5 Best Data Science Projects for Your Resume

Quick Highlights:

• Data science jobs are expected to grow by 35% from 2022 to 2032, adding approximately 17,700 new positions annually.
• Data scientists can expect a median salary of $103,500 per year, with the potential for higher earnings with more experience and advanced degrees.
• Including data science projects on your resume, particularly on GitHub, can demonstrate your practical skills, collaboration abilities, and technical proficiency to potential employers.
• Projects like sentiment analysis, fake news detection, movie recommendation systems, regression analysis, and classification algorithms are valuable additions to a resume, showcasing relevant skills and problem-solving capabilities.

From 2022 to 2032, there are supposed to be an extra 17,700 positions for data scientists every single year. This works out to an annual growth rate of 35%, according to the Bureau of Labor Statistics. In general, a data scientist can expect a median salary of $103,500 per year, according to US News. If you’re searching for a lucrative position in a growing industry, getting a degree in data science is an excellent choice. 

To get hired as a data scientist, you must figure out how to list data science projects on your resume. GitHub projects on a resume can show employers that you can collaborate with other people and use data science tools. In addition, you may want to use data science projects for a resume if you don’t have a lot of job experience. With the right data science projects to get hired in this career field, you can show employers that you are a competent worker and land your first interview.

Related:

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• Best Online Master’s in Data Science
• Top Bachelors Programs for Data Science
• Best Computer Science Masters Programs

When Should You Include Data Science Projects on Your Resume? 

While you don’t have to provide data science projects to get hired, it generally helps. Data science projects for a resume can show that you know how to use data science tools and work as a team. They are a concrete way to demonstrate your abilities to a potential employer. 

GitHub projects on a resume are especially important in the following situations. 

• You don’t have a college degree. 
• Your college degree level is a bachelor’s degree, but the job description asks for advanced degrees. 
• You don’t have a lot of experience working in the industry. 
• Your experience is in a different technology field, and you want to transition to working as a data scientist. 

Anytime your abilities are better than what your resume suggests, you will want to include data science projects to get hired. These can simply be listed with titles and descriptions on your resume. You can also include a link to GitHub projects on your resume so that employers can clearly see what your projects look like. 

The 5 Best Data Science Projects for Your Resume 

1. Sentiment Analysis Project 

One of the most popular types of data science projects for a resume is a sentiment analysis project. To create this project, you must be able to use R. With a sentiment analysis project, you must analyze words to determine a positive or negative sentiment. Sometimes, these projects are designed to go into extra detail. For instance, you may want the algorithm to consider whether the sentiment is happy, depressed, or upset.

When creating a data science project like this for your resume, you should start by finding a dataset of text samples. You can use emails, ads, or comments from social media. Then, you must make an algorithm that can sift through the samples and determine an underlying sentiment. Finally, you can use a word cloud to visualize your results. 

2. Fake News Detection Project 

If you’re searching for the best Python projects for your resume, this is a great option to use. In recent years, fake news has received a lot of attention because of the way it can spread quickly online. Social media companies combat fake news by creating algorithms that can spot it. 

You can replicate their approach using Python. When creating a data science project about fake news, you should start by making a TfidVectorizer. Afterward, you can classify the validity of each comment or post using Passive Aggressive classifiers.

3. Movie Recommendation Project 

Whether you are passionate about PowerBi or love natural language processing, there are many different data science projects for GitHub that you can choose from. If you’re searching for data science projects to get hired in the industry, you should think about making a movie recommendation project. This kind of project is ideal if you want to showcase your ability to work with R. 

With this project, you need to analyze an individual’s likes and dislikes. You will also need to look at their viewing history. To get this data, you can start by asking a group of people about the movies they have watched in the past. You should also ask them to rate these different movies. 

Next, you will need to make an algorithm that can identify responses from each person. This algorithm must be able to predict whether a different person with similar interests will like a movie they haven’t watched yet. For bonus points, you can try using Tableau, Power BI, or another data visualization tool to create charts and graphs of the resulting data. 

This project is especially important because of how familiar it is to hiring managers. Most likely, they’ve watched videos on Netflix or other streaming services in the past. As a result, they know exactly what a movie recommender project functions like in the real world.

4. Regression Project 

If you are trying to determine how to list data science projects on a resume, you should consider making a regression project. This type of project is extremely easy to create. It gets its name from regression algorithms, which are used to determine the relationship strength between two different variables. 

Data analysts often use regression algorithms to determine how one variable will end up impacting another variable. Because of how useful these projects are, you can create a regression project about many different topics. For example, you may want to use a regression algorithm to spot a connection between age and voting behavior.

5. Classification Project 

The last on the list of data science projects for your resume is classification projects. This kind of project uses machine learning algorithms to organize data points into categories. You will likely use these algorithms after you are hired as a data scientist because of how useful classification algorithms are for sorting through large amounts of data.

To create this project, start by picking the type of data you want to categorize and the categories you want to use. Initially, you can start by categorizing text messages. You must also give your machine learning algorithm the right parameters. Once it has categories for your data points, it can begin automatically sorting the data.

How to List Data Science Projects on a Resume 

Before you apply, take some time to figure out how to list data science projects on a resume. Many people include data science projects for a resume in a specific section. For instance, your resume may have sections devoted to “Projects,” “Work History,” and “Education.” Other than listing the name of the project, you should include a brief description of what you made and add links to the GitHub projects on your resume.

Is It Important to Link to GitHub? 

With data science projects for GitHub, you can show prospective employers what you are able to create. Beyond simply being a repository of your work, GitHub is where you can collaborate with other people on different projects. In addition to demonstrating your technical abilities, it can showcase your soft skills as well. For instance, your projects show that you are able to collaborate as a part of a team. 

Top Jobs in Data Science 

If you’re searching for data science projects for GitHub accounts, you’ve most likely already found a job you would like to do in this industry. Other than working as a data scientist, there are a number of positions that use data science, machine learning, deep learning, and natural language processing. Depending on your education and career goals, you may want to apply for some of the following positions.

• Data scientist: A data scientist earns a median wage of $103,500 in the United States, according to US News. As a data scientist, you would be responsible for using statistical methods to organize and analyze data. 
• Machine learning engineer: On average, machine learning engineers earn a salary of $162,007 per year, according to Indeed. A machine learning engineer uses algorithms and data to help machines process information and make predictions. 
• Data analyst: As a data analyst, you will handle managing databases, identifying data sources, cleaning data, and analyzing patterns in the data. Because this is an in-demand position, you can expect to earn an annual salary of $106,376, according to Salary.com. 
• Business intelligence analyst: A business intelligence analyst is paid an average salary of $101,535 each year, according to Glassdoor. This type of analyst gathers data together and analyzes it for trends or patterns. Then, they present their findings to stakeholders.
• Database administrator: As a database administrator, you can expect to earn a median salary of $99,890 per year, according to US News. Database administrators are responsible for making sure that databases are working properly. They must create and manage systems for storing data.
• Data architect: A typical data architect earns $131,809 per year, according to Glassdoor. It is their job to design, manipulate, and analyze database systems.

Should You Use Data Science Projects to Get Hired?

While creating data science projects for GitHub is an excellent way to counteract a lack of job experience, there are other reasons to include data science projects for a resume as well. GitHub projects on a resume are a representation of your technical skills and coding ability. In addition, learning how to list data science projects on a resume can help you demonstrate your abilities. You don’t have to have data science projects on your resume to get hired, but it can help you stand out of the crowd.

Sources:

Medium