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I got my first data analytics internship back in 2020.
Ever since then, I’ve transitioned into a senior-level full-time role, landed multiple freelance data analytics gigs, and consulted for companies in different parts of the world.
During this time, I have reviewed resumes for data analyst positions and even shortlisted candidates for jobs.
And I noticed one thing that separated the most prominent applicants from everyone else.
Projects.
Even if you have zero experience in the data industry and no technical background, you can stand out from everyone else and get hired solely based on the projects you display on your resume.
In this article, I’m going to show you how to create projects that help you stand out from the competition and land your first data analyst job.
If you’re reading this article, you probably already know that it is important to display projects on your resume.
You might even have built a few projects of your own after taking an online course or boot camp.
However, many data analytics projects do more harm to your portfolio than good. These projects can actually lower your chances of getting a job and must be avoided at all costs.
For example, if you’ve taken the popular Google Data Analytics Certificate on Coursera, you’ve probably done the capstone project that comes with this certification.
Image from Coursera
However, over 2 million other people have enrolled in the same course, and have potentially completed the same capstone project.
Chances are, recruiters have seen these projects on the resume of hundreds of applicants, and will not be impressed by it.
A similar logic applies to any other project that has been created many times.
Creating a project using the Titanic, Iris, or Boston Housing dataset on Kaggle can be a valuable learning experience, but should not be displayed on your portfolio.
If you want a competitive edge over other people, you need to stand out.
Here’s how.
A project that stands out must be unique.
Pick a project that:
- Solves a real-world problem.
- Cannot be easily replicated by other people.
- Is interesting and tells a story.
Much of the advice on data analytics projects on the Internet is inaccurate and unhelpful.
You will be told to create generic projects like an analysis of the Titanic dataset—projects that add no real value to your resume.
Unfortunately, the people telling you to do these things aren’t even working in the data industry, so you must be discerning when taking this advice.
In this article, I will be showing you examples of real people who have landed jobs in data analytics because of their portfolio projects.
You will learn about the types of projects that actually get people hired in this field so that you can potentially build something similar.
1. Job Trends Monitoring Dashboard
The first project is a dashboard displaying job trends in the data industry.
I found this project in a video created by Luke Barousse, a former lead data analyst who also specializes in content creation.
Here is a screenshot of this dashboard:
Image from SkillQuery
The above dashboard is called SkillQuery, and it displays the top technologies and skills that employers are looking for in the data industry.
For instance, we can tell by looking at the dashboard that the top language that employers are looking for in data scientists is Python, followed by SQL and R.
The reason this project is so valuable is because it solves an actual problem.
Every job-seeker wants to know the top skills that employers are looking for in their field so they can prepare accordingly.
SkillQuery helps you do exactly this, in the form of an interactive dashboard that you can play around with.
The creator of this project has displayed crucial data analytics skills such as Python, web scraping, and data visualization.
You can find a link to this project’s GitHub repository here.
2. Credit Card Approval
This project was created to predict whether a person will be approved for a credit card or not.
I found it in the same video created by Luke Barousse, and the creator of this project ended up getting a full-time role as a data analyst.
The credit card approval model was deployed as a Streamlit application:
Image from Semasuka’s GitHub Project
You simply need to answer the questions displayed on this dashboard, and the app will tell you whether or not you have been approved for a credit card.
Again, this is a creative project that solves a real-world problem with a user-friendly dashboard, which is why it stood out to employers.
The skills displayed in this project include Python, data visualization, and cloud storage.
3. Social Media Sentiment Analysis
This project, which I created a few years ago, involves conducting sentiment analysis on content from YouTube and Twitter.
I’ve always enjoyed watching YouTube videos and was particularly fascinated by channels that created makeup tutorials on the platform.
At that time, a huge scandal surfaced on YouTube involving two of my favorite beauty influencers—James Charles and Tati Westbrook.
I decided to analyze this scandal by scraping data on YouTube and Twitter.
I built a sentiment analysis model to gauge public sentiment of the feud and even created visualizations to understand what people were saying about these influencers.
Although this project had no direct business application, it was interesting since I analyzed a topic I was passionate about.
I also wrote a blog post outlining my findings, which you can find here.
The skills demonstrated in this project include web scraping, API usage, Python, data visualization, and machine learning.
4. Customer Segmentation with Python
This is another project that was created by me.
In this project, I built a K-Means clustering model with Python using a dataset on Kaggle.
I used variables such as gender, age, and income to create various segments of mall customers:
Image from Kaggle
Since the dataset used for this project is popular, I tried to differentiate my analysis from the rest.
After developing the segmentation model, I went a step further by creating consumer profiles for each segment and devising targeted marketing strategies.
Because of these additional steps I took, my project was tailored to the domain of marketing and customer analytics, increasing my chances of getting hired in the field.
I have also created a tutorial on this project, providing a step-by-step guide for building your own customer segmentation model in Python.
The skills demonstrated in this project include Python, unsupervised machine learning, and data analysis.
5. Udemy Course Data Analysis Dashboard
The final project on this list is a dashboard displaying insights on Udemy courses:
Image from Medium
I found this project in a Medium article written by Zach Quinn, who currently is a senior data engineer at Forbes.
Back when he was just starting out, Zach says that this dashboard landed him a data analyst job offer from a reputable company.
And it’s easy to see why.
Zach went beyond simply using SQL and Python to process and analyze data.
He has incorporated data communication best practices into this dashboard, making it engaging and visually appealing.
Just by looking at the dashboard, you can gain key insights about Udemy’s courses, its student’s interests, and its competitors.
The dashboard also demonstrates metrics that are vital to businesses, such as customer engagement and market trends.
Among all the projects listed in this article, I like this one the most since it goes beyond technical skills and displays the analyst’s adeptness in data storytelling and presentation.
Here is a link to Zach’s article where he provides the code and steps taken to create this project.
I hope that the projects described in this article have inspired you to create one of your own.
If you don’t have any project ideas or face obstacles when developing your own, I recommend utilizing generative AI models for assistance.
ChatGPT, for example, can provide a wealth of project ideas and even generate fake datasets, allowing you to hone your analytical skills.
Engaging with ChatGPT for data analysis will allow you to learn new technologies faster and become more efficient, helping you stand out from the competition.
If you’d like to learn more about using ChatGPT and generative AI for data analysis, you can watch my video tutorial on the topic.
Natassha Selvaraj is a self-taught data scientist with a passion for writing. Natassha writes on everything data science-related, a true master of all data topics. You can connect with her on LinkedIn or check out her YouTube channel.
