Attending a data science seminar helps students gain valuable knowledge and hands-on experience in one of the fastest-growing fields, Data Science.
Wondering where to start?
We have listed some of the top data science seminar topics for final-year students. These seminars introduce new trends, tools, and real-world applications, making learning more engaging.
Data science seminars can help students improve their resumes, develop essential technical skills, and explore career opportunities.
Data science seminar topics at a glance:
What is a Data Science seminar?
A data science seminar is an educational event where experts teach students about data science concepts, tools, and real-world applications. These seminars cover topics like machine learning, data analysis, artificial intelligence, and big data. Trainers explain how businesses and researchers use data to make better decisions.
Some seminars include hands-on workshops where students can practice coding and data visualization. Attending a data science seminar helps students learn new skills, explore career opportunities, and stay updated on the latest trends in technology.
Here are the Best Seminar Topics for Data Science
1. Assessment of Exploratory Data Analysis
Technicality Level: Beginner
Description: Exploratory Data Analysis (EDA) helps understand datasets before applying models. It finds patterns, missing values, and outliers using visualizations and statistics.
This seminar teaches students how to clean data, detect errors, and summarize key insights. Learning EDA improves decision-making and ensures better predictions in machine learning.
What to Cover in This Seminar Topic:
- Importance of EDA in data science
- Common techniques (summary statistics, visualizations)
- Handling missing values and outliers
- Identifying patterns and correlations
- Tools like Pandas, Matplotlib, and Seaborn
Learning Resources/Project References:
2. Big Data Analytics: Telecommunication Approach
Technicality level: Intermediate
Description: Big data analytics helps telecom companies process and analyze massive amounts of customer data, network usage, and call records. It improves network efficiency, detects fraud, and enhances customer experience.
Telecom providers use machine learning and AI to predict service issues, optimize bandwidth, and personalize offers. This seminar will explore how big data tools handle real-time and historical telecom data to drive better decisions.
What to cover in this seminar topic:
- Introduction to big data in telecom
- Data sources: call records, network logs, customer data
- Tools: Hadoop, Spark, and machine learning models
- Fraud detection and customer churn prediction
- Real-time data processing in telecom networks
Learning resources/project references:
3. Credit Card Fraud Detection
Technicality level: Intermediate
Description: Fraudulent credit card transactions cause financial losses and security risks. This seminar explores how data science detects fraud using machine learning. Banks analyze transaction patterns to identify suspicious activity.
Algorithms learn from past fraud cases to recognize new threats. This seminar explains how fraud detection models work, their challenges, and how to improve accuracy.
What to cover in this seminar topic:
- Introduction to credit card fraud and its impact
- Basics of fraud detection techniques
- Machine learning models for fraud detection
- Challenges in detecting fraud
- Best practices for building fraud detection systems
Learning resources/project references:
4. Online Detection Published Fake News
Technicality level: Beginner
Description: This seminar explores how to identify fake news online using data science. Participants will learn simple methods to spot false information on the internet.
What to cover in this seminar topic:
- Understanding what fake news is
- Recognizing the impact of fake news on society
- Learning basic techniques to detect fake news
- Exploring real-world examples of fake news detection
Learning resources/project references for this seminar:
5. Analysis of customer segmentation
Technicality Level: Beginner
Description: Businesses group customers based on their behavior, preferences, and demographics to improve marketing and sales.
This process, called customer segmentation, helps companies target the right audience with personalized offers. Data scientists use clustering techniques like K-Means or DBSCAN to find patterns in customer data.
In this seminar, students will learn how to segment customers using real-world datasets.
What to Cover in This Seminar Topic:
- Introduction to customer segmentation
- Importance of segmentation in marketing and business
- Common segmentation methods (demographic, behavioral, etc.)
- Basics of clustering algorithms (K-Means, DBSCAN)
- Hands-on data analysis with Python
Learning Resources/Project References:
6. Cloud computing for data science
Technicality level: Advanced
Description: Cloud computing helps data scientists store, process, and analyze large datasets without needing powerful local computers. It provides remote servers, storage, and tools that make data analysis faster and more efficient.
This seminar explains how cloud platforms like AWS, Google Cloud, and Azure support data science workflows. You will learn how to use cloud-based tools for machine learning, big data processing, and model deployment.
What to cover in this seminar topic:
- Basics of cloud computing and its benefits for data science
- Popular cloud platforms (AWS, Google Cloud, Azure)
- Cloud-based data storage and databases
- Running machine learning models on the cloud
- Deploying data science applications using cloud services
Learning resources/project references:
7. Sentiment analysis on Twitter data
Technicality level: Beginner
Description: This seminar explores how to analyze tweets to understand people’s emotions about a topic. It introduces sentiment analysis, a technique that uses Natural Language Processing (NLP) and machine learning to classify tweets as positive, negative, or neutral.
Students will learn how to clean Twitter data, use pre-trained models, and visualize sentiment trends.
