Top 15 amazing data science projects to try out in 2021
Data is the new oil of the 21st century. So, it’s no wonder that interesting data science projects fuel various industries. They also drive the careers of data scientists to new heights and become a gem in their portfolios.
If you’re an aspiring data specialist or an enthusiastic business owner, this post will tell you about worthy data analysis ideas in 2021. Most of the projects that you’ll see here are implemented in Python. We’ll also go over both simple and advanced implementations to cater to all levels.
Data science project from zero to hero
Before we dive into popular data science projects and solutions, let’s grapple with the basics. Conducting a good data science project always takes time and upfront effort. Often, this undertaking goes beyond coding. Instead, it presupposes sophisticated planning and a calibrated step-by-step process.
The following checklist will help fetch a business value from each unique project and minimize risks.
Define the goals.
Most data science project ideas start with a vague aim. However, a clear organizational need is key to ensure its success. Thus, identify sound analytical goals, such as a specific business problem or process. For example, data science projects in healthcare always tackle a particular issue. This can identify microscopic deformities in the scanned images or precision medicine in healthcare.
Then refine the goal until you have explicit KPIs in mind. Remember that all good data science projects are problem-centric, not data-centric.
Collect the data.
The second step is to fetch and merge usable data from various sources. Here, you have quite a few options:
- Tap into the internal database
- Explore Application Programming Interfaces your company uses
- Scrape open data available online.
In most cases, you’ll need a combination of the three to get enough insights for your project.
Exploratory data analysis.
This is the stage most developers shudder at. During this phase, data scientists analyze the collected information. And although data preparation guzzles up 80% of the groundwork, it is a mandate for every project.
Here, data analysts clean the obtained data and ensure its compliance with privacy regulations. They also pay due diligence to transparency, equity, and accountability issues. Data cleansing will remove corrupt or erroneous records from a set of data.
Once you have cleaned the data, enrich it further by merging 3rd-party data from an external authoritative source. This step will help you make more informed decisions and crystallize the essential data features.
Now it’s time to visualize a whole data system or parts of it. A graphic representation ensures the effective use of data. It also serves as a blueprint for building new solutions or overhauling a legacy application.
This is not an exhaustive list of a data analysis project life cycle. However, these are essential stages that lay the ground for further iteration.
Data science project examples: inspo board for 2021
It’s challenging to brew up a decent project idea within the realm of data analytics. However, these are necessary to reinforce your beginner skills. If you’re a business owner, data science projects can ease your operational pains. A hands-on understanding of how core concepts are applied is a must.
With that said, here’s a list of 15 interesting data science projects that address different aspects related to data science. Here, you’ll encounter both simple and complex data science projects currently in existence.
Recommendation engines are one of the most prominent data science project examples. Their usage rate is increasing daily. You can see these systems embedded into blogs, E-commerce websites, streaming platforms, and others.
Essentially, recommendation engines are a type of data filtering system. They aim to predict user preferences and make relevant suggestions to users. These systems feed on user data, browsing history, and content data. Although you can create them in several programming languages, Python is more common here.
The technology comes into action each time when Netflix or Amazon suggests a new item. Netflix even sponsored a competition to enhance its recommender system. The result was an unrivaled recommender technology that leans on SVD and Restricted Boltzmann Machines.
From a tech standpoint, these python data science projects may use the following techniques:
- Collaborative filtering. This technique relies on similarities between users and items simultaneously to provide recommendations. It considers the user-item interaction only.
- Content-based filtering. This method leverages additional information about users to generate recommendations. Hence, demographic analysis and the type of content powers the engines.
Both techniques are at the heart of most recommender engines. Those are beginner-friendly, but you can implement more advanced and complicated ones.
The popular machine learning algorithms for building a recommender system are:
- Association rules mining – to analyze data for patterns, or co-occurrences.
- Slope one algorithm – for item-based collaborative filtering based on ratings.
- Decision tree algorithm – CART and C4.5 for recommender systems.
Fake news detection
This data analysis project idea is another machine-driven implementation of data science. Any media company can invest in it to automatically identify whether or not the circulating news is a hoax. This way, you don’t need employees to manually browse thousands of news-related articles. Besides, studies have showed that ML and NLP algorithms have the potential to amplify traditional fact-checking.
Fake news detection is a beginner-friendly Python data science project. Technically wise, it leans on a real-time ML model that can scan social media news for authenticity. First, you scrape the dataset of both real and fake news. To ease the strain, most data specialists tap into publicly available LIAR or RealNews. Then, you need to train the text classification models.
To do this, you can use standard classifiers such as:
- Naive Bayes – a simple technique for constructing classifiers.
- Logistic Regression – to model the probability of a discrete outcome.
- Decision Tree – for classification and regression.
- In other cases, you can train your custom classifier.
Credit card fraud detection
Financial frauds continue to climb to new heights in 2021. Over the last years, the number of identity theft by credit cards increased by over 44%. However, a business can take heart with fraud detection systems and new advancements in machine-driven systems.
