9 Project Ideas For Your Data Analytics Portfolio [2022] (2024)

Finding projects for your data analytics portfolio can be tricky, especially when you’re new to the field. You might also think that your data projects need to be especially complex or showy, but that’s not the case. The most important thing is to demonstrate your skills, ideally using a dataset that interests you. And the good news? Data is everywhere—you just need to know where to find it and what to do with it.

In this post, we’ll highlight the key elements that your data analytics portfolio should demonstrate. We’ll then share nine project ideas that will help you build your portfolio from scratch, focusing on three key areas: Data scraping, exploratory analysis, and data visualization.

We’ll cover:

  1. What should you include in your data analytics portfolio?
  2. Data scraping project ideas
  3. Exploratory data analysis project ideas
  4. Data visualization project ideas
  5. What’s next?

Ready to get inspired? Let’s go!

1. What should you include in your data analytics portfolio?

Data analytics is all about finding insights that inform decision-making. But that’s just the end goal. As any experienced data analyst will tell you, the insights we see as consumers are the result of a great deal of work. In fact, about 80% of all data analytics tasks involve preparing data for analysis. This makes sense when you think about it—after all, our insights are only as good as the quality of our data.

Yes, your portfolio needs to show that you can carry out different types of data analysis. But it also needs to show that you can collect data, clean it, and report your findings in a clear, visual manner. As your skills improve, your portfolio will grow in complexity. As a beginner though, you’ll need to show that you can:

  • Scrape the web for data
  • Carry out exploratory analyses
  • Clean untidy datasets
  • Communicate your results using visualizations

If you’re inexperienced, it can help to present each item as a mini-project of its own. This makes life easier since you can learn the individual skills in a controlled way. With that in mind, we’ll keep it nice and simple with some basic ideas, and a few tools you might want to explore to help you along the way.

2. Data scraping project ideas for your portfolio

What is data scraping?

Data scraping is the first step in any data analytics project. It involves pulling data (usually from the web) and compiling it into a usable format. While there’s no shortage of great data repositories available online, scraping and cleaning data yourself is a great way to show off your skills.

The process of web scraping can be automated using tools like Parsehub, ScraperAPI, or Octoparse (for non-coders) or by using libraries like Beautiful Soup or Scrapy (for developers). Whichever tool you use, the important thing is to show that you understand how it works and can apply it effectively.

Before scraping a website, be sure that you have permission to do so. If you’re not certain, you can always search for a dataset on a repository site likeKaggle. If it exists there, it’s a good bet you can go straight to the source and scrape it yourself. Bear in mind though—data scraping can be challenging if you’re mining complex, dynamic websites. We recommend starting with something easy—a mostly-static site. Here are some ideas to get you started.

Data scraping project ideas

The Internet Movie Database

A good beginner’s project is to extract data from IMDb. You can collect details about popular TV shows, movie reviews and trivia, the heights and weights of various actors, and so on. Data on IMDb is stored in a consistent format across all its pages, making the task a lot easier. There’s also a lot of potential here for further analysis.

Job portals

Many beginners like scraping data from job portals since they often contain standard data types. You can also find lots of online tutorials explaining how to proceed. To keep it interesting, why not focus on your local area? Collect job titles, companies, salaries, locations, required skills, and so on. This offers great potential for later visualization, such as graphing skillsets against salaries.

E-commerce sites

Another popular one is to scrape product and pricing data from e-commerce sites. For instance, extract product information about Bluetooth speakers on Amazon, or collect reviews and prices on various tablets and laptops. Once again, this is relatively straightforward to do, and it is scalable. This means you can start with a product that has a small number of reviews, and then upscale once you’re comfortable using the algorithms.


For something a bit less conventional, another option is to scrape a site like Reddit. You could search for particular keywords, upvotes, user data, and more. Reddit is a very static website, making the task nice and straightforward. Later, you can carry out interesting exploratory analyses, for instance, to see if there are any correlations between popular posts and particular keywords. Which brings us to our next section.

3. Exploratory data analysis project ideas

What is exploratory data analysis?

The next step in any data analyst’s skillset is the ability to carry out an exploratory data analysis (EDA). An EDA looks at the structure of data, allowing you to determine their patterns and characteristics. They also help you to clean your data. You can extract important variables, detect outliers and anomalies, and generally test your underlying assumptions.

While this process is one of the most time-consuming tasks for a data analyst, it can also be one of the most rewarding. Later modeling focuses on generating answers to specific questions. An EDA, meanwhile, helps you do one of the most exciting bits—generating those questions in the first place.

