Essential Data Science Projects for Beginners to Enhance Your Portfolio
Written on
Chapter 1: Introduction to Data Science Projects
As an aspiring data scientist, you've likely encountered the advice to "engage in data science projects" numerous times. These projects not only enhance your learning experience but also help distinguish you from other data science enthusiasts eager to enter the field. However, it's essential to be cautious; not all projects will bolster your resume. In fact, including inappropriate projects could be detrimental.
This article will guide you through the key projects that should be featured on your resume, complete with sample datasets and tutorials to assist you in executing them.
Skill 1: Data Collection
Data collection and preprocessing are vital skills for any data scientist. In my role, a significant portion of my responsibilities involves gathering and cleaning data using Python. Once the business requirements are established, the next step is to access relevant data from online sources, which can be achieved through APIs or web scraping techniques. Following this, the data must be cleaned and organized into data frames suitable for machine learning models, which can be quite time-consuming.
To demonstrate your capabilities in data collection and preprocessing, consider the following projects:
Web Scraping — Food Reviews Site
Tutorial: Zomato Web Scraping with BeautifulSoup
Language: Python
Building a web scraper to collect reviews from a food delivery service is a practical project that adds value to your resume. You can enhance this project by developing a sentiment analysis model to classify reviews as positive or negative.
In this video, discover five beginner data science projects to kickstart your journey!
Web Scraping — Online Course Site
Tutorial: Build a Web Scraper with Python in 8 Minutes
Language: Python
If you're looking for the best online courses in 2021, scraping an online course platform to gather data can be very useful. You can further this project by visualizing data around pricing and ratings, helping you find quality courses at affordable rates.
Additionally, consider creating projects that involve collecting data through APIs or external tools, as these skills are often essential in the workplace. For example, use the Twitter API to gather data associated with a specific hashtag.
Skill 2: Exploratory Data Analysis
Once you've collected and stored your data, it's crucial to analyze the variables in your data frame. You'll want to understand the distribution of each variable and their interrelationships. Answering questions based on the data is a common task for data scientists, often surpassing predictive modeling in frequency.
Here are a couple of EDA project ideas:
Identifying Heart Disease Risk Factors
Dataset: The Framingham Heart Study
Tutorial: The Framingham Heart Study: Decision Trees
Language: Python or R
This dataset includes factors like cholesterol levels, age, and family history, which can be analyzed to predict heart disease risk. You can explore questions such as the impact of diabetes on early heart disease risk.
World Happiness Report Analysis
Dataset: World Happiness Report
Tutorial: World Happiness Report EDA
Language: Python
This report tracks six key factors influencing global happiness. You can analyze which country ranks highest in happiness and what factors contribute most significantly.
Skill 3: Data Visualization
As a data scientist, you'll often present findings to clients who may not have a technical background. Thus, effective data visualization is crucial. An interactive dashboard can be an excellent way to convey insights, making them easily digestible.
Here are some projects to showcase your data visualization skills:
Covid-19 Dashboard Creation
Dataset: Covid-19 Data Repository at Johns Hopkins University
Tutorial: Building Covid-19 Dashboard with Python and Tableau
Language: Python
After preprocessing the data, you can create an interactive dashboard using Tableau, a highly sought-after tool in data visualization.
IMDB Movie Dataset Dashboard
Dataset: IMDb Top Rated Movies
Tutorial: Exploring IMDb Top 250 with Tableau
You can design an interactive dashboard with this dataset, which can be shared on Tableau Public, providing potential employers the opportunity to engage with your work.
Skill 4: Machine Learning
Finally, it's essential to undertake projects that highlight your machine learning expertise. I recommend including both supervised and unsupervised machine learning projects in your portfolio.
Sentiment Analysis on Food Reviews
Dataset: Amazon Fine Food Reviews Dataset
Tutorial: A Beginner’s Guide to Sentiment Analysis with Python
This project will help you analyze customer sentiment toward products, a critical area for many businesses.
Life Expectancy Prediction
Dataset: Life Expectancy Dataset
Tutorial: Life Expectancy Regression
Here, you'll predict life expectancy based on various factors, showcasing a range of skills from classification to regression.
Breast Cancer Analysis
Dataset: Breast Cancer Dataset
Tutorial: Cluster Analysis of Breast Cancer Dataset
Implementing K-means clustering will help you analyze unlabelled data, a common scenario in real-world applications.
Conclusion
It's crucial to present a diverse array of projects demonstrating your skills in data collection, analysis, visualization, and machine learning. Online courses alone won't suffice for mastering these competencies, but ample tutorials are available to guide your learning.
With foundational knowledge of Python, you can follow these tutorials, replicate solutions, and explore various projects independently. For those just starting in data science without formal education, showcasing your portfolio projects is one of the best ways to attract potential employers and secure your first entry-level position in this field.
Remember, "Sooner or later, those who win are those who think they can." — Paul Tournier