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I landed my first internship in data analytics in 2020.
Since then, I moved into a full-time senior position, landed several freelance data analytics jobs, and consulted for companies in different parts of the world.
During this time, I reviewed resumes for data analyst positions and even shortlisted candidates for jobs.
And I noticed one thing that separated the top applicants from everyone else.
Projects.
Even if you have no data industry experience or technical knowledge, you can stand out from others and get hired solely based on the projects you show on your resume.
In this article, I'll show you how to create projects that will help you stand out from the competition and land your first job as a data analyst.
If you're reading this article, you probably already know that it's important to show projects on your resume.
You may have even created a few projects of your own after taking an online course or boot camp.
However, many data analytics projects do more harm than good to your portfolio. These projects can actually reduce your chances of getting a job and should be avoided at all costs.
For example, if you earned the popular Google Data Analytics Certificate on Coursera, you've probably completed the capstone project that comes with this certification.
Picture of Coursera
However, more than 2 million people have enrolled in the same course and potentially completed the same capstone project.
Chances are, recruiters have seen these projects on the resumes of hundreds of applicants and are not impressed.
Similar logic applies to any other project that has been created many times.
Creating a project using the Titanic, Iriseither Housing in Boston The dataset on Kaggle can be a valuable learning experience, but it shouldn't be displayed in your portfolio.
If you want to have a competitive advantage over other people, you must stand out.
That is how.
A project that stands out must be unique.
Choose a project that:
- Solve a real world problem.
- It cannot be easily replicated by other people.
- It's interesting and tells a story.
Much of the advice about data analysis projects on the Internet is inaccurate and unhelpful.
You will be asked to create generic projects, such as an analysis of the Titanic data set, projects that add no real value to your resume.
Unfortunately, the people who tell you to do these things don't even work in the data industry, so you need to use judgment when following this advice.
In this article, I'll show you examples of real people who have landed jobs in data analytics. due to their portfolio projects.
You'll learn about the types of projects that actually get people hired in this field so they can build something similar.
1. Job Trend Tracking Dashboard
The first project is a dashboard that shows job trends in the data industry.
I found this project in a video created by Luke Barousse, a former senior data analyst who also specializes in content creation.
Here is a screenshot of this panel:
Image from SkillQuery
The dashboard above is called SkillQuery and shows the top technologies and skills that employers are looking for in the data industry.
For example, looking at the dashboard we can say that the top language employers look for in data scientists is Python, followed by SQL and R.
The reason this project is so valuable is because it solves a real problem.
Every job seeker wants to know the top skills 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 you can play with.
The creator of this project has demonstrated crucial skills in data analysis, such as Python, web scraping, and data visualization.
You can find a link to this project. 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 position as a data analyst.
The credit card approval model was implemented as a Streamlit application:
Picture of Semasuka GitHub Project
You simply answer the questions displayed on this panel 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 an easy-to-use dashboard, which is why it stood out to employers.
Skills showcased in this project include Python, data visualization, and cloud storage.
3. Sentiment analysis on social networks
This project, which I created a few years ago, involves performing sentiment analysis on YouTube and twitter content.
I've always loved watching YouTube videos and was especially fascinated by the channels that created makeup tutorials on the platform.
At that time, a huge scandal emerged on YouTube involving two of my favorite beauty influencers: James Charles and Tati Westbrook.
I decided to analyze this scandal by collecting data from YouTube and twitter.
I built a sentiment analysis model to measure public sentiment about the dispute and even created visualizations to understand what people were saying about these influencers.
Although this project had no direct commercial application, it was interesting as I analyzed a topic that I was passionate about.
I also wrote a blog post describing my findings, which you can findtwitter-youtube/” rel=”noopener” target=”_blank”> here.
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 several mall customer segments:
Picture of Kaggle
Since the data set used for this project is popular, I tried to differentiate my analysis from the rest.
After developing the segmentation model, I took it a step further by creating consumer profiles for each segment and devising specific marketing strategies.
Because of these additional steps I took, my project was tailored to the domain of customer marketing and analytics, which increased my chances of getting hired in this field.
I have also created a tutorial on this project, which provides a step by step guide to build your own customer segmentation model in Python.
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 that displays information about Udemy courses:
Picture of Half
I found this project in a Medium article written by Zach Quinn, who is currently a Senior Data Engineer at Forbes.
When he was just starting out, Zach says this panel earned him a job offer as a data analyst from a reputable company.
And it's easy to see why.
Zach went beyond simply using SQL and Python to process and analyze data.
It has incorporated data communication best practices into this dashboard, making it attractive and visually appealing.
Just by looking at the dashboard, you can learn key information about Udemy courses, your students' interests, and your competitors.
The dashboard also displays 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 because it goes beyond technical skills and shows the analyst's skill in storytelling and presenting data.
Here is a link to Zach's article where he provides the code and steps followed to create this project.
I hope the projects described in this article have inspired you to create your own.
If you don't have any project ideas or are facing obstacles when developing your own, I recommend using generative ai models to help you.
ChatGPT, for example, can provide a wealth of project ideas and even generate fake data sets, allowing you to hone your analytical skills.
Using ChatGPT for data analysis will allow you to learn new technologies faster and be more efficient, helping you stand out from the competition.
If you want to learn more about using ChatGPT and generative ai for data analysis, you can check out my video-tutorial in the subject.
Natasha Selvaraj is a self-taught data scientist with a passion for writing. Natassha writes on all things data science, a true master of all things data. You can connect with her at LinkedIn or look at it Youtube channel.
