Data analytics is transforming industries by helping professionals make informed decisions using data. A data analytics seminar is a great way for students to explore this field, learn essential tools like Python, SQL, and Excel, and apply data-driven insights to real-world problems.
Whether you’re interested in AI, business intelligence, or sustainability, we have listed the top Data Analytics seminar topics that will help you develop key analytical skills.
What is a data analytics seminar?
A data analyst seminar is an event where experts teach people how to analyze data. Students can learn how to collect, organize, and study data to find useful patterns. It also shows how businesses use data to make better decisions.
Students learn about tools like Excel, SQL, and Python from a data analytics seminar. Some seminars include hands-on activities or real-world examples for a better understanding of different data analytics concepts. These events help beginners and professionals improve their skills.
Many people confuse data science and business analytics. Knowing the core distinctions between data science and business analytics is a necessity for anyone in the data analytics sphere.
Here are the handpicked data analytics seminar topics at a glance:
Best Seminar Topics for Data Analytics
1. AI-powered Data Analytics
Technicality level: Intermediate level
Description: This seminar explores how artificial intelligence (AI) improves data analysis. AI helps process large amounts of data quickly and finds patterns that humans might miss.
Machine learning models make predictions based on past data, helping businesses make better decisions. Students will learn how AI-powered tools simplify data analytics, automate tasks, and provide accurate insights.
What to cover in this seminar topic:
- Basics of AI in data analytics
- How machine learning improves data insights
- Popular AI-powered analytics tools
- Real-world applications in business and healthcare
- Hands-on demo using AI models
Learning resources/project references:
2. Data Storytelling and Visualization
Technicality level: Beginner
Description: Data storytelling turns numbers into meaningful insights. It combines visuals and narratives to help people understand complex data easily.
This seminar teaches you how to create clear and engaging charts, graphs, and dashboards. Students will learn how to turn raw data into a story that informs and influences decisions.
What to cover in this seminar topic:
- Basics of data visualization (charts, graphs, dashboards)
- Importance of storytelling in data analysis
- Choosing the right visualization for different datasets
- Common mistakes and best practices in visualization
- Introduction to tools like Tableau, Power BI, and Matplotlib
Learning resources/project references:
3. Quantum computing and Data analytics
Technicality Level: Intermediate
Description: Quantum computing processes data in a way that is much faster than regular computers. It uses quantum bits (qubits) that can perform multiple calculations at once.
In data analytics, this means solving complex problems faster, analyzing huge datasets efficiently, and improving machine learning models. This seminar will introduce you to how quantum computing can change data analytics and where it is being used today.
What to cover in this seminar topic:
- Basics of quantum computing (qubits, superposition, entanglement)
- Differences between classical and quantum computing in data analytics
- Real-world applications in data science and big data
- Challenges and future potential
Learning resources/project references:
4. Reducing manufacturing failures
Technicality Level: Beginner
Description: This seminar explains to students how factories can reduce product defects and machine breakdowns. It covers simple ways to track errors, find their causes, and fix them using data.
Students will learn how sensors, reports, and basic data tools help improve quality and reduce waste in manufacturing.
What to Cover in This Seminar:
- Common causes of manufacturing failures
- Importance of collecting and analyzing production data
- Role of sensors and real-time monitoring in quality control
- How predictive analytics helps prevent machine breakdowns
- Simple data visualization techniques for error tracking
Learning Resources/Project References:
5. Market Basket Analysis
Technicality level: Advanced
Description: Market Basket Analysis utilizes complex algorithms to analyze extensive transactional data, uncovering intricate relationships between products.
This analysis enables businesses to predict future purchasing trends, personalize marketing strategies, and enhance cross-selling opportunities. Students can learn about it in depth from this seminar.
What to cover in this seminar topic:
- In-depth exploration of advanced algorithms beyond Apriori, such as FP-Growth
- Techniques for handling large-scale data and improving computational efficiency
- Integration of Market Basket Analysis with machine learning models for predictive analytics
- Discussion on challenges like dealing with sparse data and dynamic inventory
- Exploration of high-utility itemset mining
Learning resources/project references for this seminar:
6. City Employee Salary Data Analysis
Technicality level: Beginner Level
Description: This seminar explores how to analyze city employee salary data using data analytics techniques. Students will learn how to clean, visualize, and interpret salary trends across different job roles and departments.
It introduces basic concepts like data collection, preprocessing, and simple statistical analysis to find insights.
What to cover in this seminar topic:
- Understanding city employee salary datasets
- Data cleaning and preprocessing techniques
- Exploring salary trends using visualizations
- Identifying salary disparities across job roles
- Basic statistical methods for salary analysis
Learning resources/project references:
7. Data analysis and visualisation using Apache Spark and Zeppelin
Technicality level: Beginner/Intermediate
Description: Students will learn how to analyze and visualize big data using Apache Spark and Zeppelin. Apache Spark processes large datasets quickly, while Zeppelin provides an interactive environment to write and run code.
