Top 10 Data Analytics Projects for Beginners [2024 Updated List]

Top Data Analytics Projects for Beginners

In the dynamic field of data analytics, actual initiatives serve as the means of converting abstract concepts into useful information. These projects provide an engaging, practical method for learning fundamental analytics skills, regardless of whether you’re just starting with data or want to advance your knowledge. Let’s explore the top 10 data analytics projects in this article.

What is the Role of Data Analytics?

Before knowing the top 10 data analytics projects to work on, it is necessary to understand why data analytics is essential. Let’s find out below.

  • It is used to uncover hidden insights by collecting and analyzing data based on business requirements.
  • The analyzed data is generated into reports and is shared with the respective stakeholders for consideration.
  • This data is also used for market analysis to understand a business’s strengths and weaknesses.
  • Through data analytics, businesses can also improve their customer experience.

Why Do You Need to Work on Data Analytics Projects?


If you want to advance in your career and get a job in the data analytics field, you should get hands-on experience by working on projects. 

Below are some reasons why it is essential:

  • It can help you learn data analysis by implementing practical knowledge of theoretical concepts.
  • You’ll be able to determine your advantages and disadvantages when it comes to data analysis methods and technologies.  
  • You can also determine your actual area of interest within the data analytics field.
  • It will help you create an outstanding portfolio that can make you stand out from the rest. You may demonstrate your abilities and obtain real-world experience with these projects.
  • Working on complex projects can provide you with a sense of success when you finish a project and increase your confidence in working in the data analytics industry.

How Do You Choose the Best Data Analytics Projects?

It is crucial to know how to select the best data analytics project, as it can have a significant impact on your career. Here are some steps to consider:

  • Consider your interest in data analytics before signing up for the project.
  • You should evaluate your skill level and choose a project that matches your proficiency.
  • Make a list of the objectives you hope to accomplish with the initiatives. These can be exploring a new topic, skill enhancement, or portfolio creation.
  • You should have access to the various databases you will need during the project.
  • Do consult mentors or people in your network to seek guidance on different projects and how feasible or challenging they can be to complete.

Different Types of Data Analytics Project

Choosing the best data analytics project can be really overwhelming as there can be many areas where you can find a project. Here are some areas which you should focus on as a beginner.

  • Data Importing – In these projects, you can learn how to import different datasets using Python, SQL, web scraping, or R.
  • Data Cleaning – These projects are designed to use the different R and Python libraries to first clean and later process the data.
  • Data Manipulation – Using data manipulation, you can shape the datasets of your projects and perform data analysis and visualization.
  • Data Visualization – These projects would require you to showcase the data to the different stakeholders using graphs and plots.

Top 10 Data Analytics Projects for Beginners

Listed below are the top 10 data analytics projects that beginners can start working on. 

These projects will help them enhance their skills and develop a complete understanding of the data analytics world. Working on these projects can help land prosperous career opportunities.

1. Exploring the New York City Airbnb Market

This project involves using the data importing and data cleaning techniques of the data analytics process. Using these techniques, you will be required to analyze the Airbnb market in New York City. You will be required to ingest and merge data from different types of files, clean strings, and format dates to find accurate information.

It is an excellent project for people who want to excel in data importing and data cleaning tools. If you find it difficult, you can always enroll in a data analytics bootcamp from CCSLA, get the required knowledge, and then attempt the project again.

2. Market Basket Analysis

Data scientists employ market basket analysis, which is a data mining approach, to gain deeper insights into their clientele and boost sales. Here, customers’ purchasing behaviors are tracked to find different product combinations that are bought in tandem.  


The concept here is that a customer’s purchase of a specific item or set of items (let’s call it Product ‘A’) raises the possibility that the customer will also be interested in purchasing another item or set of items (let’s call it Product ‘B’), suggesting that a customer’s interest in A may also be reflected in their interest in B.

By assisting with cross-selling, targeted promotions, and recommendations, this analysis can help boost sales. The trends seen in this research can even be taken into consideration while writing menus. Aisles and products can be organized in grocery shops to take into consideration commodities that are frequently purchased together. One method for carrying out a consumer market basket analysis is to use the data mining algorithms Apriori and Fp Growth.

3. Quora Question Pairing

One of the most widely used Q&A websites worldwide is Quora, which can be used for data analysis. Users were required to categorize duplicate question pairs using advanced natural language processing (NLP) in a recent Kaggle competition. For instance, it is inappropriate for Quora to host the questions “Which state in the U.S. has the maximum number of people?” and “What is the most populous state in the USA?” separately. 

