Data Analytics Projects for Beginners: 10 Ideas to Build Your Portfolio

The best data analytics projects for beginners use real datasets, answer one clear business question, and cover the core tools: Excel, SQL, Power BI or Tableau, and Python. Three to five well explained projects are usually enough for a fresher portfolio, as long as each one is documented and shows a result. This page gives 10 data analyst projects for beginners with datasets, steps and what to publish for each.

If you are a graduate or fresher looking for data analytics projects for freshers, the problem is rarely a lack of ideas. It is knowing which project to start with, where to find the data, and what to show at the end. Each project below has a business question, a dataset direction, steps, a deliverable and an honest time estimate.

These data analytics projects for portfolio building are grouped by tool, so you can start with the tool you already know and move on to the next one.

Read More: Data Analyst Interview For Freshers

What Makes a Good Data Analyst Portfolio Project?

A good portfolio project is not the most advanced one. It is the one a hiring manager can understand in two minutes. Strong data analyst portfolio projects usually share four qualities:

• A real question. “Which products lose money?” is better than “Analyse this file.”
• The full workflow. Cleaning, analysis, visuals and a written conclusion, not just a chart.
• A clear output. A workbook, SQL file, dashboard link or notebook that someone else can open.
• Public proof. A GitHub repository, a published dashboard or a short case study that you can link on your resume.

How to Choose Your First Data Analytics Project

Pick by Tool

Start with the tool you are most comfortable with and move forward. A common order is Excel, then SQL, then a BI tool (Power BI or Tableau), then Python. For a detailed comparison of the BI tools, read our guide on Excel vs Power BI vs Tableau.

Pick by Time

The table below compares all 10 projects. Time estimates are approximate and depend on your experience.

#	Project	Main tool	Level	Estimated time	What you publish
1	Personal expense tracker	Excel	Easiest	3 to 5 hours	Workbook
2	Superstore sales dashboard	Excel	Easy	5 to 8 hours	Dashboard sheet
3	Food delivery orders analysis	SQL	Easy	5 to 8 hours	SQL file
4	E-commerce sales and customer segmentation	SQL	Medium	6 to 10 hours	SQL file and findings note
5	HR employee attrition dashboard	Power BI	Medium	6 to 10 hours	Dashboard screenshots
6	State wise Indian public dataset analysis	Power BI	Medium	6 to 10 hours	Dashboard with state comparison
7	IPL cricket statistics dashboard	Tableau	Medium	5 to 8 hours	Public dashboard link
8	Flight delay or travel dashboard	Tableau	Medium	5 to 8 hours	Public dashboard link
9	Movie catalog exploratory analysis	Python	Medium	6 to 10 hours	Jupyter notebook
10	“Why did sales drop?” capstone	SQL, Python, Power BI	Hardest	10 to 15 hours	Case study

Which Project for Each Tool?

If you want to practise	Start with
Excel	Projects 1 and 2
SQL	Projects 3 and 4
Power BI	Projects 5 and 6
Tableau	Projects 7 and 8
Python	Project 9
Everything together	Project 10

If you are completely new, begin with Project 1. It needs no download and uses your own data.

10 Data Analytics Projects for Beginners

Each project follows the same format: the business question, dataset, steps, deliverable, skills shown, time and a sample resume line. The resume lines are templates with blanks, so fill them with your own results.

Excel Projects for Data Analysis

These Excel projects for data analysis need no coding. Excel is also the tool most Indian employers still use for reporting, so these two are worth doing first.

Project 1: Personal Expense Tracker (Basic Excel)

• Business question: Where does my money go each month, and which category is growing fastest?
• Dataset: Your own bank or UPI statement for 3 to 6 months. Remove names, account numbers and anything personal before sharing the file publicly, or use made up sample data instead.
• What you will do:
  1. Copy the statement into a clean table with date, description, category and amount columns.
  2. Add categories such as food, travel, rent and bills, using a simple lookup or manual tagging.
  3. Use SUMIFS to total each category by month.
  4. Build a pivot table and a chart for monthly spending by category.
  5. Write three observations next to the chart.
• Deliverable: One workbook with a data sheet, a summary sheet and a chart.
• Skills shown: Data cleaning, formulas, SUMIFS, pivot tables, charts.
• Time: 3 to 5 hours (estimate).
• Resume line template: “Built an Excel expense tracker using pivot tables and SUMIFS to analyse [number] months of spending and identified [finding].”

