Data Science Projects to Enhance your Resume
In Data Science, relevant data is used to solve real-world problems. Companies use this information to understand consumer behavior, project sales, automate internal processes, and estimate a product's future in the market where it is being launched.
Data science professionals from reputable universities with a curriculum that builds industry-valued skills are preferred by companies.
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In order to make your resume stand out, it must contain some interesting data science projects. These data science projects can boost your resume:
Sentiment Analysis: Sentiment analysis is a method of examining the opinions of targeted customers about a specific product or service offered by an organization. Likeability tests are conducted in companies to determine whether their products or services are liked. A customer satisfaction survey helps understand why target sales are not being achieved or why a consumer base does not like a product/service.
Detecting Parkinson's Disease: Parkinson's disease is an old-age illness in which people lose control of their body parts. The symptoms begin from hand tremors, rigidity in the body, and shuffling of steps. This disease grows in five stages. Stage one does not interfere a lot with daily activities, whereas stage five severely limits an individual's day-to-day activities. Most people suffer because the disease is not diagnosed in time. The problem of late detection can be tackled with data science.
Detecting Fake News: Fake news can be challenging to identify. The news is delivered by multiple platforms, but determining its accuracy is difficult, and the spread of fake news has serious implications. By using this idea, one can conceptualize a Python-based project. As part of the data science project, one can use Python to write a program that will be used to determine whether the news is reliable.
Recommendations for movies: Many people will be enthralled by this project. We all prefer receiving recommendations for movies or series to binge-watch. To work on this project, the inputs of the first-time viewers will be gathered, and their responses will then be analyzed. This movie-recommender system can be built using the R programming language.
Next word prediction: In data science, you have the freedom to create predictive models. We also observe this when using Google Docs, Mail, WhatsApp, or even the Google search bar; they predict the next word or phrase by customizing the suggestions after every keystroke. An advanced-level project such as this could be an interesting idea for someone who wants to try new things.
Credit Card Fraud Detection: In this project to detect credit card fraud, one has to use the R programming language along with algorithms like Decision Trees, Artificial Neural, and others. You will fit models in order to visualize insights and deriver curves from them.
Image Caption Generator: An image is just a collection of numbers that each indicate the color value per pixel for a computer. As a result, getting a computer to understand an image and provide an English description is a challenging task. For this project, you'll need Python and the Keras framework.
Census Income Data: Census income data is a classic machine learning challenge. You can tackle problems like disease detection, fraud detection, and other unbalanced situations with the use of a census income data collection. In this project, you will primarily focus on forecasting the population's income class. Adding Census Income Data as a project to your CV will be quite beneficial.
Speech Emotion Recognition: Speech Emotion Recognition is a data science project that performs speech recognition with the help of Librosa. It's a method for detecting and analyzing human emotions based on the tone of one's voice. Despite the fact that tone and speech are subjective, they are vital in identifying emotions. One can use python language for this project and the dataset that will facilitate it is RAVDESS.
Customer Segmentation: Businesses are constantly looking for and developing new ways to segment their customers. Segmentation is essential for developing customer-specific strategies that result in a product or service that meets their needs. This is a helpful activity before starting online marketing campaigns.
Customer segmentation is a popular application of unsupervised learning. Clusters are used by the organization to categorize and organize its clients based on area, gender, age, preferences, and other relevant factors. This project is also critical in determining the inputs of the consumers' annual incomes and spending habits in order to develop a plan.
The field of data science has a lot of potential for growth, and it's still growing, which means that workers with data science skills will be in high demand. Including projects on your resume is a great way to make it stand out. Today's data professionals recognize that if they want to succeed, they must improve their skills in evaluating large volumes of data, programming, and data mining. They'll be able to optimize their earnings at every stage of their careers in this manner.
Here’s a Sample Resume for Reference:
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