Start with Easy & Impactful Data Analytics Projects

Get hands-on experience with beginner-level projects in Excel, SQL, Python, and Power BI. Learn to clean, analyze, and visualize data with real-world examples.

Project 1: Retail Sales Analysis & Forecasting System

Objective: To analyze historical sales data from a retail store to uncover trends, patterns, and seasonality, and to forecast future sales using data visualization and predictive modeling. 

Core Features

  • Load and clean sales data using Pandas
  • Perform exploratory data analysis (EDA) to find top-selling products, regions, and seasons
  • Analyze sales trends over time (monthly, yearly)
  • Use groupby, pivot tables, and aggregation functions
  • Forecast future sales using moving averages, linear regression, or basic time-series models
  • Visualize insights using Matplotlib and Seaborn (line plots, bar charts, heatmaps) 

Tech Stack

  • Python
  • Pandas, NumPy
  • Matplotlib, Seaborn

Learning Outcomes

  • Mastery of data cleaning and wrangling
  • Time-series trend analysis
  • Business decision support with visual storytelling
  • Hands-on with forecasting techniques
 

Project 2: Student Attendance & Academic Performance Correlation

Objective: To explore the relationship between student attendance and academic performance to uncover patterns, risks, and interventions.

Core Features

  • Load datasets containing student IDs, attendance %, subject-wise marks
  • Calculate correlation between attendance and performance
  • Group students into attendance brackets (e.g., <50%, 50–75%, >75%)
  • Visualize performance distribution by attendance bracket
  • Identify high-potential students at risk due to low attendance 

Tech Stack

  • Python
  • Pandas, NumPy
  • Matplotlib, Seaborn

Learning Outcomes

  • Correlation analysis using real-world education data
  • Risk detection based on academic predictors
  • Attendance trend visualization and threshold grouping
  • Data-backed suggestions for early intervention
 

Project 3: Movie Ratings & Sentiment Analysis

Objective: To analyze movie ratings and reviews to identify trends in viewer preferences and uncover patterns in sentiment.

Core Features

  • Import and clean datasets like IMDb or TMDb datasets
  • Analyze movie metadata (genre, runtime, year, rating)
  • Group and visualize average ratings by genre/year
  • Perform sentiment analysis on review text using TextBlob
  • Create word clouds from reviews of best/worst movies

Tech Stack

  • Python
  • Pandas, NumPy
  • Matplotlib, Seaborn
  • (Optional) TextBlob for basic sentiment analysis

Learning Outcomes

  • Data preprocessing for text and numeric fields
  • Text-based data visualization and sentiment analysis
  • Creating genre-based and time-based dashboards
  • Merging and aggregating multi-source data
 

Project 4: Food Menu Item Popularity & Pricing Analysis

Objective: To analyze food order data and evaluate item-wise popularity, pricing strategy, and category-wise performance.

Core Features

  • Load restaurant order data (item name, category, price, quantity sold)
  • Calculate item-wise and category-wise revenue
  • Identify most profitable vs most popular items
  • Detect underpriced or overpriced items based on performance
  • Visualize results using pie charts, bar plots, and heatmaps

Tech Stack

  • Python
  • Pandas, NumPy
  • Matplotlib, Seaborn 

Learning Outcomes

  • Menu engineering using analytics
  • Performance vs profitability differentiation
  • Visualizing food category trends
  • Pricing optimization strategy development
 

Project 5: E-Commerce Customer Behavior Analysis

Objective: To analyze e-commerce customer transactions and browsing behavior to derive marketing insights.

Core Features

  • Clean and analyze transactional data (orders, browsing logs)
  • Identify purchasing patterns (frequency, recency, monetary)
  • Segment customers using RFM Analysis (Recency, Frequency, Monetary)
  • Visualize top-selling products and categories
  • Study conversion rates and cart abandonment

Tech Stack

  • Python
  • Pandas, NumPy
  • Matplotlib, Seaborn 

Learning Outcomes

  • Understanding customer segmentation techniques
  • Applying real e-commerce analytics concepts
  • Visualizing funnel and product trends
  • Business-focused decision-making using Python
 

Project 6: Stock Market Data Analysis & Trend Visualization

Objective: To analyze historical stock market data of multiple companies, identify market trends, compare performance, and visualize volatility.

