Top 6 Data Science Projects To Get You Hired in 2023
Data Science helps solve real-world problems by properly using the relevant data. In this day and age, companies are using the information procured by data science professionals to understand customers’ behaviour, project sales, and estimate the future of the product in the market it is being launched. This is why a Data Science Certification from a reputed university with a curriculum designed to build industry-valued skills is preferred by companies that are looking for data science professionals.
In order to make sure that your resume stands out from the rest, it must contain some crisp and fresh data science projects. In this article, we have collected 6 data science projects that can help you build a strong profile.
1. Sentiment Analysis
- Language: R
- Dataset: janeaustenR
- Libraries (guides included): Pandas, Scikit-learn
What is Sentiment Analysis?
Sentiment analysis is a method that is used to analyze the opinion of targeted customers on a specific product or service offered by the company. It is used in companies to perform testing of the likeability of their products or services. The main goal of this is to figure out the WHYs behind not achieving the target sales or a product/service not being liked by the customer base.
Additionally, it helps in figuring out the reforms to make the brand’s offering more acceptable to its customer base.
Details about the project
This data science project would require you to make use of NLP, computational linguistics, text analysis, and biometrics to derive rich insights from the data provided. The basic task in sentiment analysis python is to classify the polarity of the opinion of the service/product being offered. The ranges of responses can be positive, negative, or at times included with multiple options like happy, sad, neutral, excited, and so on. This is a popular data science project idea that you can customize as per your need to make the project simpler or more complex.
2. Detection of the Parkinson’s Disease
- Language: Python
- Dataset/Package: UCI ML Parkinsons dataset
- 1.5 Color Detection with Python
What is Parkinson’s disease?
Parkinson’s disease is an old-age-related problem wherein the person loses control of his/her body parts. Its symptoms begin from tremors in hands, the rigidity of the body, to even shuffling of steps. This disease has 5 stages with stage 1 being comparatively non-interfering with daily activities and stage 5 being severely limited in terms of day-to-day activities. Most people suffer more due to late detection of the disease.
Details about the project
This is where data science steps in. You can use Python as the coding language in detecting Parkinson’s disease with XGBoost. XGBoost is an open-source software library that supports multiple libraries, including C++, R, Python, Java, Julia, etc. Using this data science project, early predictions of Parkinson’s disease can be made. The patients who are prone to getting the disease or show signs of getting affected by Parkinson’s disease in the future can be notified and this way an improved health service can be given to a patient.
3. Detection of Fake News
- Language: Python
- Dataset/Packages: news.csv
- Libraries (guides included): Scikit learn (TfidfVectorizer and PassiveAggressiveClassifier), Pandas and Numpy
What is Fake News?
Recognizing fake news is not easy. There are multiple platforms and channels where information is distributed – but is it correct or not? This is a grave concern as fake news can ignite miscommunication, which can cause huge damage worldwide. With the increasing amount of data being generated every day, the spread of fake news has also increased rapidly. How can we detect this fake news with the help of Data Science?
Details about the project
You can create a project using Python with this data science project idea. This model will have two classifiers – TfidfVectorizer and a PassiveAggressiveClassifier to segment the news as Real or Fake. You can make use of JupyterLab, a web-based user interface that enables you to work with documents and activities such as Jupyter notebooks, text editors, terminals, and custom components in an integrated, and extensible manner. A data set with dimensions of 7796*4 will prove to be highly supportive in this case.
4. Prediction of the Next Word
What is the Prediction of the Next Word?
We have all used Google Docs, WhatsApp, or the Google search bar at least once in our life. Have you noticed that while you are typing, you are given a few suggestions for the next word? This is what we mean by prediction of the next word. There are various algorithms built to help in predicting and suggesting what our next word may be.
Details about the project
A distinctive aspect of working on data science projects is that you get the freedom to create predictive type models. You must have noticed this while using Google Docs, WhatsApp, or even the Google search bar: all of these use the technique of predicting the next word by suggesting a new word after each new word you type.
This project is a great choice for someone who wants to transition to advanced-level projects. It requires the knowledge of NLP or deep learning to uncover the next word. The LTSM (Long short-term memory) model is the ideal choice for this as it makes use of deep learning with a network of artificial cells that manage the entire memory. This makes them better suited for predicting the next word.
5. Movie Recommender
- Language: R
- Dataset: MovieLens
- Packages: recommenderlab, ggplot2, data.table, reshape2
What is Movie Recommender?
In today’s extremely busy world, recommendation systems are becoming very popular. Have you ever used the streaming platform Netflix? The platform understands what kind of content you typically watch, and according to your liking, it recommends similar movies or TV Series that you may enjoy.
A movie recommender helps an individual find more content that they may enjoy. It curates a specific list that can is unique to each person based on their liking. These recommendations may be based on browser history, based on what other people with similar demographics/traits are watching, and more.
Details about the project
This is one of the data science projects that will surely grab a lot of eyeballs! After all, who doesn’t like to be recommended movies or series on YoutTube or Netflix, that too in the area of your interest! To execute this project you can collect inputs from viewers who saw a movie first and classify their responses.
You can make use of the R programming language to create this movie recommender system. The right choice of dataset for this project will be MovieLens. It covers 58k movies and you can also avail packages like reshape2, ggplot2, and data.table.
6. Customer Segmentation
- Language: R
What is Customer Segmentation?
The process of dividing a company’s customers into different groups (each group reflects similarity), is known as customer segmentation. The goal is to decide how we can relate the customers in each segment to maximize the value of the customers to the business.
Typically, customers are divided into segments based on the following factors:
Psychographic, demographic, geographic segmentation, and behavioral. However, there are other ways to divide the customer group as well. Companies perform customer segmentation because they realize that each customer group may have different needs. To satisfy the various requirements of different groups, a company must cater to their needs differently.
Details about the project
Businesses are always in the process of devising methods to segment their customers. The segmentation process ensures that the business can create consumer-specific strategies and create a product or service that suits their needs. This is a MUST-DO activity before running any online marketing campaign.
Customer Segmentation is a popular application of unsupervised learning. In this, clusters are used by the company to define and place its customers in different groups which are categorized on the basis of region, gender, age, preferences, and so on. This project is also useful to identify the inputs of annual incomes and spending trends of the customers to create a strategy for that segment.
Closing Thoughts
No data science project is difficult if you have adequate knowledge about the right tools and techniques. In fact, the practical application of any technology is best tested by working on a number of projects. It gives you the right amount of exposure and increases your problem-solving skills.
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