The Power of Project-First Learning: A Deep Dive into Dataquest Projects

Your portfolio should consist of several projects that highlight your best work. For example, you might include a project where you used machine learning algorithms to predict customer behavior based on a dataset, or a project where you created an impressive data visualization to help a company understand its sales data. Another example could be a project that demonstrates your ability to clean and analyze a complex dataset, showcasing your data wrangling and statistical skills. These projects serve as a testament to your capacity to handle real-world problems and deliver valuable insights.

Employers value project portfolios highly. As an emerging field, data science focuses more on the work you have done rather than the number of years you have spent in the industry. In fact, without prior experience in the data science field, your project portfolio may be the determinant of whether you receive that crucial call back for an interview. This is because your projects substitute for work experience in your applications and prove to potential employers that you’re capable of performing the kind of data science work you’re applying for.

Creating an engaging narrative through your portfolio is essential in the field of data analytics, where facts and figures are utilized to weave a story. Hence, presenting your work creatively and effectively is just as crucial as the content you’re sharing. A solid portfolio not only increases your chances of landing a job but also places you at an advantage in a competitive job market.

The first step in making a high-quality portfolio is to know what skills to demonstrate. The primary skills that companies want in data scientists, and thus the primary skills they want a portfolio to demonstrate, are:

• Ability to communicate
• Ability to collaborate with others
• Technical competence
• Ability to reason about data
• Motivation and ability to take initiative

Any good portfolio will be composed of multiple projects, each of which may demonstrate 1-2 of the above points. If you want to learn more about what skills employers want to see a candidate demonstrate, here’s a good blog on what those skills look like and how to build a portfolio that demonstrates all of those skills effectively.

Pick projects that resonate with you. Remember, data science is broad and it’s impossible to be an expert in everything. The projects you select should allow you to delve into different facets of data science and progressively grow your skillset. Beginners can start with projects such as House Prices Regression or Titanic Classification. It’s advisable to complete multiple projects, each focusing on different aspects of data science like data collection, data cleaning, data exploration, data visualization, regression, statistics, and machine learning. Opt for projects that mimic real-world situations. For example, web scraping reviews from a food delivery website or scraping an online course website can provide invaluable hands-on experience. These kinds of projects not only help you understand how to collect and clean data but also expose you to the practical applications of data science.

Here are our top four beginner-friendly projects you can start with:

1. Build a Word Guessing Game
2. Build A Food Ordering App
3. Time-Series Forecasting on the S&P 500
4. Predicting The Weather Using Machine Learning

Or you can choose from hundreds of guided projects on the Dataquest learning platform

Or pick a topic that you’re interested in and motivated to explore. It’s very obvious when people are making projects just to make them, and when people are making projects because they’re genuinely interested in exploring the data. It’s worth spending extra time on this step, so ensure that you find something you’re actually interested in. A good way to find a topic is to browse different datasets and seeing what looks interesting. Here are some good sites to start with:

• Data.gov — contains government data.

• /r/datasets — a subreddit that has hundreds of interesting datasets.

• Awesome datasets — a list of datasets, hosted on Github.

• 17 places to find datasets — a blog post with 17 data sources, and example datasets from each.

In real-world data science, you often won’t find a nice single dataset that you can browse. You might have to aggregate disparate data sources, or do a good amount of data cleaning. If a topic is very interesting to you, it’s worth doing the same here, so you can show off your skills better.

Now that you’ve chosen a project, you need a dataset. There are many resources available online where you can find datasets. Some of our favorites are Kaggle, UCI Machine Learning Repository, and Google Dataset Search. Choose a dataset that aligns with your project goals. Ensure the data comes from reliable sources to maintain the credibility of your project. If you need a little help finding free datasets, here’s a good place to start.

Once you’ve found your dataset, it’s time to roll up your sleeves and clean it. This step involves removing any errors or inconsistencies in the data. It might seem tedious, but remember, a clean dataset is crucial to the success of your project. For instance, you might need to handle missing values, remove duplicates, or correct inconsistent entries.

Now comes the fun part – exploring the data! This involves looking at the data from different angles, visualizing it, and trying to find patterns or insights. It’s like being a detective, where your clues are hidden in the data. For example, for a house price prediction project, you might plot the distribution of prices, investigate the relationship between the number of rooms and price, or identify neighborhoods with the highest average prices.

The important thing is to stick to a single angle. Trying to focus on too many things at once will make it hard to make an effective project. It’s also important to pick an angle that has sufficient nuance. Here are examples of angles without much nuance:

• Figuring out which banks sold loans to Fannie Mae that were foreclosed on the most.
• Figuring out trends in borrower credit scores.
• Exploring which types of homes are foreclosed on most often.
• Exploring the relationship between loan amounts and foreclosure sale prices

All of the above angles are interesting and would be great if we were focused on storytelling, but aren’t great fits for an operational project.

Once you’ve built your model and made some predictions, it’s time to share your findings. This could involve creating a report or presentation, or even writing a blog post about your project. Remember, communication is a key skill in data science. It’s not enough to build a great model; you also need to explain it to others.

Let’s consider a beginner data science project, such as analyzing a dataset of Titanic passengers to predict survival. After you’ve built your model and made some predictions, you might create a presentation to share your findings.

In your presentation, you could start by explaining the problem you were trying to solve and the data you used. Then, you could discuss the exploratory data analysis you performed, the insights you gained, and the model you chose.

Next, you could present the results of your model. This could include the accuracy of your model, the most important features for predicting survival, and any interesting patterns or trends you discovered. For example, you might find that passenger class and gender were important factors in survival, which could lead to a discussion about the social dynamics of the time.

Finally, don’t forget to discuss any challenges you faced during the project and how you overcame them, as well as any future improvements you would like to make to your model. This shows that you can reflect on your work and are always looking to improve, which are important qualities in a data scientist.

Remember, the goal is not just to present your results, but to tell a story with your data that engages your audience and demonstrates your understanding of the project.

After you’ve communicated your results, don’t forget to get feedback. This can help you improve your skills and make your future projects even better. Don’t be afraid of criticism. Instead, view it as a learning opportunity. You could ask your peers or mentors to review your project and provide feedback.

Finally, showcase your hard work on GitHub. It’s a great platform to host your code, data files, models, and notebooks. This allows potential employers to see your code quality, organization skills, and style of documentation. For the house price prediction project, you might create a GitHub repository containing your code, the cleaned dataset, any visualizations you created, and a readme file explaining your project.

Link your GitHub repositories to your LinkedIn profile, resume, or personal website, which can help attract the attention of recruiters. Here’s a more detailed tutorial on how to upload and present your projects on GitHub.