Core Features

  • Import historical stock data from CSV or API (e.g., Yahoo Finance)
  • Calculate moving averages, daily returns, cumulative returns
  • Compare stock performance across sectors or companies
  • Identify bullish/bearish trends using candlestick-like plots
  • Visualize volatility, correlation matrices, and stock risk

Tech Stack

  • Python
  • Pandas, NumPy
  • Matplotlib, Seaborn

Learning Outcomes

  • Real-world financial data handling
  • Rolling window operations (SMA, EMA)
  • Risk-return profiling
  • Comparative and correlation-based visualizations
 

Project 7: Library Book Borrowing & Reading Habit Analysis

Objective: To analyze library borrowing data to understand reading preferences, peak borrowing seasons, and book popularity.

Core Features

  • Load datasets (book title, genre, borrow date, return date, member ID)
  • Calculate average borrowing duration by genre
  • Identify most borrowed authors, titles, and genres
  • Detect borrowing trends during exams, holidays, etc.
  • Visualize genre-wise readership and top borrowed books

Tech Stack

  • Python
  • Pandas, NumPy
  • Matplotlib, Seaborn

Learning Outcomes

  • Time-based borrowing behavior analysis
  • Genre-wise trend insights
  • Duration-based habit identification
  • Visual storytelling in library and education sector
 

Project 8: Student Performance Analytics System

Objective: To analyze student marks, attendance, and participation across multiple classes/subjects and derive academic performance insights.

Core Features

  • Analyze student scores across terms, subjects, and activities
  • Detect top performers, weak areas, subject difficulty trends
  • Compare performance based on gender, background, attendance
  • Visualize grade distributions, averages, and subject trends
  • Create predictive insights on pass/fail likelihood

Tech Stack

  • Python
  • Pandas, NumPy
  • Matplotlib, Seaborn

Learning Outcomes

  • Education-based analytics modeling
  • Multi-variable comparison and filtering
  • Performance dashboards for faculty
  • Stakeholder-friendly data storytelling
 

Project 9: Road Accident Data Analytics & Prevention Insights

Objective: To analyze traffic and road accident datasets to identify key accident-prone zones, timings, and conditions, and suggest preventive measures.

Core Features

  • Load accident datasets (location, cause, vehicle, time, casualty)
  • Group by state/city to find hotspots and black spots
  • Identify major causes (drunken driving, speed, weather, etc.)
  • Analyze accident severity by time of day, road type
  • Visualize density heatmaps, accident trend lines, fatality rates 

Tech Stack

  • Python
  • Pandas, NumPy
  • Matplotlib, Seaborn

Learning Outcomes

  • Spatial and temporal pattern analysis
  • Cause-effect-based data correlations
  • Use of heatmaps, histograms, and pie charts for awareness
  • Policy-level decision support from data insights
 

Project 10: Mobile App Usage and Retention Analytics

Objective: To analyze user engagement, session duration, and app retention data to identify high-usage features, drop-off points, and retention trends.

Core Features

  • Import app usage data (user ID, session duration, feature used, activity timestamp)
  • Identify frequently used features and active time slots
  • Analyze daily/weekly/monthly active users
  • Detect uninstall or inactivity trends over time
  • Visualize retention curves and feature popularity charts 

Tech Stack

  • Python
  • Pandas, NumPy
  • Matplotlib, Seaborn

Learning Outcomes

  • Behavioral analysis using digital product data
  • Feature-based engagement segmentation
  • Retention and churn visualization
  • App usage funnel creation using time-based grouping 
 

Project 11: Insurance Claims and Risk Pattern Analysis

Objective: To analyze insurance claim data to uncover risk-prone customer profiles, high-claim periods, and policy-wise claim distribution.

Core Features

  • Load claim datasets (policy type, claim amount, date, age, region, gender)
  • Analyze claim frequency by age group and policy type
  • Detect regions or months with frequent high-value claims
  • Compare male vs female claim trends
  • Visualize claim distributions, risk heatmaps, and policy performance

Tech Stack

  • Python
  • Pandas, NumPy
  • Matplotlib, Seaborn

Learning Outcomes

  • Risk factor identification using claim history
  • Category-wise insurance usage trends
  • Time and demographic-based insights
  • Visual risk analysis and cost exposure patterns
 

Project 12: Hospital Patient Admission & Treatment Pattern Analysis

Objective: To analyze hospital admission records to find patterns in treatment, disease occurrence, and healthcare resource utilization.

