13 Popular Big Data Project Ideas & Topics for Beginners

13 Popular Big Data Project Ideas & Topics for Beginners

Big Data is an interesting topic. This allows you to see patterns and find results that you might not have otherwise. This skill is highly sought after and can be quickly mastered to advance your career. If you’re a beginner in big data, it’s a good idea to start working on big data projects.

Theory alone will not be applicable in real-world work environments. This article will explore some great big data project ideas that beginners can use to put their knowledge to the test. This article will provide top big data project ideas for beginners in order to gain hands-on experience with big data.

But, just knowing the theory behind big data won’t make you a great deal. It’s important to put what you have learned into practice.

Big data projects are a great way to practice your big-data skills. These projects are a great way for you to practice your skills. These projects are great for your resume.

What are the problems you may face in doing Big Data Project?

Many industries use big data. There are many big data topics that you can work on. A big data analyst must face many challenges while working on these projects, in addition to the variety of ideas.

These are the ones:

Limited Monitoring Solutions

Monitoring real-time environments can present problems because there aren’t many options available for this purpose.

This is why it’s important to be familiar with the technologies before you can start working on a project, you will need to know what big data analysis is.

Timing issues

Data virtualization is a common issue that causes data analysis problems. These latency issues are common because most of these tools are very high-end.

Virtualization of data can cause timing problems due to latency in output generation.

High-level scripting is a requirement

You might run into tools and problems that require more advanced scripting when working on big data analysis projects.

You should seek out other people who have the same problem to help you learn more.

Data Privacy & Security

You must ensure that your data is kept private and secure while you work with the data. Data leakage can cause havoc in your project and your work. You should also remember that data can be leaked by users.

Inaccessibility of Tools

End-to-end testing can’t be done with one tool. It is important to determine which tools are needed to complete a particular project. If you don’t have the correct tool for a particular device, it can waste much time and cause a many of frustration. This is why it is important to have all the necessary tools before you begin the project.

Too Big Datasets

Sometimes, a large dataset is just too much for you to handle. You might also need to verify additional data in order to complete the project.

To solve this problem, make sure you regularly update your data. You might also have duplicates of your data, in which case you should delete them.

To solve the problems presented by big data projects, remember these points:

  • You can ensure that your work is not hampered by the inability to use the appropriate combination of software and hardware tools.
  • Make sure to thoroughly review your data and eliminate duplicates.
  • For better efficiency and greater results, use Machine Learning techniques
  • What technologies will you need for Big Data Analytics Projects?

The following technologies are recommended for beginners in big data projects:

  • Open-source databases
  • C++ and Python
  • Cloud solutions, such as Azure or AWS
  • SAS
  • R (programming language).
  • Tableau
  • Javascript and PHP

Each technology will be useful in a specific sector. Cloud solutions are required for data storage and access.

You will also need R to access data science tools. These are the main problems that you will need to address when working on big data projects.

Before you start working on a project, it is advisable to become familiar with the technology before you begin. You will gain more experience if you attempt more big data projects.

Here are some Big Data Project Ideas that beginners can use:

Big Data Project Ideas for Beginners

This list of top big data project ideas for students can be used by both beginners and professionals who are just getting started with big data. These big data project ideas can help you get started in your career as a big data developer.

This list will help you find big data project ideas to complete your final year. Let’s get to it. These big data project ideas will help you build your foundation and move up the ladder.

As a beginner, we know how difficult it can be to find the right ideas for your project. It’s hard to know what you should do and how you can benefit from it.

We have prepared the following list for big data projects so that you can get started on them: Let’s begin with big data project ideas.

1. Classify 1994 Census Income Data

This project is a great way to get started with big data projects for students. Based on the available data, you will need to create a model that predicts if an individual’s income in the US is greater or lesser than $50,000.

Income is dependent on many factors and you will need to consider each one.

2. Chicago Crime Rates – Analyze

Big data is used by law enforcement agencies to identify patterns in crimes. This helps agencies predict future events and they are able to reduce crime rates. It is necessary to identify patterns and create models. Then validate the model.

Also read: 10 Best Cybersecurity Projects Ideas for Final Year Students

3. Text Mining Project

This is a great deep learning project idea for beginners. Text mining is a highly sought-after skill that will allow you to showcase your strengths as a data scientist. This big data project for beginners will require you to analyze and visualize the text in documents.

This task will require you to use Natural Language Process Techniques.

