Data Analyst Lab Exercises (100+)

🔰 Basic Level (30+ Exercises)

 

Goal: Build a strong foundation in data collection, preparation, and exploratory analysis.

 

Excel & Spreadsheet Skills

  • Clean and transform raw data using Excel functions.

  • Create pivot tables for summarizing data.

  • Build dashboards using slicers and charts.

  • Use VLOOKUP, HLOOKUP, INDEX, MATCH.

  • Automate reports using Excel Macros.

SQL Basics

  • Write SQL queries to select and filter data.

  • Use GROUP BY and aggregate functions.

  • Perform JOIN operations on multiple tables.

  • Write subqueries and nested SELECTs.

  • Create and manipulate tables using DDL/DML.

Python for Data Analysis

  • Load and inspect data using Pandas.

  • Handle missing values and duplicates.

  • Generate descriptive statistics.

  • Visualize data using Matplotlib.

  • Merge, group, and filter datasets.

Data Cleaning

  • Identify and handle outliers.

  • Normalize and scale data.

  • Format datetime fields.

  • Rename columns and reindex data.

  • Convert categorical to numerical data.

 

🚀 Intermediate Level (40+ Exercises)

 

Goal: Extract actionable insights through visualization, statistics, and business-focused analysis.

 

Data Visualization

  • Create bar, pie, and line charts using Seaborn.

  • Develop dashboards in Power BI/Tableau.

  • Plot geospatial data on maps.

  • Create heatmaps for correlation analysis.

  • Build interactive filters and slicers.

Statistical Analysis

  • Conduct hypothesis testing (t-test, chi-square).

  • Analyze correlation and causation.

  • Perform regression analysis.

  • Calculate confidence intervals and p-values.

  • Run ANOVA for multi-group comparison.

Advanced SQL

  • Use window functions (ROW_NUMBER, RANK).

  • Create complex CTEs (Common Table Expressions).

  • Perform time-series operations in SQL.

  • Write procedures and functions.

  • Optimize slow queries using indexing.

Python Analysis Projects

  • Analyze marketing campaign performance.

  • Segment customers using clustering.

  • Forecast product sales using linear regression.

  • Build a KPI dashboard with Plotly Dash.

  • Automate reporting with Python scripts.

 

🧠 Advanced Level (40+ Exercises)

 

Goal: Master advanced analytics, modeling, and deployment of data-driven solutions.

 

Predictive Analytics

  • Build classification models for churn prediction.

  • Use logistic regression for binary outcomes.

  • Perform feature selection and engineering.

  • Evaluate models using ROC-AUC, confusion matrix.

  • Compare model performance using cross-validation.

Big Data Tools

  • Query data using Hive and Spark SQL.

  • Use PySpark for distributed data processing.

  • Perform ETL operations using Apache Airflow.

  • Clean and analyze data in Google BigQuery.

  • Connect BI tools with cloud databases.

Business Intelligence & Reporting

  • Build complete dashboards in Power BI/Tableau.

  • Implement row-level security in Power BI.

  • Create data stories for business use-cases.

  • Publish dashboards to Power BI service.

  • Design executive reports for C-suite.

Domain-Focused Projects

  • Retail: Analyze product performance & sales trends.

  • Finance: Predict loan default and visualize risk.

  • Healthcare: Analyze patient data and hospital KPIs.

  • HR: Analyze attrition and build retention models.

  • Logistics: Optimize delivery and forecast demand.

Capstone Projects

 

  • Customer 360 dashboard for a retail client.

  • Predictive analysis for e-commerce cart abandonment.

  • Sales forecasting dashboard with seasonal trends.

  • Real-time data dashboard using APIs and Python.

  • End-to-end data pipeline from raw data to BI dashboard.

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