Languages like R and Python are often used to carry out these tasks. They have many pre-existing algorithms that you can use to carry out the work for you. The real skill lies in presenting your project and its results. How you decide to do this is up to you, but one popular method is to use an interactive documentation tool like Jupyter Notebook. This lets you capture elements of code, along with explanatory text and visualizations, all in one place. Here are some ideas for your portfolio.

Exploratory data analysis project ideas

Global suicide rates

This global suicide rates dataset covers suicide rates in various countries, with additional data including year, gender, age, population, GDP, and more. When carrying out your EDA, ask yourself: What patterns can you see? Are suicides rates climbing or falling in various countries? What variables (such as gender or age) can you find that might correlate to suicide rates?

World Happiness Report

On the other end of the scale, the World Happiness Report tracks six factors to measure happiness across the world’s citizens: life expectancy, economics, social support, absence of corruption, freedom, and generosity. So, which country is the happiest? Which continent? Which factor appears to have the greatest (or smallest) impact on a nation’s happiness? Overall, is happiness increasing or decreasing?

Aside from the two ideas above, you could also use your own datasets. After all, if you’ve already scraped your own data, why not use them? For instance, if you scraped a job portal, which locations or regions offer the best-paid jobs? Which offer the least well-paid ones? Why might that be? Equally, with e-commerce data, you could look at which prices and products offer the best value for money.

Ultimately, whichever dataset you’re using, it should grab your attention. If the data are too complex or don’t interest you, you’re likely to run out of steam before you get very far. Keep in mind what further probing you can do to spot interesting trends or patterns, and to extract the insights you need.

We’ve compiled a list of ten great places to find free datasets for your next project here.

4. Data visualization project ideas

What is data visualization?

Scraping, tidying, and analyzing data is one thing. Communicating your findings is another. Our brains don’t like looking at numbers and figures, but they love visuals. This is where the ability to create effective data visualizations comes in. Good visualizations—whether static or interactive—make a great addition to any data analytics portfolio. Showing that you can create visualizations that are both effective and visually appealing will go a long way towards impressing a potential employer.

Some free visualization tools include Google Charts, Canva Graph Maker (free), and Tableau Public. Meanwhile, if you want to show off your coding abilities, use a Python library such as Seaborn, or flex your R skills with Shiny. Needless to say, there are many tools available to help you. The one you choose depends on what you’re looking to achieve. Here’s a bit of inspiration…

Data visualization project ideas


Topical subject matter looks great on any portfolio, and the pandemic is nothing if not topical! What’s more, sites like Kaggle already have thousands of Covid-19 data sets available. How can you represent the data? Could you use a global heatmap to show where cases have spiked, versus where there are very few? Perhaps you could create two overlapping bar charts to show known infections versus predicted infections. Here’s a handy tutorial to help you visualize Covid-19 data using R, Shiny, and Plotly.

Most followed on Instagram

Whether you’re interested in social media, or celebrity and brand culture, this dataset of the most-followed people on Instagram has great potential for visualization. You could create an interactive bar chart that tracks changes in the most followed accounts over time. Or you could explore whether brand or celebrity accounts are more effective at influencer marketing. Otherwise, why not find another social media dataset to create a visualization? For instance, this map of the USA by data scientist Greg Rafferty nicely highlights the geographical source of trending topics on Instagram.

Travel data

Another topic that lends itself well to visualization is transport data. Here’s a great project by Chen Chen on github, using Python to visualize the top tourist destinations worldwide, and the correlation between inbound/outbound tourists with gross domestic product (GDP).

5. What’s next?

In this post, we’ve explored which skills every beginner needs to demonstrate in their data analytics portfolio. Regardless of the dataset you’re using, you should be able to demonstrate the following abilities:

  • Web scraping—using tools like Parsehub, Beautiful Soup, or Scrapy to extract data from websites (remember: static ones are easier!)
  • Exploratory data analysis and data cleaning—manipulating data with tools like R and Python, before drawing some initial insights.
  • Data visualization—utilizing tools like Tableau, Shiny, or Plotly to create crisp, compelling dashboards, and visualizations.

Once you’ve mastered the basics, you can start getting more ambitious with your data analytics projects. For example, why not introduce some machine learning projects, like sentiment analysis or predictive analysis? The key thing is to start simple and to remember that a good data analytics portfolio needn’t be flashy, just competent.