Project 2: Superstore Sales Dashboard With Pivot Tables and Slicers (Advanced Excel)

• Business question: Which regions, categories and products drive sales and profit, and which lose money?
• Dataset: The Sample Superstore dataset, a retail sales sample originally provided with Tableau and also available on Kaggle. Check the licence on the page you download it from.
• What you will do:

1. Clean the data: check duplicates, blanks, date formats and data types.
2. Create pivot tables for sales and profit by region, category and month.
3. Add slicers for region and year.
4. Use a lookup (XLOOKUP, or INDEX and MATCH if your Excel version does not support XLOOKUP) to bring in extra fields.
5. Place KPI cards and 3 or 4 charts on one dashboard sheet.

• Deliverable: A single dashboard sheet with slicers, plus a short written summary.
• Skills shown: Pivot tables, slicers, lookups, KPIs, dashboard layout.
• Time: 5 to 8 hours (estimate).
• Resume line template: “Created an interactive Excel sales dashboard with pivot tables and slicers, finding that [category] had the lowest profit margin at [value].”

SQL Projects for Data Analyst Beginners

These SQL projects for data analyst beginners show that you can pull and summarise data yourself. You can practise on a free database such as MySQL, PostgreSQL or SQLite by importing a CSV file.

Project 3: Food Delivery Orders Analysis

• Business question: Which cities, restaurants and time slots produce the most orders and the longest delays?
• Dataset: A food delivery orders dataset from Kaggle. Search for one with order date, city, restaurant, amount and delivery time. Check that the columns exist before you commit to it.
• What you will do:

1. Import the CSV into a database table.
2. Write queries to count orders by city and by hour of the day.
3. Use GROUP BY with SUM and AVG to find revenue and average delivery time by restaurant.
4. Use HAVING to keep only restaurants with a minimum number of orders.
5. Write down what each query tells the business.

• Deliverable: A SQL file with about 10 documented queries and a short findings note.
• Skills shown: SELECT, WHERE, GROUP BY, HAVING, ORDER BY, aggregate functions.
• Time: 5 to 8 hours (estimate).
• Resume line template: “Analysed [number] food delivery orders using SQL (GROUP BY, HAVING) to find that [city or time slot] had the highest [metric].”

Project 4: E-commerce Sales and Customer Segmentation (RFM)

• Business question: Who are our best customers, and who is about to stop buying?
• Dataset: A public e-commerce orders dataset from Kaggle with customer ID, order date and order value. Pick one with several tables (customers, orders, products) if you want JOIN practice.
• What you will do:

1. Join the customers and orders tables.
2. For each customer calculate Recency (days since last order), Frequency (number of orders) and Monetary value (total spend). This is called RFM analysis.
3. Use a window function such as NTILE to give each customer a score from 1 to 4 on each measure. The exact syntax depends on your database.
4. Combine the scores into groups such as “best customers” and “at risk”.
5. Summarise how many customers sit in each group.

• Deliverable: A SQL file and a one page findings note with a suggested action for each group.
• Skills shown: JOIN, GROUP BY, window functions, subqueries or CTEs.
• Time: 6 to 10 hours (estimate).
• Resume line template: “Segmented [number] customers using RFM analysis in SQL (JOIN, window functions) and identified [number] at risk customers.”

Power BI Projects for Beginners

These Power BI projects for beginners show that you can turn raw data into a dashboard a manager can use. Power BI Desktop is free to download for Windows. For a deeper look at the tool, see our [Excel vs Power BI vs Tableau](INTERNAL LINK: Excel vs Power BI vs Tableau page) guide.

Project 5: HR Employee Attrition Dashboard

• Business question: Which departments, roles and employee groups have the highest attrition?
• Dataset: The IBM HR Analytics Employee Attrition and Performance dataset on Kaggle. Check the licence and description on the dataset page before publishing your work.
• What you will do:

1. Load the data and clean it in Power Query (check data types, remove unused columns).
2. Create a measure for attrition rate using DAX, for example Attrition Rate = DIVIDE(COUNTROWS(FILTER(HR, HR[Attrition] = “Yes”)), COUNTROWS(HR)).
3. Build visuals: attrition by department, by age group and by years at company.
4. Add slicers for department and job role.
5. Add a short text box with the three main insights.

