story with your dashboard for a non-technical audience.
  17. Freelance analytics project. Help a small business or college club with a real data question and deliver a report.
  18. Analyst portfolio. Publish 3 projects with dashboards, notebooks and write-ups on GitHub or a portfolio site.

Real software work, not just training

Every Data Analytics task is reviewed by our team and saved to your portfolio. Interns who pass the readiness check can take real work from Training Trains, W3AppDevelopers and DomainHostly projects on the Company Work board.

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Frequently asked questions

Do I need experience to join the Data Analytics internship?+

Basic maths and Excel are enough to begin; SQL and Python are taught through the tasks.

What will I do in the Data Analytics internship?+

You complete 60 graded tasks in three levels: foundations, real features, and professional work. Each task is submitted with GitHub or document proof and reviewed. The final capstone is: Analyst portfolio.

Do I get a certificate for the Data Analytics internship?+

Yes. After your required tasks are approved, your attendance is above 75% and you pass the proctored final exam, you get a graded completion certificate that anyone can verify at trainingtrains.com/verify.

Can I do the Data Analytics internship online?+

Yes. You can do it fully online from home, or offline at our Erode and Perundurai branches. Tasks, quizzes and mentor reviews are the same in both modes.

Available both online (work from home) and offline at our Erode and Perundurai branches - see the full internship program details for departments, duration, and the application process.

Apply for the Data Analytics internship