Therefore, recognizing fraudulent credit card transactions is one of the top data science projects today. To execute this project, developers have to put their best foot forward and apply complex algorithms. Unlike previous Python data science projects, you’ll need the programming language of R.
The latter is then combined with algorithms, such as:
- Artificial Neural Networks
- Gradient Boosting Classifier
- Logistic Regression
- Decision Trees (e.g. XGBoost and Light GBM)
- Random Forest
- One-class SVM, and others.
In short, you need to build and train models on the data collected from previous transactions. The models use and analyze several factors. The latter comprises a variety of data, including the amount, the sender location, the transferee location, and others. The same factors will help label future transactions as fraudulent or genuine.
Customer segmentation is another simple, yet the powerful application of data science in business. It usually refers to dividing the consumer market into clusters (segments) according to certain characteristics.
Companies use the clustering process to track similar categories of customers and target potential audiences. Thus, before running an email campaign, marketers group the audience based on relevant characteristics.
Customer segmentation falls into different types, including:
- Demographic Segmentation – gender, age, education level
- Geographic Segmentation – place of residence/work/study, etc.
- Psychographic Segmentation – hobbies, lifestyle, values, goals, and others
- Technographic Segmentation – technology ownership and usage
- Behavioral Segmentation – actual and potential behavior of clients
- Needs-based Segmentation – product needs of specific customer groups
- Value-based Segmentation – strategic importance of the customer, credit risks, and other business rules.
The unsupervised learning process underpins this data science project. Hence, customer segmentation has a narrow learning curve for aspiring data specialists. K-means clustering is the go-to strategy for this data science project.
Breast cancer classification
Let’s come back to data science projects in healthcare. This industry is a frequent flyer at data science consulting. The applications of this discipline vary from medical image analysis to anomaly detection. Here, you can contribute to the medical field by classifying breast cancer with Python. The primary goal is to identify whether breast cancer is benign or malignant.
To implement this Python-based system, you can look into the IDC_regular dataset. It’ll allow the solution to detect carcinoma. The IDC_regular includes histology images for cancer-inducing malignant cells. Hence, you can train your model on this dataset. Convolutional Neural Networks also work wonders for this task, combined with Python libraries.
Sentiment analysis projects
Sentiment analysis or opinion mining is a common project for AI companies. Sentiment analysis can come into existence by using a combination of techniques. These include Natural Language Processing (NLP), Machine Learning, Text Mining, and Information Theory as well as Semantic Approach.
Opinion mining allows businesses to identify the subjective information in the source material. Thus, obtained data fall into positive, negative, or neutral. Data scientists mine opinions from a multiplicity of sources, including online reviews and surveys. As a result, textual data allows companies to track brand and product sentiment in customer feedback. The nature of the sentiment also lays the ground for a deep dive into customer needs.
This application falls into the category of complex data science projects. Thus, you need the language R and the dataset by the ‘janeaustenR’ package. General-purpose lexicons like AFINN and Loughran are also essential for this project. Then, you need to perform an inner join and build a word cloud to see the output.
Speech emotion recognition
While we’re on this note, let’s discuss speech emotion recognition as one of the promising top data science projects. Speech Emotion Recognition is scanning and classifying speech signals to identify the embedded emotions. Today, automatic speech emotion recognition is one of the main data science challenges. Thus, we still have seen noneffective real-time detection methods for commercial use. However, you can try your hand at grasping the subjective nature of human speech.
This Data Science project relies on the librosa library. The latter is a Python package for music and audio analysis. It provides the building blocks necessary to detect the emotions of human-machine communication users.
The following datasets come in handy to build an emotion classifier:
All three are available to the public. They contain audio files across seven common emotion categories. Thus, they can classify emotions into neutral, happy, and sad. The spectrum also features angry, fearful, disgusted, and surprised data samples.
Gender and age detection
Remember those funny face scanners that tell your age from the look of your face? Age and gender recognition apps can also become powerful AI business solutions. In its essence, such data science project ideas boil down to Machine Learning and Computer Vision. Also, you’ll require hands-on experience with:
- Convolutional Neural Networks to display the age group and gender from visual stimuli.
- Pre-trained models – ‘Tal Hassner’ and ‘Gil Levi’ for the ‘Adience’ dataset.
- .pb, .prototxt, .pbtxt, & .caffemodel files.
Many companies are benefiting from these tools. These solutions make it easier to upgrade customer experience and cater to their needs better. Therefore, this interesting data science project will certainly become a headliner in your portfolio.
Driver drowsiness and fatigue detection
Driver fatigue and micro-sleep often lead to thousands of casualties each year. A paper by the National Sleep Foundation shows that around 20% of drivers fall asleep behind the wheel. However, crashes can be prevented before serious consequences follow.
Driver drowsiness detection app is one of the latest prevention methods. Here, the system keeps tabs on the driver’s eyes. If the driver is sleepy, it beeps an alarm. Hardware such as a webcam is essential for this implementation to detect frequent closing of eyes. Also, you’ll need OpenCV for face detection and Keras to facilitate Deep neural network techniques.