What to cover in this seminar topic:
- Basics of sentiment analysis and NLP
- Collecting Twitter data using APIs
- Preprocessing tweets (removing noise, tokenization)
- Using machine learning models for sentiment classification
- Visualizing sentiment trends with graphs
Learning resources/project references:
8. Handwritten digit recognition
Technicality level: Beginner
Description: Handwritten digit recognition is a machine-learning task where computers learn to read and identify numbers written by hand.
It uses image processing and deep learning techniques to recognize digits from scanned documents or photos. This technology powers applications like automatic form reading and postal code recognition.
What to cover in this seminar topic:
- Basics of image processing
- Introduction to neural networks
- Understanding the MNIST dataset
- Building a simple digit recognition model using Python
- Training and testing the accuracy of models
Learning resources/project references:
9. Image caption generator
Technicality Level: Intermediate
Description: An image caption generator is a machine-learning model that automatically creates short image descriptions. It uses deep learning techniques like Convolutional Neural Networks (CNNs) for image feature extraction and Recurrent Neural Networks (RNNs) or Transformers for text generation.
This seminar will explain how AI can understand images and generate relevant captions, like humans describing pictures.
What to Cover in This Seminar Topic:
- Introduction to image captioning
- Role of CNNs and RNNs in caption generation
- Using pre-trained models like VGG16 and LSTM
- Implementation using TensorFlow/Keras
- Datasets like MS COCO for training
- Real-world applications and challenges
Learning Resources/Project References:
10. Personalised healthcare recommendation system
Technicality Level: Intermediate
Description: This seminar explores how machine learning helps doctors and patients get personalized health advice.
A personalized healthcare recommendation system analyzes medical history, lifestyle, and symptoms to suggest treatments, diet plans, or medications. It uses AI to provide tailored recommendations, making healthcare more effective.
What to Cover in This Seminar Topic:
- Basics of recommendation systems
- Machine learning models for healthcare recommendations
- Data collection and preprocessing
- Ethics and privacy concerns in medical AI
- Real-world applications and case studies
Learning Resources/Project References:
11. Airbnb price prediction
Technicality level: Intermediate
Description: This seminar explores how to predict Airbnb rental prices using data science. Participants will learn how to collect and analyze property data, identify key pricing factors, and build machine-learning models.
Students can estimate rental prices based on location, amenities, and demand by understanding these concepts.
What to cover in this seminar topic:
- Introduction to Airbnb dataset
- Data cleaning and preprocessing
- Feature selection and engineering
- Applying machine learning models (Linear Regression, Random Forest, etc.)
- Evaluating model performance
- Visualizing insights from data
Learning resources/project references:
12. Resume screening system
Technicality level: Advanced
Description: This seminar explores how companies use machine learning to filter job applications. A resume screening system scans resumes, picks relevant skills, and ranks candidates based on job requirements.
It saves recruiters time and ensures fair selection. Attendees will learn how to build an automated system using Natural Language Processing (NLP) and classification models.
What to cover in this seminar topic:
- Importance of automated resume screening
- How NLP extracts skills, experience, and education
- Training machine learning models for classification
- Challenges like bias in AI and overcoming them
- Hands-on implementation with Python
Learning resources/project references:
13. Data mining in search engine analytics
Technicality level: Advanced
Description:
Search engines process massive amounts of data to show relevant results. Data mining helps analyze search queries, user behavior, and ranking factors. This seminar explores how search engines use algorithms to improve accuracy and personalization.
Students will learn about techniques like web scraping, keyword analysis, and trend prediction.
What to cover in this seminar topic:
- Basics of search engines and data mining
- Web crawling and indexing
- User behavior analysis using data mining
- Keyword trend prediction and ranking factors
- Sentiment analysis in search queries
Learning resources/project references:
14. Generative AI- Gen AI
Technicality level: Beginner
Description: Generative AI (Gen AI) helps computers create new content like text, images, music, or videos. It learns from existing data and generates original outputs based on patterns.
This technology powers chatbots, AI art, and even deepfake videos. Companies use it for product design, marketing, and creative work. The seminar will explain how Gen AI works and its impact on different industries.
What to cover in this seminar topic:
- Basics of AI and machine learning
- How generative models work (e.g., GPT, DALL·E)
- Applications in real-world scenarios
- Ethical concerns and limitations
Learning resources/project references:
15. Time series forecasting on weather data
Technicality Level: Intermediate
Description: This seminar explores how to predict future weather conditions using past data. Time series forecasting helps analyze temperature, rainfall, and wind speed patterns.
It uses mathematical models and machine learning to make accurate weather predictions. Understanding these techniques can improve climate research and disaster preparedness.
What to Cover in This Seminar Topic:
- Basics of time series forecasting
- Weather data collection and preprocessing
- Common forecasting models (ARIMA, LSTM, Prophet)
- Model evaluation techniques
- Real-world applications in weather prediction
Learning Resources/Project References:
- Weather Forecasting: Using Time Series Analysis Under Different Stations Of The UK
- Deep Neural Network for Weather Time Series Forecasting
Thinking about a data science career but not sure where to start? We’ve all been there.
Let’s walk through the basics of launching your Data Science career.