To further develop your skills, there are loads of online courses designed to set you on the right track. To start with, why not try our free, five-day data analytics short course?

And, if you’d like to learn more about becoming a data analyst and building your portfolio, check out the following:

  • How to build a data analytics portfolio
  • The best data analytics certification programs on the market right now
  • These are the most common data analytics interview questions
9 Project Ideas For Your Data Analytics Portfolio [2022] (2024)


How do I create a project portfolio for data analyst? ›

How to Build a Data Analyst Portfolio: Tips for Success
  1. Portfolio Platforms. ...
  2. About me. ...
  3. Projects. ...
  4. Other items to include. ...
  5. Use your portfolio to demonstrate your passions. ...
  6. Take advantage of tools like Jupyter Notebook and R Notebook. ...
  7. Only include your best work. ...
  8. Build your portfolio as you learn.
13 Jul 2022

What are the 7 data analysis process? ›

7 Steps of Data Analysis

Define the business objective. Source and collect data. Process and clean the data. Perform exploratory data analysis (EDA).

What are the 5 types of data analytics? ›

5 Types of analytics: Prescriptive, Predictive, Diagnostic, Descriptive and Cognitive Analytics - WeirdGeek | Data analytics, Data analysis tools, Data science.

How do I write a project portfolio? ›

How to write project case studies for your portfolio
  1. Write down your case studies before you do almost anything else. ...
  2. Keep it brief & caption everything. ...
  3. Include the right details. ...
  4. Give credit & explain your role. ...
  5. Write in your voice. ...
  6. Don't image dump. ...
  7. Think of each case study like a magazine feature.
26 Feb 2018

What is Project Portfolio and example? ›

A portfolio in project management refers to a grouping of projects, and programs. It can also include other project-related activities and responsibilities. The purpose of a portfolio is to establish centralized management and oversight for many projects and programs.

What are 7 good things to put in a portfolio? ›

As you begin to create your portfolio, there are several different categories that you should consider: Personal Information, Values, Personal Goals and History, Accomplishments and Job History, Skills and Attributes, Education and Training as well as Testimonials and Recommendations.

What are 5 things a great portfolio includes? ›

What Should My Portfolio Contain?
  • Table of Contents.
  • Career and professional development goals, tailored for each interviewer.
  • Work philosophy statement; personal mission statement.
  • List of areas of expertise.
  • Works in progress (activities and projects)

What are the 4 main types of data analytics? ›

Modern analytics tend to fall in four distinct categories: descriptive, diagnostic, predictive, and prescriptive.

What are the 8 stages of data analysis? ›

data analysis process follows certain phases such as business problem statement, understanding and acquiring the data, extract data from various sources, applying data quality for data cleaning, feature selection by doing exploratory data analysis, outliers identification and removal, transforming the data, creating ...

How do I start my first data analysis project? ›

  1. Step 1: Start small, with the basics. ...
  2. Step 2: Take an online certification for a defined approach. ...
  3. Step 3: Work through the Data Science lifecycle. ...
  4. Step 4: Create a diverse portfolio of projects. ...
  5. Step 5: Create visualizations & work on storytelling.
6 Jun 2022

What are the 9 stages of data processing? ›

What are the Stages of Data Processing?
  • Acquisition. First, relevant data sources are identified. ...
  • Preparation. Data preparation is itself a sequence of smaller processes. ...
  • Integration. ...
  • Organization. ...
  • Processing. ...
  • Visualization. ...
  • Storage. ...
  • Acquisition and Preparation.
30 Jun 2021

What are the 10 steps in analyzing data? ›

A Step-by-Step Guide to the Data Analysis Process
  1. Defining the question.
  2. Collecting the data.
  3. Cleaning the data.
  4. Analyzing the data.
  5. Sharing your results.
  6. Embracing failure.
  7. Summary.
5 days ago

What are the 5 steps of analysis? ›

In this post we'll explain five steps to get you started with data analysis.

What are the 10 data types? ›

10 data types
  • Integer. Integer data types often represent whole numbers in programming. ...
  • Character. In coding, alphabet letters denote characters. ...
  • Date. This data type stores a calendar date with other programming information. ...
  • Floating point (real) ...
  • Long. ...
  • Short. ...
  • String. ...
  • Boolean.
21 Jul 2021

What are the 8 types of data? ›

These include: int, byte, short, long, float, double, boolean, and char.

