10 Data Analyst Projects for Beginners

Start your data analysis journey with 10 data analyst projects to gain practical experience

Starting a Data Analysis profession involves practical experience, which may be obtained by working on Data Analyst projects. The thorough manual 10 Data Analyst Projects for Beginners offers useful projects for aspiring data analysts. These projects cover a range of data analysis topics, including cleansing, manipulating, and interpreting data. A data analyst can improve their abilities, comprehend practical applications of data analysis, and demonstrate their aptitude to future employers by working on these projects. These 10 Data Analyst Projects are therefore more than just assignments; they are also stepping stones to a rewarding Data Analysis career. Here are 10 data analytics projects with details that are beginner-friendly:

1. Exploring the NYC Airbnb Market:

You will use your data importing and cleaning skills in this research to examine the New York Airbnb industry. To extract accurate information, you will ingest and merge data from various file kinds, clean strings, and format dates.

2. Word Frequency in Classic Novels:

You will scrape a book from the Project Gutenberg website for this project using requests and BeautifulSoup. You will use NLP to identify the most common words in Moby Dick after scraping and cleaning the text data. The project gives you an introduction to the world of Python web scraping and NLP.

3. Consumer Sentiment Analysis:

To study consumer sentiment toward a product or brand in this project, you will leverage data from Twitter. Before classifying the tweets as favorable, bad, or neutral, you will preprocess them using text-mining algorithms. Your ability to clean up data, visualize data, and conduct research is improved by working on this project.

4. Sales Data Analysis:

In this project, you will examine sales performance and trends using data from an online retail company. You will investigate many facets of the information, including product categories, customer groups, order dates, and geographical areas. To illustrate the critical metrics and insights, you will also design interactive dashboards. You may hone your data manipulation, data aggregation, and data visualization skills through this project.

 5. Customer Segmentation:

You will segment the customers in this project based on their purchasing habits using data from an e-commerce platform. Recency, Frequency, and Monetary (RFM) analysis will be used to award scores to each client, and clustering methods will be used to divide them into several parts. The project gives you practice using your feature engineering, unsupervised learning, and data pretreatment skills.

6. Movie Recommendation System:

You will create a movie recommendation system for this project using data from MovieLens. Both collaborative filtering and content-based filtering will be used. Movies are recommended using content-based filtering based on how closely they match the user's tastes. Through collaborative filtering, movies are suggested based on the opinions of other users who share your preferences. Your ability to explore data, model data, and evaluate data is improved by working on this project.

7. Credit Card Fraud Detection:

You will use Kaggle data in this project to find fraudulent credit card transactions. The imbalanced data collection will be handled using a variety of strategies, including resampling, feature selection, and anomaly detection. Various categorization algorithms will also be used to determine whether a transaction is fraudulent or not. You may hone your data analysis, data balancing, and supervised learning skills by working on this project.

8. Stock Price Prediction:

You will use information from Yahoo Finance in this project to forecast a company's stock price in the future. The trend, seasonality, and noise components of the data will be divided using time series analysis techniques. To predict stock values based on previous data, you will also employ several regression models. You can exercise your skills with the project in forecasting, time series analysis, and data visualization.

9. Spotify Music Analysis:

You'll examine user music patterns and preferences in this research using data from Spotify. You will look into the songs' tempo, energy, valence, popularity, genre, and other characteristics. In addition, you'll make playlists based on many factors, like decade, activity, and mood. Your ability to gather data, explore data, and visualize data is improved by working on this project.

10. COVID-19 Data Analysis:

In this project, you will examine how the COVID-19 pandemic has affected various nations and regions using data from Our World in Data. You will look at different pandemic indicators, such as cases, deaths, immunizations, testing, etc. Additionally, you will contrast the responses and results of other nations and areas. The project gives you the chance to hone your data import, data cleaning, and data analysis abilities.