• Deliverable: A Power BI report with screenshots, or a published link if you have access to publish.
• Skills shown: Power Query, DAX measures, KPIs, slicers, dashboard design.
• Time: 6 to 10 hours (estimate).
• Resume line template: “Built a Power BI HR dashboard with DAX measures and found that attrition was highest in [group] at [value].”

Project 6: State Wise Analysis of an Indian Public Dataset

• Business question: How do states compare on one public indicator, and how has it changed over time?
• Dataset: Choose one dataset from data.gov.in, the Open Government Data Platform India, such as rainfall, education or employment data. Choose one with a state column and a year column, and read its licence and description before use. This project works with any dataset that meets those two conditions.
• What you will do:

1. Download the file and clean it in Power Query (standardise state names, fix blanks, set data types).
2. Create a measure for the main indicator and for year on year change.
3. Build a map or bar chart comparing states.
4. Add a line chart for the trend over time and a year slicer.
5. Write three findings and one limitation of the data.

• Deliverable: A dashboard with a state comparison and a short written note on what the data does and does not show.
• Skills shown: Power Query cleaning, DAX, maps, trend analysis, data storytelling.
• Time: 6 to 10 hours (estimate).
• Resume line template: “Analysed [indicator] across Indian states using public data and Power BI, finding that [state or group] [finding].”

Tableau Project Ideas for Beginners

These Tableau project ideas for beginners are best published on Tableau Public, which lets you share a dashboard through a link. Check Tableau’s current terms before you publish, because public dashboards can be seen by anyone.

Project 7: IPL Cricket Statistics Dashboard

• Business question: Which teams and players perform best, and does the toss decision matter?
• Dataset: IPL matches and deliveries datasets are available on Kaggle. Different versions cover different seasons, so check the date range of the one you use and say so in your write up.
• What you will do:

1. Connect the matches file (and the deliveries file if you want player statistics).
2. Build charts for wins by team, runs by season and top run scorers.
3. Create a calculated field to compare toss winners with match winners.
4. Combine the charts into one dashboard with a filter for season.
5. Publish to Tableau Public and add a short description.

• Deliverable: A public dashboard link.
• Skills shown: Data connections, calculated fields, filters, dashboard actions, visual storytelling.
• Time: 5 to 8 hours (estimate).
• Resume line template: “Created a Tableau Public dashboard analysing [number] IPL matches, showing that [finding].”

Project 8: Flight Delay or Travel Data Dashboard

• Business question: Which airlines, routes and months have the most delays?
• Dataset: A flight delay or travel dataset from Kaggle or from data.gov.in. Choose one that has airline, route or airport, date and delay columns.
• What you will do:

1. Connect the data and check data types, especially dates.
2. Create a calculated field for “delayed or not” using a delay threshold you define and state.
3. Build charts for delay rate by airline, by month and by route.
4. Add a map if the data has airport locations.
5. Combine everything in a dashboard with filters.

• Deliverable: A public Tableau dashboard link and a short note on how you defined “delayed”.
• Skills shown: Calculated fields, maps, filters, defining a metric clearly.
• Time: 5 to 8 hours (estimate).
• Resume line template: “Built a Tableau dashboard of flight delays by airline and month, identifying [finding].”

Python Data Analysis Projects for Beginners

These Python data analysis projects for beginners start with exploration, not machine learning. You do not need to build a model to show good analytical skills.

Project 9: Movie Catalog Exploratory Analysis (Pandas)

• Business question: Which movies and genres are rated highest, and how have ratings changed over time?
• Dataset: MovieLens, a ratings dataset collected by the GroupLens research group. It comes in several sizes, such as 100k and 1M ratings. Read the README that comes with the download for the usage terms. A Netflix titles dataset from Kaggle is an alternative.
• What you will do:

1. Load the movies and ratings files into Pandas DataFrames and check shapes and missing values.
2. Merge the two tables on the movie ID.
3. Find the top rated movies, but only among movies with a minimum number of ratings, and explain why.
4. Split the genres column and compare average rating by genre.
5. Plot rating trends with Matplotlib and write your conclusions in the notebook.

• Deliverable: A Jupyter notebook on GitHub with a README.
• Skills shown: Pandas, merging, groupby, data cleaning, Matplotlib, written explanation.
• Time: 6 to 10 hours (estimate).
• Resume line template: “Performed exploratory analysis on [number] movie ratings using Python (Pandas, Matplotlib) and found that [finding].”