What are the 5 types of portfolio? ›

Types of Portfolio Investment
  • The Aggressive Portfolio.
  • The Defensive Portfolio.
  • The Income Portfolio.
  • The Speculative Portfolio.
  • The Hybrid Portfolio.

What are the 6 portfolio phases? ›

The multimedia development process usually covers the following stages: Assess/Decide, Plan/Design, Develop, Implement, Evaluate.

What are the 7 steps of portfolio process? ›

Processes of Portfolio Management
  1. Step 1 – Identification of objectives. ...
  2. Step 2 – Estimating the capital market. ...
  3. Step 3 – Decisions about asset allocation. ...
  4. Step 4 – Formulating suitable portfolio strategies. ...
  5. Step 5 – Selecting of profitable investment and securities. ...
  6. Step 6 – Implementing portfolio. ...
  7. Step 7 – ...
  8. Step 8 –

What are the 4 types of portfolio? ›

4 Common Types of Portfolio
  • Conservative portfolio. This type is also called a defensive portfolio or a capital preservation portfolio. ...
  • Aggressive portfolio. Also known as a capital appreciation portfolio. ...
  • Income portfolio. ...
  • Socially responsible portfolio.
28 Nov 2022

What projects should I do for portfolio? ›

Top 7 Front-End Projects to Build Your Portfolio
  • A Personal Website or Portfolio Homepage.
  • Create a Simple Quiz Game with Javascript.
  • A Responsive Virtual Keyboard Design.
  • Build a weather app with dark mode.
  • A Dynamic Landing Page using Boostrap.
  • Facebook Timeline clone.
17 Feb 2020

What is a portfolio sample? ›

A portfolio is a collection of work samples that you can bring to an interview, send to a prospective employer, or even post online. Portfolios can: Provide evidence of work that you've done.

What is a portfolio Class 9? ›

A portfolio is a useful collection of purposely chosen student's work depicting a selection of performances that are collected over time and represents the learner's efforts, progress, growth and accomplishment in key areas learning outcomes.

What are 3 things you should put in a professional portfolio? ›

What should be included in a career portfolio?
  • Your personal information. ...
  • A career summary and list of goals. ...
  • Your resume. ...
  • A list of skills and accomplishments. ...
  • Work samples. ...
  • A list of continued education qualifications or professional development activities. ...
  • A reference list — including testimonials, if applicable.
12 Aug 2022

What are the 4 qualities effective of portfolio? ›

When we use the term “well-constructed portfolio,” we mean a portfolio that contains the following four key traits.
  • Effective Diversification. What do you think of when you think of a diversified portfolio? ...
  • Active Management. ...
  • Cost Efficiency. ...
  • Tax Efficiency.
12 Oct 2016

What a good portfolio looks like? ›

A diversified portfolio should have a broad mix of investments. For years, many financial advisors recommended building a 60/40 portfolio, allocating 60% of capital to stocks and 40% to fixed-income investments such as bonds. Meanwhile, others have argued for more stock exposure, especially for younger investors.

What can I put in my portfolio? ›

Your portfolio should contain written and visual overviews of projects and significant pieces of work that you've managed or been involved with. It should also include an insight into skills you have, methods you've used, the impact of your work, along with any relevant outcomes and / or lessons you've learned.

How do I create my own portfolio? ›

How to create an online portfolio
  1. Gather inspiration.
  2. Choose a template.
  3. Showcase your best projects.
  4. Use high quality images.
  5. Include the right content and features.
  6. Improve your portfolio's UX.
  7. Work on your site's SEO.
  8. Make it mobile friendly.
21 Jan 2021

What is the 2 main type of portfolios? ›

There are two main types of portfolio assessments: “instructional” or “working” portfolios, and “showcase” portfolios. Instructional or working portfolios are formative in nature. They allow a student to demonstrate his or her ability to perform a particular skill. Showcase portfolios are summative in nature.

What are the 6 steps of data analytics? ›

According to Google, there are six data analysis phases or steps: ask, prepare, process, analyze, share, and act. Following them should result in a frame that makes decision-making and problem solving a little easier.

What are the 2 main methods for data analysis? ›

The two primary methods for data analysis are qualitative data analysis techniques and quantitative data analysis techniques.

Does data analytics require coding? ›

Some Data Analysts do have to code as part of their day-to-day work, but coding skills are not typically required for jobs in data analysis.

