Data analytics is becoming very popular every passing year. Most students are trying to join data analysis courses to be good data analysts. And the students need to work on data analytics projects within their courses to get hands-on experience. These projects are pretty helpful for the students to enhance their skills and show their expertise in their portfolios.
We know that finding the best data analytics projects is tricky, especially for students who are not as good as their fellows. These students always think it is pretty tough or complex to work on data analytics projects. But it is not valid for everyone.
In this blog, we will share with you different types of data analytics projects that the students should work on. We will also cover the examples of data analytics projects so that you can understand the project’s concept better and start working on these projects. Data analytics projects take more time to complete if you need to work with huge datasets for data analysis projects. Let’s have a look at data analysis projects in Python and many more:-
What Is A Data Analytics Project?
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The data analytics process is not rocket science. But yes, it requires analytical and logical skills. Most straightforwardly, it is a method to use past and current project data to enable the data analytics to make effective decisions on project delivery. The major parts of data analytics are descriptive analytics and predictive analytics. Descriptive analytics is used to present the data in the most effective format. At the same time, predictive analytics predict future performance based on past data.
How Long Do Data Analyst Projects Take?
It is estimated that a data analytics project takes somewhere 2 weeks to 6 months on average. It depends on the amount, complexity, and processing time of data. Apart from that, the resources and needs for the projects also affect the duration of data analytics projects.
Tools Used in Data Analytics Projects
Here is some information about Open-Source and Proprietary Tools Used in Data Analytics Projects:
A. Open-Source Tools
Open-source tools refer to software applications that are free to use, modify, and distribute. The source code for these tools is made available to the public, and users can access and modify it per their requirements. Some of the commonly used open-source tools for data analytics projects are:
R – A programming language used for statistical computing and graphics
Python – A programming language used for data analysis, machine learning, and artificial intelligence
Apache Hadoop – An open-source distributed storage and processing framework used for big data analysis
Apache Spark – An open-source cluster computing framework used for big data processing
Apache Cassandra – An open-source distributed database management system used for storing and managing large amounts of data
B. Proprietary Tools
Proprietary tools refer to software applications that are owned by a company or an individual, and the source code is not available to the public. These tools are often paid and come with licensing agreements. Some of the commonly used proprietary tools for data analytics projects are:
Tableau – A data visualization tool used for creating interactive dashboards and reports
SAS – A software suite used for data management, statistical analysis, and predictive modeling
IBM SPSS – A software package used for statistical analysis and data management
Microsoft Excel – A spreadsheet software used for data analysis and visualization
Alteryx – A self-service data analytics tool for data blending, cleansing, and predictive modeling.
Both open-source and proprietary tools have their advantages and disadvantages. Open-source tools are often free to use and have a large community of developers contributing to the development of these tools.
On the other hand, proprietary tools come with technical support, training, and customization options. The choice of tool ultimately depends on the specific requirements of the project and the resources available to the team.
Benefits Of Using Data Analytics Tools
Here are some benefits of using data analytics tools :
- Faster data processing and analysis
- Improved accuracy and reliability of results
- Better visualization and presentation of data
- Facilitates decision-making and problem-solving
- Helps identify patterns and trends in data
- Enables predictive modeling and forecasting
- Facilitates data sharing and collaboration
- Improves operational efficiency and productivity
- Increases revenue and profitability
- Helps identify areas for improvement and optimization
How Do You Start A Data Analysis Project?
The topmost prominent methods for data analytics projects are:-
- The initial step is to understand the issue and then outline the expectations.
- The next step is to understand the dataset.
- After understanding the dataset, it is time to prepare the data.
- Perform exploratory analysis and modeling on the data
- The next step is to validate the data to ensure it is error-free and relevant.
- The last step is to visualize the final data.
Skills Required For Data Analytics Projects
Having good command over statistics is not enough to be data analytics. Therefore, the students should work on skills to get a good knowledge of data analytics.
SQL stands for a structured query language used to work with data within the databases. Data analysts need to work with data in their daily routine. They need to perform various operations with databases such as access, retrieve, delete or alter the data whenever required.
2. Programming Language
The students should have a good command of programming languages like R and Python. There is no need to learn both of these programming languages, and you should be one for it. Both of these programming languages are quite popular for data analytics. These programming languages offer massive amounts of libraries that make it convenient to process and manipulate the data. Keep in mind that if you learn both of these programming languages, you will better work on data analytics projects in Python or R.
3. Data Visualization
Data visualization is an art for a data analyst. For this, there are lots of tools available for data visualization through charts, graphs, or other visual layouts. The students need to work on data visualization skills to work efficiently with data analytics projects.
4. Data cleaning
Data cleaning is used to improve the data equity with the help of filters for noisy, inaccurate, and irrelevant data for analysis. It is the key skill needed to work efficiently with data analytics projects.
5. Microsoft Excel
MS Excel is one of the topmost spreadsheet software in the world. It is used to organize the raw data into the most readable format. There are many tools available in Excel that allow you to customize the field and functions and convert the data into the most valuable format.
6. Machine Learning
The students should have machine learning and natural processing skills to work efficiently on data analytics projects. Machine learning offers various algorithms and techniques to easily process a massive amount of data.
Why Should Students Work On Data Analytics Projects?
- Let us learn why students should work in data analytics projects. Here are the topmost reasons to work on data analytics projects.
- Data analytics projects are the best ways to get hands-on experience instead of theoretical knowledge.