Core Features

  • Import patient data (age, gender, disease, treatment duration, cost)
  • Find most frequent diagnoses and seasonal disease spikes
  • Analyze average treatment time/cost by disease or age group
  • Compare male vs female disease ratios
  • Visualize patient inflow, discharge trends, resource usage

Tech Stack

  • Python
  • Pandas, NumPy
  • Matplotlib, Seaborn

Learning Outcomes

  • Healthcare domain analytics
  • Handling sensitive or anonymized data
  • Creating hospital KPIs through visual dashboards
  • Decision-support metrics for resource allocation
 

Project 13: Hotel Booking Cancellation and Trend Analysis

Objective: To analyze hotel booking datasets to understand cancellation behavior, peak booking times, and customer preferences.

Core Features

  • Clean and process hotel booking data (dates, guest info, booking status)
  • Study seasonal booking trends, peak and off-peak periods
  • Predict likelihood of cancellations using trends
  • Analyze lead time, room type, meal type preferences
  • Visualize cancellation rates, lead time trends, booking volumes

Tech Stack

  • Python
  • Pandas, NumPy
  • Matplotlib, Seaborn

Learning Outcomes

  • Understanding booking behavior analytics
  • Handling date-based feature engineering
  • Data visualization for marketing and operations
  • Pre-cancellation risk flagging using statistical patterns
 

Project 14: Online Course Platform – Learner Activity & Dropout Analysis

Objective: To analyze student engagement on an e-learning platform and identify reasons for dropouts or low engagement.

Core Features

  • Analyze login frequency, video views, assignments, and course completion
  • Identify dropout stages and high-risk students
  • Group students based on completion percentage and activity
  • Visualize weekly active users, engagement funnels, heatmaps
  • Recommend interventions to improve completion

Tech Stack

  • Python
  • Pandas, NumPy
  • Matplotlib, Seaborn

Learning Outcomes

  • Real-world EdTech analytics experience
  • Engagement funnel and behavior segmentation
  • Deriving actionable insights for improving e-learning
  • Complex user-behavior pattern analysis
 

Project 15: Housing Loan Disbursement & Repayment Pattern Analysis

Objective: To analyze housing loan data to evaluate borrower behavior, repayment trends, and region-wise disbursement volume.

Core Features

  • Load loan data (customer ID, amount, tenure, EMI, region, start date, status)
  • Compare sanctioned vs repaid amounts
  • Detect defaults and identify repayment trends by region or tenure
  • Group loans by income segment or age bracket
  • Visualize monthly EMI trends and region-wise disbursement heatmaps

Tech Stack

  • Python
  • Pandas, NumPy
  • Matplotlib, Seaborn

Learning Outcomes

  • Finance and lending behavior analysis
  • Loan lifecycle tracking
  • EMI trend visualization
  • Data-backed suggestions for credit control
 

Project 16: Job Market Analytics from Online Job Portals

Objective: To analyze job listings data to uncover trends in skill demand, salary ranges, and job availability across roles, locations, and industries.

Core Features

  • Import job listings from Kaggle or scraped datasets (title, skills, company, location, salary)
  • Analyze demand for top programming languages, tools, and platforms
  • Compare job counts by city, experience level, or industry
  • Identify most frequently required soft & hard skills
  • Visualize trends using bar charts, word clouds, heatmaps

Tech Stack

  • Python
  • Pandas, NumPy
  • Matplotlib, Seaborn

Learning Outcomes

  • Real-world career insight generation
  • Text data parsing and keyword extraction
  • Understanding job dynamics and skill relevance
  • Trend-based comparison with visual proof
 

Project 17: Health & Fitness Tracker Analytics

Objective: To analyze activity logs from fitness trackers to detect trends in user activity, sleep, and calorie burn for wellness insights.

Core Features

  • Import logs (user ID, steps, calories, sleep hours, heart rate, date)
  • Track weekly activity changes and weekend patterns
  • Compare activity levels across user groups (age, gender)
  • Analyze average sleep duration and quality
  • Visualize step count trends, calorie burn charts, and heart rate heatmaps

Tech Stack

  • Python
  • Pandas, NumPy
  • Matplotlib, Seaborn

Learning Outcomes

  • Health domain analytics
  • Behavior and performance pattern detection
  • Personal wellness visualization
  • Fitness data grouping and time-slicing
 

Project 18: Electricity Consumption & Bill Analysis for Households

Objective: To analyze monthly electricity consumption across households to detect high-usage months, overbilling, and cost-saving opportunities.