The biggest gripe that haunts most business owners is the lack of a workforce for mundane tasks. And that’s what paved the way for AI assistants or chatbots. The modern chatbot landscape is very diverse and exciting. Hence, beginner developers have an established background to get this project off the ground.
Chatbots are used to reduce customer service costs and achieve business scalability. This application uses Deep Learning techniques to engage with consumers and Python as the main coding language.
Virtual assistants fall into two types according to the development methods:
- retrieval-based chatbots – they use keyword matching, machine learning, or deep learning to choose the most appropriate response.
- generative-based models – they produce original combinations of language instead of choosing from pre-defined responses.
The second type of chatbot lives by consuming massive amounts of data to train the model. Therefore, a retrieval-based chatbot is less difficult. The project calls for a profound knowledge of Python, Keras, and Natural language processing (NLTK).
To build one of the most interesting data science projects, you need:
- Download the chatbot code and a data set (available online)
- Import and load the data file
- Preprocess data (tokenization, lemmatization, duplicate removal)
- Create training and testing data
- Build the model (Keras sequential API)
- Predict the response (Graphical user interface).
You can tailor the data according to business needs and train the application with high accuracy.
Traffic signs recognition
You can upgrade your Python proficiency even further by building a road sign recognition system. This data analysis application allows you to recognize road sign information with great accuracy. It also keeps the driver aware of speed limits, traffic signals, and other vital traffic signs.
Automatic recognition operates by capturing real-life images through a camera. It then compares the visual input with the information in the navigation system. To make this happen, companies turn to data science consultancies for a ready-made solution.
This project relies on previous experience with Keras, Matplotlib, and Scikit-learn. You’ll also need an understanding of how Pandas, PIL, and image classification work.
Here’s how you put road signs recognition into practice:
- Download a dataset available at Kaggle
- Explore the dataset
- Build a CNN model (to sort the images into appropriate categories)
- Train and validate the model
- Test the model with the test dataset
- Save the trained model using the Keras model.save() function.
- Build a Graphical User Interface to display the model.
In the end, you’ll have a traffic signs classifier with 95% accuracy and boosted knowledge of CNN.
Forest fire prediction
If you feel like saving our mother Earth, nail this simple Python project. Building a forest fire and wildfire prediction system is a stellar contribution to nature and your portfolio.
Every summer, a wildfire or forest fire is a front-of-mind for thousands of people. The 2021 wildfire season took its course on all continents. Every instance of a burned forest resulted in severe damage to animal habitat and human property.
To predict the chaotic nature of wildfires, you can use k-means clustering and classification neural networks. These methods will help you pinpoint the main burned area hotspots.
The step-by-step process follows the pattern below:
- Finding which meteorological factors play a pivotal role in wildfires. The insights can then be used as inputs.
- Downloading the data and building a dataset using the inputs.
- Using the dataset to train a neural network for greater accuracy.
- Embedding the model into the interface.
This data science project also allows for more efficient resource allocation to cut the response time.
Climate impact on food supply
Another project that will get you hired in 2021 is climate change impact. Frequent climate change can disrupt food availability. In the next 30 years, food security will become insecure if little or no action is taken to address the issue. Climate irregularities will have varying degrees of impact on different places. And different human-made systems will have an unstable ability to slow the decline.
This data science implementation focuses on estimating the basic crop yields due to weather changes. To predict crop production, you’ll need Machine Learning algorithms. In particular, convolutional neural networks have proved effective for this project. Here, you’ll also consider agriculture factors, including temperature, rainfall, and soil type. The level of atmospheric concentrations of carbon dioxide is also often used for such projects.
Handwritten digit and character recognition project
This is yet another project to get your hands dirty into deep learning. Handwritten digit recognition refers to machines recognizing human handwritten digits. Since our handwriting is unique, digits are usually not perfect.
The MNIST dataset of handwritten digits is a popular choice to build this application. Once you preprocess data, Convolutional Neural Networks come on stage to train the model. In the end, you wrap a ready-to-go model into a nice graphical user interface.
The final in our list of data science projects with solutions is a visualization dashboard for mapping COVID-19. Maps break down main pandemic trends into digestible information. They also allow governments to inform citizens of breakpoints, medical facility availability, and others. Thus, John Hopkins University has provided real-time updates since the pandemic outbreak. This map features insights about the spread, total vaccines administered, and more.
Here, we’ll overview a beginner-level approach to visualize the outbreak of the disease using Python. To implement this project, data scientists enable statistical data visualization. The use of bar graphs, scatter plots, and bubble charts is highly recommended in this case. Also, choropleth maps will allow you to combine the spatial and statistical data.
How to do data science project: bottom line
Data science goes from data to value. And this is front and center in the job of a data scientist. As we see from the article, Machine Learning and Convolutional Neural Networks are the future of data science. Most of the featured projects relied heavily on the in-depth understanding of these concepts.
As a budding data scientist, you have to continuously hone up your skills. Strong acumen of both simple algorithms and advanced data techniques makes you worth your salt.
Need advice on how to implement data science in your project? Schedule a call with our team of skilled data scientists and engineers.