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7 Data Pet Projects To Learn Data Science Faster

A little creativity and an open mind are all it takes to identify a personal project.

Photo by Thought Catalog on Unsplash
Photo by Thought Catalog on Unsplash

I am a firm believer in following your heart. That said, learning Data Science online was a struggle because I had no idea where my heart was. I would follow a beginner course and only go so far because I felt completely disconnected from the data problem they were working on.

This is where a pet project comes in.

A pet project is a venture, activity, or goal, especially pursued out of personal interest rather than because it is generally accepted as necessary or important. –Word Hippo

This is usually a personal data-related project interesting to you with familiar data where you look for insights and answer a given question.

In this article, I will list seven example pet projects to get you started. Some may require data collection, while others may have the data available to you.

How to choose a project

There are all sorts of blogs and articles online giving suggestions on project ideas to beginners. These are all well and good but how do you decide? The four pointers below will help you make a decision.

  1. You have a valid and important question or problem to be answered by the data.
  2. The data is available or can be collected easily.
  3. The project is meaningful to you.
  4. The project will not take too much time. Set time for every part of the process from problem understanding to analysis to modeling. Without a clear time frame, you might temporarily abandon the project when things get tough or get bogged down trying to use the perfect tool or process. Remember, we are looking to learn here, not to prove our genius.

Benefits of working on pet projects

  1. Develop your skills and learn new techniques in the context of a real problem. This boosts your confidence when approaching future problems.
  2. Re-frame your mind into an analytical mindset that breaks down problems into manageable steps and chunks
  3. Learn how to ask questions by cultivating a curiosity that goes beyond the obvious questions.
  4. Helps you make data-driven decisions in your personal life or business based on the data analysis.
  5. Strengthens your data storytelling skills. Using the charts and graphs from the project, you can create a compelling presentation of your findings then upload it into your GitHub and share it with potential job recruiters.

Let us now get into the projects.

Project 1: Monitor your diet

The food we eat largely determines the quality of our lives and so we find ourselves constantly working to change or improve our diets.

Tracking your eating habits can have a massive influence on changing food habits as evidenced by Kevin Jacobs who consistently lost weight while tracking his body’s response to a low-carb diet.

In his blog post, he lays out the entire process from recording his food and taking body measurements, the tools he used for analysis, and some visual plots explaining his findings.

Example question to answer: Will eating a low-carb diet for a month lead to significant weight loss?

Features to track: weight, belly measurements, daily calories, fat, protein, and carbs intake.

Data collection: An old-fashioned notebook or spreadsheet is a great place to start. You can also use apps such as loseit.

Project 2: Track your exercise and fitness

Working out is another activity that we all want to exercise but sometimes fail to be consistent. Tracking your workouts may become that motivational partner that keeps you accountable and committed.

Take Bobby Muljono for example who tracked his weight loss after exercise and nutrition, and eventually used machine Learning to predict future weight loss based on exercise and diet.

We also see Jeh Lokhande combine his sleep, fitness, and web browsing data to analyze how various factors are correlated with the quality of sleep.

Example question: Will my sleep significantly improve with consistent exercise?

Features to track: sleep amount and quality, and exercise time and intensity.

Data collection: Keep a spreadsheet, use a fitness app or invest in a health wearable. James Clear offers a helpful guide on how to keep a workout journal.

Project 3: Time management and productivity

Productivity is another elusive concept that we all want to master. With all the Technology and gadgets around us, time-wasting is a stubborn problem.

Erin Greenawald used the Toggl app to track her time for one month and learned that watching TV took up an amount of time she was hugely uncomfortable with. She also took up listening to interesting podcasts while driving, where a lot of her time went.

Example question: Where am I spending less time than I should?

Features to track: Group daily activities into a few categories such as work, self-development, relationships, and play, then measure the time spent for each category over a week or month.

Data collection: You can log activities in a spreadsheet, or use a productivity tracker app like toggl.

Project 4: Money spending

While we all want to spend our hard-earned money on important things, save and invest a good amount and give the rest, this rarely happens. Tracking your spending habits may expose where your money is disappearing into. It may also reveal that you need to get serious and earn more money.

Lorenzo Rosa shows us how we can consolidate our spending from various bank accounts using an app called Yolt. She uses python to create a personalized monthly report of her finances. See also Agatha of The Wealth Tribe Community who successfully tracked every dollar that she earned in 2020.

Example question: Where can I cut my spending and consequently increase my income in the next 6 months?

Data collection: record your spending on a notebook then transfer to a spreadsheet, or use an expense tracking app such as Mint.

Project 5: Domain-specific business data

If you run a business or someone close to you does, chances are that there is some form of record-keeping involved.

Come up with some challenges that face the business and use the data to see if you can offer a solution. It may also happen that you will find other hidden insights and trends as you explore the data.

Example question: Are there products or services that are more popular during certain seasons or months?

Data collection: Enter the data into an excel sheet or use a business accounting tool such as Quickbooks.

Project 6: Job search, interviews, and placements

The job search process can be very frustrating and time-consuming. A good strategy is to analyze several job postings that interest you and find the top skills common to those positions.

Ken Jee, one of my favorite content creators, has an entire video series (Data science project from scratch) on his YouTube channel where he scraped and analyzed job placement data from the Glassdoor website. I highly recommend it for every beginner as a project-based learning approach.

As for tracking your job applications and interviews, James Mayr provides a simple spreadsheet to take you through the whole process.

Example Question: What are the 5 top skills needed to get into my dream job?

Data collection: A spreadsheet, or scraping data from a website.

Project 7: Hobby specific data

What is it that you enjoy doing and are good at, and would like to take it to the next level? Be it art, content creation, or a sport, data might be your answer.

Take Ken Jee again. He used data to propel his passion as a golfer in college, realized professional golf was not for him, settled into a data career, and is now a leading sports analytics expert. What started as a simple project morphed into a thriving career that is impacting lives including mine.

Example question: Which areas of [insert your hobby here] do I need to work on so that I can make a living off of it?


Presenting the findings

Finally, an important skill that you can acquire from a pet project is communication through data storytelling. This involves presenting the project in a simplified manner through a report or a blog post.

To tell a story effectively, put yourself in your audiences’ shoes and assume no technical background on their part. Pull them in with the introduction by explaining the situation and pain points that started the whole project.

Mention how you collected the data and captivate them with clear visualizations of your findings and deductions. Finally, conclude with a convincing and satisfying conclusion on why undertaking the project was beneficial, and include the way forward with real action points for tackling the problem.

Sharing your project is not so much for feedback or criticism, but to ensure you do your best work in a way that others can comprehend. Upload the project files on GitHub and share the link through a blog post, on social media, or in your resume.


Conclusion

It is important to work on a real-world project as soon as possible while learning data science. A pet project is personal and beneficial to you and has no outside pressure and is, therefore, a good place to start.

Through a pet project, you acquire the practical skills to solve real problems. This will also keep you motivated and confident in handling new problems as you expand your toolset.

I wish you the best in your journey. If you liked this article and would like to get more like these from me, subscribe [here](https://medium.com/@suemnjeri/membership). If you are not yet a member on medium, join here to read more valuable content. Thank you for reading!


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