Core Features

  • Import monthly usage data (household ID, units consumed, billing amount, month/year)
  • Compare consumption across seasons and households
  • Identify abnormal billing or excessive consumption trends
  • Visualize consumption patterns and peak months
  • Recommend usage optimization based on trends

Tech Stack

  • Python
  • Pandas, NumPy
  • Matplotlib, Seaborn 

Learning Outcomes

  • Utility usage trend analysis
  • Cost vs consumption visualization
  • Time-based resource optimization
  • Household-level comparison and behavior insights
 

Project 19: Supermarket Basket & Product Affinity Analysis

Objective: To analyze supermarket transaction data to uncover patterns in product bundling, peak sale items, and customer purchase behavior.

Core Features

  • Load transaction datasets (item name, quantity, bill amount, timestamp)
  • Perform basket-level grouping and item frequency analysis
  • Identify top 10 co-purchased items (affinity analysis)
  • Compare seasonal vs non-seasonal product popularity
  • Visualize product frequency, heatmaps, pie charts for category sales

Tech Stack

  • Python
  • Pandas, NumPy
  • Matplotlib, Seaborn

Learning Outcomes

  • Market basket analysis fundamentals
  • Grouping and filtering multi-item datasets
  • Product trend insights for inventory and marketing
  • Real-world retail data application
 

Project 20: Public Event Participation Analysis

Objective: To analyze participant data from community or cultural events to determine engagement levels, demographic trends, and event popularity.

Core Features

  • Load participant data (name, age, gender, event type, location, feedback rating)
  • Group participants by event type, age bracket, and location
  • Identify popular events and demographics with high turnout
  • Analyze satisfaction ratings and repeat participation
  • Visualize participation trends using bar charts, pie charts, and age-group histograms

Tech Stack

  • Python
  • Pandas, NumPy
  • Matplotlib, Seaborn

Learning Outcomes

  • Community engagement data analysis
  • Event planning and feedback-based evaluation
  • Demographic pattern discovery
  • Visual insights for outreach improvement
 

Project 21: Real Estate Market Price Trend Analysis

Objective: To analyze real estate property listings and transaction data to understand pricing trends, regional demand, and investment opportunities.

Core Features

  • Import datasets with property type, price, size, location, and amenities
  • Analyze price trends by city, locality, and property type
  • Calculate price per sq. ft and compare across regions
  • Identify high-growth and underpriced areas
  • Visualize price distributions, trendlines, and heatmaps

Tech Stack

  • Python
  • Pandas, NumPy
  • Matplotlib, Seaborn

Learning Outcomes

  • Geo-based and price-based segmentation
  • Multi-variable real estate data interpretation
  • Investment hotspot detection using data
  • Practical exposure to high-value industry datasets
 

Project 22: Online Product Review Text Analytics (EDA only)

Objective: To analyze product review data to discover customer sentiment, most-used keywords, and common complaints or praises.

Core Features

  • Import review data (product ID, rating, review text)
  • Group reviews by rating and product category
  • Extract top keywords using basic text processing
  • Identify positive vs negative review patterns
  • Visualize word frequency with bar charts and word clouds

Tech Stack

  • Python
  • Pandas, NumPy
  • Matplotlib, Seaborn
  • (Optional: WordCloud or TextBlob for text processing)

Learning Outcomes

  • Text data analysis using Pandas
  • Sentiment segmentation through ratings
  • Keyword frequency extraction
  • Customer feedback visualization
 

Project 23: YouTube Channel Performance Analytics

Objective: To analyze video-level metrics from a YouTube channel dataset and determine content success factors and audience engagement patterns.

Core Features

  • Use data like views, likes, dislikes, comments, video length, upload time
  • Analyze engagement ratios (likes/views, comments/views)
  • Detect trends for best-performing content types
  • Compare short vs long video performance
  • Visualize subscriber growth and video virality

Tech Stack

  • Python
  • Pandas, NumPy
  • Matplotlib, Seaborn

Learning Outcomes

  • Content analytics for social platforms
  • Audience engagement metric calculation
  • Viral content pattern detection
  • Strategic content planning using data
 

Project 24: Credit Card Spending & Category Trend Analysis

Objective: To analyze anonymized credit card transaction data for trend identification, fraud pattern detection, and spending behavior.

Core Features

  • Study transactions by category (food, travel, online, fuel, etc.)
  • Identify peak spending months and categories
  • Analyze high-value vs low-value transactions
  • Detect outlier transactions (EDA-based only)
  • Visualize monthly category-wise spending and average transaction size

Tech Stack

  • Python
  • Pandas, NumPy
  • Matplotlib, Seaborn

Learning Outcomes

  • Banking and financial data exposure
  • Budgeting pattern analysis
  • Visual fraud detection using boxplots and histograms
  • Business decision-making via user behavior segmentation
 

Project 25: Netflix Viewing & Content Popularity Analysis

Objective: To analyze Netflix viewing trends using open datasets and understand content popularity by region, genre, and release period.

