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Data Science Projects for Beginners



The saying goes, “Practice makes perfect”. This expression does not escape the tech industry. In this industry, practicing is critical to how much you will succeed, especially when you are a beginner. It requires a certain level of motivation and determination to break into tech. 

So, there is no surprise that there is, in fact, a step that you can take even before attending a college or bootcamp. This step often evades many young professionals or just beginners in general. That step is merely practicing and practicing and practicing. 

While you don’t have to fully commit to training yourself everything there is to know about data science, familiarizing yourself with some data science projects prior to college or a bootcamp can be extremely beneficial. We understand it may be challenging to decide where to start when thinking about data science projects so we’ve got you covered. This article will explore some of the best data science projects that you, as a beginner, can work on.

Beginner-Friendly Projects

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Practice, practice, practice!



One of the best ways to become familiar with data science, before attending any college or bootcamp, is by trial and error. What does this mean? Well, simply put, this translates into trying out some smaller beginner projects on your own. These projects can be used as a sort of test to see if you are truly passionate about the field. 

They will also allow you to access if this industry or a specific tech position is the right fit for you. On top of that, they can set the tone for when you enroll in a college or bootcamp. There are many beginner data science projects you can try out. Below is a list of some of these projects that would be ideal to try if you are a beginner.

1. Data Cleanup

The main task: Accessing two Excel sheets and correcting the flaws and mistakes.

Data scientists spend the majority of their time cleaning up data. If you can prove that you are already experienced at this task, then you will show how much more committed you are to your program or job. What you should learn from this is to view two Excel spreadsheets and figure out the mistakes and how to correct them. 

Along with correcting any mistakes you encounter, you will also learn how to fill in any missing data, handle formatting, and how to load in the Excel files. To find some messy data sets and begin your “process”, try looking at open data sites like this one. Once you’re there, you will be able to search for a specific topic you may be interested in.

2. EDA: Exploratory Data Analysis

The main task: Come up with relevant questions, test them with visualizations, identify trends, find similarities between variables, and display results with visuals.

Another essential task to learn as a data scientist is understanding exploratory data analysis, also known as EDA. It is a process where you form questions and proceed to investigate them visually. This is an important task as a data scientist as it allows you to understand the various data. Furthermore, it can even help you make more discoveries that you would not have otherwise been able to find. This is a great skill set to have already and bring to your program or new career. 

When beginning an EDA, you should keep a few things in mind. First, come up with relevant questions, then test those questions with visualizations. Next, identify any trends in the data you are viewing and look for similarities between these variables. Finally, display these results with visuals like a scatter plot, for example. 

An example of an EDA is from a post by Will Koehrsen that you can see here. In his example, he was viewing doctors’ appointments, looking particularly at no-shows or missed appointments, and how much they cost the health care system every year.

3. Machine Learning

The main task: Identify reasoning, organize data, select the accurate metric, apply engineering and tech skills, and include hyperparameter tuning.

Machine learning is another great area of tech to add to your knowledge as an aspiring data scientist. For this project, you will want to stick with the basics by starting small so that you can understand the whole process. One way to start is with either linear regression or logistic regression. 

First, decide on a project that will be best for you. The following procedures should be included in your reasoning for whichever one you choose. First, include your reason for why you choose a specific model. Next, you will begin splitting the data into training/test sets. After that, you will select the right metric for your project, use engineering and selection, and then finally include hyperparameter tuning. 

 4. Presentation

The main task: Explain your research to a group of people with a PowerPoint presentation

A great skill to practice continuously throughout any of these projects is how to communicate your results to an audience. All great data scientists will have to present their findings and data to a group at some point in their careers. If you cannot explain your results, then the process or findings could be deemed useless. Practice is key when it comes to this project. Start with practicing in front of a friend or family member. This is so you can receive feedback and also practice making eye contact with your audience.

Some great tips in communicating your research and results include understanding and knowing your audience. You should tailor your presentation for who you will be speaking to, considering different audiences will have different levels of understanding in the subject matter. For example, presenting your results to a group of bystanders is going to be a lot different than presenting it to a group of fellow data scientists. 

More importantly, do not overcrowd the slides on your PowerPoint. Remember only to use relevant visuals that you feel are necessary to get your point across. You will want your audience to stay focused and entertained. Lastly, make sure your presentation and PowerPoint flow well together while staying on track and being relevant. Communication will be a skill used greatly as a data scientist so it’s crucial to ace this.

Get Started on a Data Science Project Today

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Take your first step towards a tech career today just by practicing some beginner data science projects.



The projects listed above are just a few of the many that you can explore online. Do not stop at just the projects mentioned in this article but discover some on your own. Dedicate some time each day to practicing, and you will be surprised at how many tasks you will be able to accomplish in just a few weeks. Once you are confident that you have mastered a certain skill or project, then move on to the next. Better yet, move on to a more difficult task or project to keep things interesting. Challenge yourself! You will thank yourself later.

These data science projects will essentially set you up for success when you do decide to enroll in a bootcamp or computer science program. You will certainly be ahead of many students in your class, thus accelerating your education. Furthermore, if you understand the basics of your program, then you will be able to learn more quickly in any educational setting.

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