This seminar covers how to clean data, perform analysis, and create visual reports. By the end, students will understand how to use these tools to make data-driven decisions.
What to cover in this seminar topic:
- Basics of Apache Spark and Zeppelin
- Setting up Spark with Zeppelin
- Data preprocessing and cleaning
- Running queries and transformations
- Creating interactive visualizations
Learning resources/project references:
8. Analysing Co2 Emissions
Technicality level: Advanced
Description: This seminar explores machine learning techniques to predict future CO₂ emissions. Participants will build predictive models using regression analysis and neural networks.
They will work with large-scale climate data and apply AI-driven approaches to identify key emission drivers. The goal is to understand how technology can help in making data-driven environmental policies.
What to cover in this seminar topic:
- Collecting and processing real-time CO₂ data
- Feature engineering for predictive modeling
- Machine learning models (Linear Regression, Random Forest, Neural Networks)
- Evaluating model accuracy and optimization techniques
- Applying AI in sustainability and policy-making
Learning resources/project references:
9. Data Analysis using clustering
Technicality level: Advanced
Description: Clustering involves grouping data points into subsets where members share similar properties. Advanced clustering techniques address complex datasets with varying densities and noise.
Methods like DBSCAN (Density-Based Spatial Clustering of Applications with Noise) can identify clusters of arbitrary shape and handle outliers effectively.
What to cover in this seminar topic:
- In-depth study of advanced clustering algorithms such as DBSCAN and Gaussian Mixture Models
- Handling high-dimensional data and the curse of dimensionality in clustering
- Techniques for clustering large-scale datasets
- Incorporating domain knowledge into clustering processes
- Case studies showcasing the application of advanced clustering in various industries
Learning resources/project references for this seminar:
10. Data Analytics in Smart Cities
Technicality Level: Advanced
Description: Smart cities use data to improve daily life. Sensors, cameras, and devices collect information about traffic, pollution, and energy use. Data analysts study this information to find patterns and suggest better ways to manage resources.
This seminar explains how data analytics helps in solving urban challenges like reducing traffic jams, saving energy, and making cities safer.
What to Cover in This Seminar Topic:
- Basics of smart cities and IoT devices
- How data is collected from urban systems
- Analyzing city data to improve transportation and energy use
- Case studies of data-driven smart city projects
- Tools and techniques used in smart city analytics
Learning Resources/Project References:
11. Movies Review Scraping and Analysis
Technicality level: Beginner/Intermediate
Description: This seminar covers how to collect and analyze movie reviews from websites like IMDb or Rotten Tomatoes. Students will learn to scrape review data, clean it, and find insights using Python.
They will also explore sentiment analysis to understand audience opinions. The session includes practical coding examples and data visualization techniques.
What to cover in this seminar topic:
- Web scraping basics with Python (BeautifulSoup, Scrapy)
- Extracting movie reviews from websites
- Cleaning and processing text data
- Sentiment analysis using Natural Language Processing (NLP)
- Visualizing trends in movie reviews
Learning resources/project references:
12. Zomato Data Analysis Using Python
Technicality level: Intermediate
Description: This seminar explores how to analyze Zomato restaurant data using Python. Attendees will learn how to clean, visualize, and interpret data to uncover trends in restaurant ratings, cuisines, and pricing. Python libraries like Pandas, Matplotlib, and Seaborn will be used for data manipulation and visualization.
This seminar will also introduce basic machine learning techniques to predict ratings and customer preferences.
What to cover in this seminar topic:
- Introduction to Zomato dataset
- Data cleaning and preprocessing using Pandas
- Data visualization with Matplotlib and Seaborn
- Exploratory data analysis (EDA) on restaurant ratings and cuisine trends
- Building a simple predictive model using Scikit-learn
Learning resources/project references:
13. ESG Analytics: Measuring Sustainability in Business
Technicality Level: Intermediate
Description: ESG (Environmental, Social, and Governance) analytics helps businesses track their sustainability performance. Companies use data to measure their carbon footprint, social impact, and corporate ethics.
This seminar will explore how organizations collect, analyze, and report ESG data to meet regulations and attract investors. Participants will learn about key ESG metrics, data sources, and visualization techniques. This seminar will teach you about ESG analytics and how it helps businesses improve transparency and decision-making.
What to Cover in This Seminar Topic:
- Introduction to ESG and its importance
- Data sources for ESG analytics
- Measuring environmental impact (carbon emissions, energy use)
- Assessing social responsibility (labor rights, diversity, community impact)
- Governance factors (ethics, leadership, compliance)
- Tools and techniques for ESG data analysis
- ESG reporting frameworks (GRI, SASB, TCFD)
Learning Resources/Project References:
14. Sports Analytics: Data-Driven Performance Optimisation
Technicality Level: Intermediate
Description: This seminar teaches students about how data helps improve athlete performance and team strategies. Coaches and analysts use data from games, player movements, and fitness levels to make better decisions.