Core Features

  • Import datasets including movie/TV titles, genres, duration, ratings, country, release year
  • Group content by genre and viewer region
  • Analyze trends in content length and release years
  • Identify top genres per country or period
  • Visualize trends using stacked area charts, pie charts, and histograms

Tech Stack

  • Python
  • Pandas, NumPy
  • Matplotlib, Seaborn

Learning Outcomes

  • Entertainment and streaming analytics
  • Genre-wise user preference detection
  • Region-based content performance
  • Strong visual communication of large datasets
 

Project 26: Online Grocery Purchase Behavior Analysis

Objective: To analyze purchase patterns in online grocery shopping data to understand category preferences, cart size, and purchase frequency.

Core Features

  • Import grocery order data (order ID, customer ID, product, category, quantity, price, order date)
  • Identify top-selling categories and products
  • Calculate average cart value and quantity per order
  • Group orders by day/time to find high-traffic periods
  • Visualize product trends and customer behavior patterns

Tech Stack

  • Python
  • Pandas, NumPy
  • Matplotlib, Seaborn

Learning Outcomes

  • E-commerce data analytics in the grocery sector
  • Basket size and average order value tracking
  • Product demand forecasting
  • Visualization of purchase patterns by category and time
 

Project 27: Online Learning Quiz & Assessment Performance Analysis

Objective: To analyze student quiz results and assessment data to identify question difficulty, topic-wise understanding, and learner progress patterns.

Core Features

  • Import quiz records (student ID, quiz ID, topic, score, total, time taken)
  • Identify frequently missed questions and low-scoring topics
  • Track average scores and improvements over time
  • Compare performance across batches or sections
  • Visualize topic-wise accuracy and attempt distribution

Tech Stack

  • Python
  • Pandas, NumPy
  • Matplotlib, Seaborn

Learning Outcomes

  • Assessment-based education analytics
  • Performance clustering by topic or student
  • Difficulty detection and mastery tracking
  • Visual dashboards for instructor feedback
 

Project 28: Human Resources Analytics – Employee Attrition Study

Objective: To analyze employee datasets and explore attrition (resignation), satisfaction levels, and department-wise trends.

Core Features

  • Use HR datasets with employee details (age, department, salary, years at company, attrition flag)
  • Analyze reasons for leaving – low salary, high overtime, poor satisfaction
  • Compare attrition across departments, experience levels, and genders
  • Visualize trends using bar graphs, histograms, and correlation heatmaps

Tech Stack

  • Python
  • Pandas, NumPy
  • Matplotlib, Seaborn

Learning Outcomes

  • Organizational behavior insights through data
  • Attrition-related pattern detection
  • Workforce planning and HR policy suggestions
  • Use of KPIs in employee lifecycle analysis
 

Project 29: Online Retail Coupon Usage & Impact Analysis

Objective: To analyze the effectiveness of digital coupons and discounts by studying redemption rates, profit margins, and repeat usage behavior.

Core Features

  • Import transaction data (order ID, customer ID, coupon code, discount value, order total, repeat flag)
  • Calculate coupon-wise redemption rate and average discount
  • Compare average order value with vs without coupon
  • Identify customers who use coupons repeatedly
  • Visualize coupon trends and impact using bar plots and pie charts

Tech Stack

  • Python
  • Pandas, NumPy
  • Matplotlib, Seaborn

Learning Outcomes

  • Digital coupon campaign performance analysis
  • Customer behavior and offer dependency detection
  • Revenue impact analysis with discount segmentation
  • E-commerce insights based on promotional tools
 

Project 30: Student Dropout & Performance Monitoring Dashboard

Objective: To analyze academic performance data to monitor dropouts and identify struggling students before term completion.

Core Features

  • Dataset: student ID, term marks, attendance, assignment scores, dropout flag
  • Identify common dropout indicators: low marks, attendance, activity
  • Visualize term-wise performance by class/subject
  • Compare dropouts vs active students using boxplots and bar charts
  • Suggest proactive interventions using trends

Tech Stack

  • Python
  • Pandas, NumPy
  • Matplotlib, Seaborn

Learning Outcomes

  • Education analytics based on historical performance
  • Dropout risk identification
  • Dashboards for teacher-level intervention
  • Behavioral and academic indicator correlation