⭐ Bonus: Other seminar and research topics for Data Science
1. Understanding Bias in Machine Learning Models
Bias in machine learning happens when a model makes unfair decisions based on incorrect patterns in data. It can lead to discrimination and inaccurate predictions.
This seminar covers different types of bias, such as selection bias and confirmation bias. Students will learn how biased data affects AI decisions and ways to reduce bias in machine learning models.
2. The Intersection of Data Science and Cybersecurity
Data science helps detect cyber threats, while cybersecurity protects data from attacks. Together, they make systems safer.
This seminar explores how machine learning detects fraud, stops cyberattacks, and strengthens online security. Students will see real-world examples of AI-powered cybersecurity tools.
3. Data Science Skill Gap Analysis
A skill gap happens when companies need certain skills but workers don’t have them. In data science, this gap slows down progress.
This seminar explains what skills data scientists need and why some skills are missing. Students will explore ways to bridge the gap through training and education.
4. Data Quality Management
Bad data leads to bad decisions. Data quality management ensures that data is clean, complete, and useful.
In this seminar, students will learn how to check data accuracy, remove errors, and improve data quality. They will also understand why good data is important for AI and business.
5. Analysis of Forest Fire Prediction
Forest fires destroy land, wildlife, and homes. Data science can help predict where fires may happen.
This seminar teaches students how data scientists use temperature, wind, and humidity data to predict fires. They will also learn about AI tools that help firefighters react faster.
6. Analysis of Gender Detection and Age Prediction
AI can estimate a person’s age and gender using images, voice, and text. This technology is used in security, marketing, and social media.
Students will explore how machine learning analyzes faces and voices to make predictions. They will also learn about challenges such as fairness and accuracy.
7. Smart Sustainable Mobility and Communities
Smart cities use data to improve transportation and make life better. Sustainable mobility means using eco-friendly ways to travel.
This seminar shows students how data science helps in smart traffic management, electric vehicles, and pollution control. They will also explore smart city projects around the world.
8. Sustainable Development
Sustainable development means using resources wisely to protect the future. Data science helps track climate change, pollution, and resource use.
Students will learn how AI predicts environmental changes and helps in making green policies. They will also see how data science supports clean energy projects.
9. Technology Behind the AI Revolution
AI is changing the world by making machines smarter. But what makes AI work?
This seminar explains the key technologies behind AI, such as deep learning, neural networks, and big data. Students will understand how these technologies power chatbots, self-driving cars, and more.
10. High-Performance Computing: Big is Beautiful
High-performance computing (HPC) solves complex problems by using powerful computers. It is used in weather prediction, AI, and space exploration.
Students will discover how supercomputers process massive amounts of data quickly. They will also see why HPC is important for science and technology.
11. Data Engineering: The Unglamorous Reality Behind Data Science
Data engineers prepare raw data for analysis. Without them, data scientists cannot build AI models.
This seminar explains the hidden but important work of data engineers. Students will learn how to clean, store, and organize data to make it useful.
12. Velocity 360: A Tour on the 3rd Dimension of Big Data
Big data is not just large; it moves fast. Velocity is about how quickly data is created and processed.
Students will explore how real-time data powers live tracking, stock markets, and smart assistants. They will also learn why speed matters in data science.
13. Digital Humanities: Art is Not Only for Humans Anymore
AI is creating art, music, and literature. It is changing the way we think about creativity.
This seminar explores how AI analyzes history, writes poems, and creates paintings. Students will discuss whether AI can truly be creative.
14. Federated Learning: The Future of Edge Intelligence is Now!
Federated learning trains AI models without sharing data. It keeps data private while improving AI performance.
Students will learn how federated learning works in phones, hospitals, and businesses. They will also see why it is important for privacy and security.
15. Explainability and Governance of Artificial Intelligence
AI makes many decisions, but can we trust them? Explainability means understanding how AI makes choices.
This seminar teaches students how AI transparency improves fairness and trust. They will also explore rules and laws that make AI responsible.
How 10Pie helps you in preparing for your next Data Science seminar presentation
10Pie helps you prepare for your data science seminar presentation with the right resources. You can learn key topics and understand important terms with simple explanations.
Our technology glossary gives clear definitions of complex ideas. At 10Pie, you will find multiple career guides, including the Data Science Career Guide, which helps you stay updated on industry trends.
You will find useful insights and real-world examples from these guides that will help you do better and create well-informed seminar presentations.
Somrita Shyam is a content writer with 4.5+ years of experience writing blogs, articles, web content, and landing pages in multiple domains. She holds a master’s degree in Computer Application (MCA) and is a Gold Award winner at Vidyasagar University. Her knowledge of the tech industry and experience in crafting creative content helps her write simple and easy-to-understand tech pieces for readers of all ages. Her interest in content writing began after helping PhD scholars in submitting their assignments. Later in 2019, she started working as a freelance content writer at Write Turn Services, and has worked with numerous clients, before joining Experlu (An UK based accounting firm) in 2022 and working as a full-time content writer in GigDe (2022-2023).