- It also helps students identify their strengths and weaknesses with various data analytics tools and techniques.
- It helps the students to add the experience to their portfolio.
- The data analytics projects also help the students boost their confidence in working in data analytics and get a sense of accomplishment.
- Top 7 Big Data Analytics Tools | Its Technology And Techniques
- Top Most Data Analysis Tools In Excel You Should Use
11 + Data Analysis Project Ideas For Students In 2023
1. The Internet Movie Database
It is one of the best data analytics projects ideas for beginners. In this, the students need to extract the data from IMDb. You can also collect details about the top TV shows, movie reviews, and many more. Apart from that, it should also contain the bio of the actress and actors. In IMDb, the data is stored in a consistent format. Therefore, you can build a movie recommendation system with the data. And then use the data for further analysis.
Apart from that, several other tools can help you in this process. The best websites you should use for web scraping are:
- Job Portals
- Business Listing Sites
Apart from these sites, Kaggle is the best repository site for those students who are not good at finding the datasets.
2. Job portals
Job portals are the best sources for data scraping. These types of portals contain standard data types. There are plenty of methods to scrape data from these portals. To be more precise with the data, you should target the locations. And then collect the job titles, companies, salaries, locations, skills, etc., based on the location.
3. E-commerce Sites
Ecommerce sites always have a massive amount of data from product details to price. You can quickly scrap various product information from these sites and reviews. The data is organized in the best format, and it is also scalable. It can help you create a project where you can pick a product category and then gather data from these sites.
4. Social Media Sites
Social media sites like Reddit and Quora are pretty helpful for data scraping. The reason is that these sites are full of various kinds of data. In these, you can search for a particular keyword and find a massive amount of data with the help of keywords like upvotes, user data, comments, and many more. These sites make it a straightforward task for you because you can pick the data according to your interest.
4. Gender And Age Detection
You can build this exciting data analytics project in Python by taking the data from data.gov. In this system, the user can predict gender and age by analyzing the image in the dataset of billions of images. For this, you should know the computer vision and python principles that can make it happen.
5. Credit Card Fraud Detection
There are lots of countries that are taking loans from the World Bank. Apart from that, many business people also take loans from the World bank. But many fraudsters always try to steal money from the World Bank with the help of fake credit cards. It can be a suitable data analytics project in R. You should know decision trees, classifiers, logic regression, neural networks, etc. You can use World Bank data to detect the fraud credit cards among the genuine ones.
If you find both of these data analytics projects challenging for you, you should take the help of r/datasets on Reddit. It will help you to see lots of datasets in which you can work quickly and efficiently. They have projects for beginners, intermediate and advanced professionals.
8. Global Suicide Rates
Suicide has become the most common problem in almost every country. In this, you can pick a country and then gather the data, including year, gender, age, population, mental health, GDP, and many more. As it is an EDA, you need to ask yourself what pattern you should follow? Are suicides increasing or decreasing in that country? Which gender is more correlated to the suicide rate?
9. World Happiness Report
With this, you can track the six factors to measure the happiness of the citizens across the world. And these factors are expectancy, economics, social support, corruption level, freedom, and generosity. Here you need to figure out which country is happiest? Which content is happiest among other contents? Which is the fundamental factor behind the happiness of the nation? As a whole, is happiness increasing or decreasing?
10. Fake News Detection
Fake detection is one of the most popular data analytics projects in Python. All you need to do is build a system that can detect a piece of fake news to save people from being scammed with fake news. For this, you can use social media channels or other online media channels. You can use PassiveAggressiveClassifier in Python to build a TfidfVectorizer to detect whether the news is fake or real.
Covid also has lots of data to be used in data visualization. You can visit Kaggle to get lots of datasets on Covid 19. You can use the most trending heatmap that can show the red mark on the cities or countries where the number of covid-19 cases is high.
12. Most Followed On Instagram
It is the best idea to work on because it has lots of data related to celebrities and brands. You can visualize the most-followed people on Instagram with great potential for visualization. For this, you can create bar charts that can track the change in the most followed account during a specific time.
13. Travel data
You can work on travel data for your data visualization projects. Lots of data analytics programmers have created data analysis project in Python GitHub. You can pick any of these or start your own. Likewise, there are many destinations to showcase in your graph to correlate the expenses and the number of tourists.
As we have seen, you should work on some of the best data analytics projects. Apart from these, there are many more exciting topics to work on. But these are the best and unique projects that will help you exhibit your skills and gain confidence in the world of data science.
Some projects can be complex for you but keep in mind that you are splitting them in parts to make them easier for you. Other than that, you can also work on data analytics projects for beginners. You can also demonstrate your skills by working on the dataset that you can collect as per your interest.
In contrast, there is no limitation of datasets, and you can work on any dataset. If you are still unsure which data analytics skills, you need to work on. And which data analytics project is suitable for you? Then get in touch with our data analytics assignment help experts, and they will guide you throughout your project. They will also clear your doubts and help you gain some confidence in your life.
Frequently Asked Questions
Q1. What are some data analytics projects?
It would help if you tried some of the leading data analytics projects once in your data analytics course journey. Because these tasks are the reflation of the task, you will perform in your data analysis career.
Exploratory data analysis
Q2. What is data analysis example?
There can be a lot of data analysis projects examples. But if we talk about the best one, it is in our daily lives. For example, we need to make plenty of decisions in our daily lives. For this, based on the analysis of the past data, we ensure our future choices.