Land a six figure job with creative data science projects in your portfolio and open a new door of success in your list. Become a successful data scientist with experience working on real world projects related to frameworks and technologies like machine learning, artificial intelligence, data analysis, natural language processing, and more.
In today’s digital competitive world, your project portfolio represents your skills and problem solving abilities. In this article, let us know 7 of the best data science projects to help you land a 6 figure job in your career. Work on advanced frameworks and strengthen your portfolio with upskilling programs.
7 Data Science Projects For Beginners
Get to know some of the handpicked data science projects for beginners when you are in the early stages of your graduation years. Get data science projects with the source code below.
1. Fraud Detection Technique
With data science technologies you can create cutting edge fraud detection techniques. With the help of a fraud detection model, you can detect transactional fraud. You can also integrate machine learning algorithms to keep the data accurate and prevent any financial losses of the organization.
Frameworks & Tools Used
- Python and R language
- Pandas, NumPy Python Libraries
- Scikit Learn, XGBoost
- Tensorflow, Pytorch (Deep Learning based approaches)
- Clustering Techniques
- Autoencoders for anomaly detection
- Machine learning algorithm
Data Science Projects Source Code
Fraud Detection Projects GitHub
2. Sentiment Analysis Model
This data science project uses natural language processing to analyse customer feedback and reviews. This project can determine the sentiment polarity and provide actionable insights for product improvement.
This model catches sentiment based on polarity such as positive, negative, and neutral. This project can be used to examine client feedback and reviews.
Frameworks & Tools Used
- Python Programming Language
- SpaCy for text preprocessing
- Hugging Face Transformation for BERT-based sentiment analysis
- Tokenization and Lemmatization techniques used
- Pre trained models like BERT and RoBERTa
- Text Sentiment analysis, Audio analysis, and video analysis
GitHub Data Science Projects Source Code
3. Credit Card Approval Project
This data science project is used to predict whether a person requesting for a credit card is approved for loan and credit card. You will have to provide all your personal details to help the application assess your eligibility for the credit card. This data science project can help you strengthen and upskill your knowledge of data science.
Frameworks & Tools Used
- Python programming language
- Streamlit interface model
- AWS S3 model for storage
- Machine Learning and Deep Learning techniques
- Pre trained models like BERT.
GitHub Data Science Projects Source Code
4. Uber Fare Prediction
Develop an Uber fare prediction model with data science technologies. The dataset is collected from a large tech company which is then trained with advanced ml models. You have to build a model that can predict the total fare between a pickup and dropoff location. The complete model works on various machine-learning tools and algorithms.
Frameworks & Tools Used
- Python programming language
- Python libraries like NumPy, Pandas, Matplotlib, PyTorch, Scikit Learning
- Machine Learning algorithms like Linear Regression, Decision Tree algorithm
- Math library consisting of mean absolute error, Sum, average, etc.
Kaggle Data Science Projects Source Code
5. Time Series Prediction: Future Trends and Market Analysis
Develop data science models for future trends based on historical time series data and techniques using Prophet, ARIMA, LSTM and applications such as sales prediction or stock price forecasting for future trends and market analysis.
Frameworks & Tools Used
- Python programming languages
- Pandas, NumPy for data preparation
- Statsmodel (ARIMA)
- PyTorch and TensorFlow (LSTM)
- Prophet for forecasting
- Sliding windows for sequence modeling
GitHub Data Science Projects Source Code
Data Science Project Ideas For Final Year Students
Check some of the advanced data science projects for final year students below with source code.
1. Image Recognition System
Image recognition system is a technology used to process, analyze and interpret visual data to identify and classify objects, people, and patterns. This data science project forms the backbone of many data science applications such as object detection, medical imaging, facial recognition, autonomous vehicles, and more.
Machine learning models and deep learning algorithms help this model to analyse and interpret a large number of complex datasets. The working of an image recognition system includes data collection, preprocessing, feature extraction, model training and interference.
Frameworks & Tools Used
- Python programming language and Matplotlib
- Deep learning frameworks like PyTorch, TensorFlow, and Keras
- Libraries like OpenCV, Scikit Image, and Python image library
- Pre trained models like YOLO, SSD, Mask R-CNN and Convolutional Neural Networks (CNNs)
- Classification using ResNet, VGG, and Inception
GitHub Data Science Projects Source Code
2. Data Science Job Dashboard
Ever thought of using your own job portal website to find your dream data science job? This project can impress your recruiter and land you a six figured data science role in a popular tech company.
This website provides the number of data science positions available to apply and the skills required to be eligible for the role. This project also helps job seekers clear their confusion regarding the skills and tools they need to master to land a data science job.
Frameworks & Tools Used
- For data collection, use BeautifulSoup, Selenium, Pandas, APIs.
- For data storage AWS S3, Cloud Storage, Azure data lake storage in a cloud environment
- Data processing using NumPy, Pandas, Scikit Learn, etc
- Data visualization using Matplotlib and Seaborn
- Dashboard development using Flask, Streamlit, and Dash
- Use Google Cloud platforms for deployment
- Get real time updates and version control with Celery and Git
Kaggle Data Science Projects Source Code
GitHub for Data Science Job Dashboard
Master Data Science with PW Skills
Become a master in Data Science and Machine Learning with PW Skills Data Science with Generative AI Course. Build real world capstone projects based on the concepts covered in the machine learning, Python, and artificial intelligence modules.
Experts at PW Skills will guide you through industry oriented curriculum and prepare you for interview opportunities. Delve into instructor-led live sessions and leverage Dedicated doubt support with this Python Machine learning course and become job-ready.
Data Science Projects FAQs
Q1. What are key steps in data science projects?
Ans: Follow the steps below to implement a data science project below.
Define problems with objective
Data collection with APIs and Web scraping
Data Preprocessing to handle missing values and normalization
Exploratory data analysis (EDA) to uncover pattern, relationships, and trends
Select and train models using ML Models
Test the model
Integrate the model in the production system
Continuously monitor and improve the model.
Q2. How do you select the right machine learning model for a data science project?
Ans: Selecting a machine learning model depends on the nature of the problem such as classification use models like logistic regression, decision trees, and random forest. For regression use linear regression and for simpler models you can prefer to use smaller datasets. Use metrics like F1 score, and RMSE to compare different advanced models.
Q3. What tools and frameworks are used in data science projects?
Ans: Some of the major data science tools for data science projects are given below.
Python, R and SQL programming languages
Data manipulation using Pandas and NumPy
Visualization using Matplotlib and NumPy
Machine Learning models like TensorFlow, PyTorch, Scikit Learn
Big data tools like Hadoop and Spark
Cloud platforms like AWS, Azure, and Google Cloud platform
Q4. How do you handle missing and incomplete data in data science projects?
Ans: Use data preprocessing to find and eliminate any inconsistencies and irregularities in data. If a row or column is affected, then drop the particular row or column. However, to replace missing numeric values, use median, mean or mode. You can also estimate missing values using predictive modeling.