Big Data Project Ideas: Advanced Level

4. Big Data for cybersecurity

This project will examine long-term and invariant dependencies in large data volumes. This Big Data project aims to address real-world cybersecurity issues by exploiting complex multivariate time series data to identify vulnerability disclosure trends. This cybersecurity project aims to create a robust and innovative statistical framework that will allow you to gain a deep understanding of disclosure dynamics and their fascinating dependence structures.

5. Prediction of health status

This is one of many interesting big data projects. This Big Data project uses massive data sets to predict health status. This project will require the creation of a machine-learning model that can accurately classify users based on their health attributes. It will also qualify them for having heart disease or not. Decision trees are the best method of machine learning for classification and therefore, they make a great prediction tool. The feature selection approach will improve the accuracy of the ML model’s classification.

6. Cloud servers can detect anomalies

This project will implement an anomaly detection method for streaming large data sets. Two core algorithms will be used to detect anomalies in cloud servers: state summarization (NAHSMM) and a novel nested-arc hidden semi-Markov model. State summarization will extract usage behavior reflective states from the raw sequences. NAHSMM will create an algorithm for anomaly detection with a forensic module in order to determine the normal behavior threshold during the training phase.

7. Big Data Recruitment

The HR department of any company must be responsible for recruiting. We’ll be creating a Big Data project to analyze large amounts of data from real-world job postings that have been published online. This project has three stages:

  • Four Big Data job families can be identified in the dataset.
  • Companies are looking for nine types of Big Data skills.
  • Each Big Data job family should be categorized according to the required level of expertise for each Big Data skill.
  • This project aims to assist the HR department in finding better Big Data job opportunities.

8. Malicious user detection in Big Data collection

This is one of the most popular deep learning project ideas. Trustworthiness (or reliability) is a key aspect of Big Data collection. This project will determine the reliability factor for users within a Big Data collection. The project will split trustworthiness into familiarity trustworthiness and similarity trustworthiness to achieve this. It will also divide the participants according to their similarity trustworthiness factor, and calculate each group’s trustworthiness separately to reduce computational complexity. This allows the project’s trust level to be represented as a whole by grouping.

9. Tourist behavior analysis

This is one of many great big data analytics projects for students. This Big Data project analyzes tourist behavior to determine their interests and most visited places. It then predicts future tourism needs. This project has four stages:

  • To extract interest candidates from geotagged photos, textual metadata processing is used.
  • Clustering of geographic data to identify tourist destinations for each identified interest.
  • Each tourist attraction has a representative photo identification
  • Time series modeling is used to build a time series by counting the number of tourists each month.

Also read: 8 Best AWS Projects Ideas for Beginners

10. Credit Scoring

This project explores the potential of Big Data to improve credit scoring. This project aims to examine the performance of both economic and statistical models. It will create credit card scorecards by using a unique combination of data sets that include call-detail records and credit and debit account information. This will allow you to predict the creditworthiness of credit card applicants.

11. Forecasting electricity prices

This is an interesting idea for a big data project. This project uses Big Data sets to forecast electricity prices. To predict electricity prices, the model uses the SVM classification. However, the model will be trained in SVM classification and include redundant and irrelevant features, which can reduce its forecasting accuracy. Grey Correlation Analysis, (GCA), and Principle Component Analysis will be used to address this issue. These methods allow you to select the most important features and eliminate all unnecessary elements. This improves the accuracy of your model’s classification.

12. BusBeat

BusBeat uses GPS trajectories to track the movements of regular cars that are regularly moving in urban areas. This project proposes data-interpolation and network-based event detection techniques in order to successfully implement early event detection using GPS trajectory data. Data interpolation is a technique that helps to recover missing data from GPS data. It uses the primary feature of periodic cars and the network analysis to estimate the location of an event venue.

13. Yandex.Traffic

Yandex.Traffic was created when Yandex used its data analysis skills to create an app that could analyze multiple sources of information and display a live map of traffic conditions within a city.

Yandex.Traffic collects large amounts of data from different sources and analyzes the data to map precise results for a specific city via Yandex.Maps. Yandex’s web-based map service. Yandex.Traffic is able to calculate the average congestion level in large cities that have serious traffic jam problems. Yandex.Traffic gets information directly from traffic creators to give a clear picture of congestion in a city and allow drivers to work together.


This article will cover the top big-data project ideas. These are easy beginner projects that you can complete with no difficulty. After you have completed these easy projects, you can go back and learn more concepts, then you can move on to the intermediate projects. Once you feel confident you can tackle the more difficult projects. These big-data project ideas will help you improve your big-data skills.

Your strengths and weaknesses will be revealed when you work on big data projects. These projects will give you real-life data scientist experience.

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