There are more than 400,000 lines in this Quora dataset that could be questioned about duplicate pairs. Each line includes the complete text of each question, the IDs for every query in the pair, and a value in binary format indicating whether the line has more than one duplicate pair. 

4. Exploring Bitcoin Crypto or Stock Market

This project is all about the Bitcoin and cryptocurrency market. This project will require you to use data manipulation the most. Here, you would have to remove all cryptocurrencies with no market capitalization and later compare Bitcoin with other cryptos. You will also have to prepare data and reports for visualization.

You can also apply the same principle as above to compare different stocks in the stock exchange market. This will help you learn how to manipulate data for analysis purposes.

5. Google Audience Reviews

Google reviews are best for data analysis purposes as they provide excellent customer feedback. You can use the Google My Business API and work with location data to extract Google audience reviews. This project will require you to use Python and perform sentiment analysis on the Google reviews from the Google Play Store. 

After that, you’ll need to perform exploratory data analysis using Panda profiling to look for variables, correlations, missing values, and interactions. TextBlob can also be used to determine sentiment scores based on sentiment polarity and subjectivity.

6. Airline Data Analysis

Data analytics play a vital role in the airline business. Starting from preflight to post-flight, there are a lot of datasets that need regular analysis, and thus, it makes it the top 10 data analytics projects to work on for beginners. This project can make the customer experience more streamlined by customizing the travel experience.

You can analyze data to determine the popular routes or flights based on seasonality or identify patterns and trends in flight delays. Another point for analysis can be the technical problems to figure out which airlines face this problem. One of the main obstacles in analyzing this dataset is the sheer volume of variables and data points that need to be examined in each area where improvements are required.

7. Finding Movie Similarities Using Plot Summaries

This an exciting and interesting project that will require you to use clustering and NLP to find similarities in different movie plots. If you love working on statistics, it is the perfect project to apply all its knowledge.


You will be required to import two database sets, merge them, tokenize and stem words, turn words into vectors, and then cluster using KMeans. Additionally, you will compute the similarity distance and use Linkage, Matplotlib, and Dendrograms to illustrate the findings.

8. Yelp Data Analysis Using Hive

As per Wikipedia, Yelp had 52 million unique mobile site visitors and 43 million unique desktop webpage visitors in 2020. This is a massive amount of data for analysis purposes. Yelp is a popular online directory that displays reviews submitted by users about companies in a particular area.

The main project goal is to gain experience handling massive datasets with Hive and Apache Hadoop while implementing data engineering concepts. These concepts will include processing, storing, and retrieving data from the Yelp dataset. By examining the Yelp dataset, businesses can learn important information about what customers think about both their company and their competitors.

9. Nutrition Facts of McDonald’s

Another fun and eye-opener project that you can be involved in. More often than not, McDonald’s is criticized for its high sodium and fat content in its food products. You can use Kaggle to find this dataset and perform a nutrition analysis on each food product listed on its menu. Irrespective of whether it’s a salad, beverage, or dessert, you can analyze all.

You will have to import the files in Python in CSV format. Later, categorize these products based on different factors, such as fiber content and sugar. For visualization purposes, you can use bars, scatter plots, pie charts, and heat maps. You will have to use tools such as Pandas, Numpy, and Seaborn libraries for this project.

10. Credit Card Approval Prediction

You will need to create an ideal machine-learning model for this project in order to forecast whether or not specific credit card applications will be approved. You will be required to interpret the data and fill in the missing values as the first step. Subsequently, you will use the training set to build a model based on logistic regression and preprocess the data.  

Finally, you will assess the outcomes and use Grid searching to enhance the model’s performance. Using simple ML algorithms is an essential part of the daily life of a data analyst, and hence, understanding this concept can come in handy during your professional journey. Working on this straightforward yet instructive project will give you a great deal of experience.

Skills Needed to Work on These Projects

Irrespective of which level you are on or the current skill set you possess, below are a few skills you will need and can also improve:

  • SQL
  • Programming
  • Data Cleaning
  • Visualization
  • Microsoft Excel
  • Machine Learning
  • Artificial Intelligence
  • NPL

Final Thoughts

Upon the conclusion of our top 10 data analytics projects for beginners in 2024,’ keep in mind that every project serves as a springboard for your data analytics career as well as a learning opportunity. Take on the difficulties, polish your abilities, and convert abstract ideas into practical knowledge. These projects help you advance in the fast-paced field of data analytics. 

Additionally, you can start learning these concepts by enrolling in a Data Analytics and Engineering bootcamp training program by CCSLA. These bootcamps are excellent to gain a theoretical and practical understanding of the broader concept. Moreover, these do not require a lot of time, and in just 12 weeks, you can become an expert in data analysis and start your prosperous and rewarding career.