Capstone Project

Project 10: End to End “Why Did Sales Drop?” Analysis (SQL, Python, Power BI)

• Business question: Sales fell in one month. What caused it, and what should the business do?
• Dataset: Reuse a sales dataset from Projects 2, 3 or 4, or a retail sales dataset that covers at least a year of orders.
• What you will do: Use this simple framework:

1. Clarify how sales are measured and compare with the same month last year to rule out seasonality.
2. Check the data with SQL for missing or duplicate records.
3. Segment sales by region, product and customer type using SQL, and cross check in Python.
4. Find the likely cause from where the drop is concentrated.
5. Present the findings in a Power BI dashboard with a recommendation.

• Deliverable: A short case study with four parts: problem, data, method and result, plus the dashboard.
• Skills shown: Structured thinking, SQL, Python, Power BI, communication. This is also the kind of scenario asked in interviews, which you can practise with our [data analyst interview questions for freshers](INTERNAL LINK: Data Analyst Interview Questions for Freshers page).
• Time: 10 to 15 hours (estimate).
• Resume line template: “Investigated a [value] sales decline using SQL, Python and Power BI and recommended [action] based on [finding].”

10 More Data Analytics Project Ideas to Try Next

Once you finish a few projects above, these data analysis project ideas keep your portfolio growing. They are listed briefly, so choose a dataset, define the question and follow the same pattern.

Idea	Tool	Question to answer
Customer churn analysis (no modelling)	Python or Excel	Which customer groups leave most often?
Bank loan approval analysis	Python or Excel	Which applicant features are linked to approval?
Marketing campaign performance	Excel or Power BI	Which channel gives the best return?
Retail inventory and supply analysis	SQL or Excel	Which products are overstocked or run out?
Student performance analysis	Excel or Python	What affects exam scores?
Website traffic dashboard	Power BI or Looker Studio	Which pages and sources bring visitors?
A/B test analysis	Excel or Python	Did the new version perform better?
Weather pattern analysis for your city	Python or Excel	How have temperature and rainfall changed?
Stock price trend and volatility	Python	Which stock was more volatile?
Public health trend analysis	Excel or Python	How has a health indicator changed by state?

Projects such as churn prediction models, fraud detection and recommendation systems need machine learning. They are not beginner projects, so leave them until you are comfortable with the basics above.

Free Datasets for Data Analytics Projects

Here are reliable places to find data analytics projects with datasets. Always read the licence and description before you use or publish a dataset.

Source	What it offers	Good for projects
Kaggle	Thousands of user shared datasets across topics	2, 3, 4, 5, 7, 8
data.gov.in (Open Government Data Platform India)	Indian government datasets on topics such as education, health and agriculture	6, 8 and data analysis project ideas using Indian datasets
GroupLens (MovieLens)	Movie ratings datasets in several sizes	9
Your own data	Expense, study or fitness records, anonymised	1

Using Indian public data makes your work more relevant to Indian employers. If you want data analysis project ideas using Indian datasets, start with Project 6 and repeat it for a different indicator.

How to Present Your Projects in a Portfolio

Doing the project is half the work. How you present it decides whether a recruiter understands it.

GitHub and README Checklist

Create one repository per project, with a README that covers:

• The business question in one sentence
• The data source and its link
• The tools you used
• The steps you followed
• Your three main findings
• Screenshots of the dashboard or key charts
• Limitations of the data

How to Write the Project on Your Resume

Use one line per project: what you did, which tool you used and what you found. Keep the numbers real. Never add results you did not produce. Use the templates above and replace the blanks with your own findings.

How to Explain a Project in an Interview

Describe each project in four sentences: the problem, the data, the method and the result. This is also how interviewers expect fresher answers to sound.

Common Mistakes Beginners Make in Portfolio Projects

• Copying a tutorial exactly. Fix: change the question, the filters or the dataset so the work is yours.
• No business question. Fix: write the question at the top of every project.
• Only overused datasets. Fix: mix well known data with at least one Indian or personal dataset.
• No README or explanation. Fix: add a short write up to every project.
• Too many half finished projects. Fix: finish three to five properly.
• Jumping to machine learning too early. Fix: build strong analysis and dashboard skills first.
• Dashboards with no insight. Fix: add a written takeaway beside every chart.
• Overstating results. Fix: report only what your data shows and mention its limits.