With machine learning and statistical models, they predict injuries, analyze opponents, and enhance training plans. Sports organizations use these insights to gain a competitive edge.
What to Cover in This Seminar Topic:
- Basics of sports data collection
- Key metrics in player and team performance
- Role of machine learning in sports predictions
- Injury prevention using data analytics
- Case studies of teams using analytics successfully
Learning Resources/Project References:
15. Data Analytics in Business Making
Technicality Level: Beginner
Description: Businesses use data analytics to make smart decisions. It helps companies understand customers, predict trends, and improve products.
This seminar explains how data is collected, analyzed, and used to solve business problems. You will see real-world examples of companies using data to grow.
What to cover in this seminar topic:
- Importance of data in business
- Basics of data collection and analysis
- Common business analytics tools (Excel, SQL, Power BI)
- Case studies of data-driven decisions
Learning resources/project references:
⭐ Bonus: Other seminar and research topics for data analytics
1. Building Job Portal using Twitter Data
A job portal helps people find jobs. This seminar will show how to collect and analyze job-related tweets to create a simple job portal.
Students will learn how to find job postings on Twitter using keywords and hashtags. They will clean and organize the data, then build a system to match job seekers with job openings.
2. Event Data Analysis
Events like concerts, sports matches, and conferences create lots of data. This seminar will teach how to collect and study event data to find patterns.
Students will learn how to analyze ticket sales, attendance, and social media reactions. They will discover how data can help improve future events.
3. Building Music Recommendation Engine
A music recommendation engine suggests songs based on listening history. This seminar will explore how data helps predict what songs people might like.
Students will learn how streaming platforms use data to recommend songs. They will work with user preferences, song features, and machine learning to build a basic recommendation system.
4. Housing Price Analysis and Predictions
House prices change based on location, size, and market trends. This seminar will show how to analyze and predict housing prices using data.
Students will learn how to collect real estate data, find price trends, and build models that estimate future house prices. They will understand how data helps buyers and sellers make smart choices.
5. Customer Churn Analysis
Businesses lose customers over time. This seminar will explore how data helps predict when and why customers leave.
Students will learn to analyze customer behavior and find patterns in shopping habits. They will build models that help businesses keep customers happy and loyal.
6. HR Analytics to Track Employee Performance
Companies use data to measure employee performance. This seminar will explain how HR teams use analytics to improve workplace productivity.
Students will learn how to analyze attendance, work quality, and job satisfaction. They will explore ways data can help employees grow and succeed.
7. Google Search Analysis
Every Google search creates data. This seminar will show how analyzing search trends can reveal popular topics and user interests.
Students will learn how to collect and study research data. They will explore how businesses and researchers use search trends to make decisions.
8. Anomaly Detection in Time Series Data
Anomalies are unexpected changes in data. This seminar will focus on how to find unusual patterns in time-based data.
Students will learn how to detect fraud, sensor failures, and stock market crashes. They will use simple tools to spot unusual trends in real-world data.
9. Food Price Forecasting
Food prices go up and down based on supply, demand, and weather. This seminar will teach how to predict food price changes using data.
Students will collect past price data, find patterns, and build models to forecast future food costs. They will understand how data helps farmers, stores, and consumers plan better.
10. Word Frequency in Classic Novels
Writers use certain words more than others. This seminar will explore how to count and compare word usage in famous books.
Students will analyze novels to find the most common words and themes. They will learn how data can help in literature studies.
11. Modelling Car Insurance Claim Outcomes
Car insurance companies use data to predict claims. This seminar will explain how data helps them decide costs and risks.
Students will analyze past claims to find patterns. They will build models that estimate the chances of accidents and insurance payouts.
12. Analyse International Debt Statistics
Countries borrow money to grow their economies. This seminar will explore how to analyze debt statistics from different nations.
Students will study how much countries owe, to whom, and why. They will learn how data helps governments and organizations make financial decisions.
13. Monitoring Financial Fraud Detection Model
Banks use data to catch fraud. This seminar will show how data analytics helps detect fake transactions.
Students will learn how fraud happens and how data models identify suspicious activities. They will explore real-world fraud cases and prevention techniques.
14. Data Ethics and Privacy
Data collection raises privacy concerns. This seminar will explain the importance of handling data responsibly.
Students will learn about data security, personal privacy, and ethical data use. They will explore real cases where data misuse caused problems.
15. Augmented Analytics and Automation
Augmented analytics uses AI to speed up data analysis. This seminar will show how automation helps businesses make quick decisions.
Students will explore AI-driven data tools and learn how they save time in this seminar. They will understand how automation makes data analysis easier and more efficient.
How 10Pie helps you in preparing for your next data analytics seminar presentation
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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).