What are the 7 types of data? ›

And there you have the 7 Data Types.
  • Useless.
  • Nominal.
  • Binary.
  • Ordinal.
  • Count.
  • Time.
  • Interval.
29 Aug 2018

What are the 2 main types of data? ›

There are two general types of data – quantitative and qualitative and both are equally important. You use both types to demonstrate effectiveness, importance or value.

What are the 4 E's of big data analytics? ›

There are generally four characteristics that must be part of a dataset to qualify it as big data—volume, velocity, variety and veracity. Value is a fifth characteristic that is also important for big data to be useful to an organization. Our world has become datafied.

What are the 3 common categories of data analytics? ›

There are three types of analytics that businesses use to drive their decision making; descriptive analytics, which tell us what has already happened; predictive analytics, which show us what could happen, and finally, prescriptive analytics, which inform us what should happen in the future.

What are the three pillars of data analytics? ›

To relieve frustration and deliver a better analytics solution and experience for the organization, data and business analysts must focus on strengthening the three pillars of data analytics: agility, performance, and speed.

How do I get data analytics with no experience? ›

How to Become a Data Analyst in 2022 With No Experience
  1. Understand where you want to go as a data analyst.
  2. Receive foundational training and understand which skills you need to acquire.
  3. Obtain the skills through a degree, bootcamp, or self-direct learning.
  4. Break into the chosen industry.

Is data analytics hard for beginners? ›

Data analysis is neither a “hard” nor “soft” skill but is instead a process that involves a combination of both. Some of the technical skills that a data analyst must know include programming languages like Python, database tools like Excel, and data visualization tools like Tableau.

Can I teach myself data analytics? ›

Yes, you can learn the fundamentals of data analysis on your own.

What are data analysis activities? ›

Data analysis is the method of assessing, cleaning, and transforming data by use of analytics and logics with the aim of finding useful information that will support decision making. It involves gathering information from different sources, reviewing it, and then making a conclusion.

What is a popular example of data analytics? ›

Data analysts use a variety of tools and technologies to gather all sorts of data, like statistics about how much time users spend on a website, demographic information about customers or traffic patterns in a city.

What is data analysis class 9? ›

Data analysis is the process of filtering, transforming, and modeling data to discover useful information. For example: before planning a business plan, an analysis of the budget, resources and target sets the perfect example for data analysis.

What 3 skills are involved in data analyst? ›

Part 1: Technical Skills Required for Data Analysts
  • Data Visualization. As the term suggests, data visualization is a person's ability to present data findings via graphics or other illustrations. ...
  • Data Cleaning. ...
  • MATLAB. ...
  • R. ...
  • Python. ...
  • SQL and NoSQL. ...
  • Machine Learning. ...
  • Linear Algebra and Calculus.

What type of data analytics is most difficult? ›

Prescriptive analytics is comparatively complex in nature and many companies are not yet using them in day-to-day business activities, as it becomes difficult to manage. If applied effectively, predictive analytics can have a significant impact on business growth.

What are the 10 examples of data? ›

10 data types
  • Integer. Integer data types often represent whole numbers in programming. ...
  • Character. In coding, alphabet letters denote characters. ...
  • Date. This data type stores a calendar date with other programming information. ...
  • Floating point (real) ...
  • Long. ...
  • Short. ...
  • String. ...
  • Boolean.
21 Jul 2021

What are 5 data examples? ›

In our day to day life, we can collect the following data.
  • Number of females per 1000 males in various states of our country.
  • Production of wheat in the last 10 years in our country.
  • Number of plants in our locality.
  • Rainfall in our city in the last 10 years.
  • Marks obtained by students.

What are the top trends of data analytics in 2022? ›

One of the biggest data trends for 2022 is the increase in the use of hybrid cloud services and cloud computation. Public clouds are cost-effective but do not provide high security whereas a private cloud is secure but more expensive.

Top Articles
Latest Posts
Article information

Author: Edwin Metz

Last Updated:

Views: 5601

Rating: 4.8 / 5 (58 voted)

Reviews: 89% of readers found this page helpful

Author information

Name: Edwin Metz

Birthday: 1997-04-16

Address: 51593 Leanne Light, Kuphalmouth, DE 50012-5183

Phone: +639107620957

Job: Corporate Banking Technician

Hobby: Reading, scrapbook, role-playing games, Fishing, Fishing, Scuba diving, Beekeeping

Introduction: My name is Edwin Metz, I am a fair, energetic, helpful, brave, outstanding, nice, helpful person who loves writing and wants to share my knowledge and understanding with you.