3 Data Analytics Projects for Beginners
If you're at the beginning of your career as a data analyst, then engaging in data analytics projects is of great importance to further enhance your skills and strengthen your resume during your job search.
Especially if you lack real-life experience, working on a project can help you deepen your technical knowledge.
In today's article, we have compiled the following 3 data analytics projects that you can start immediately for guaranteed success:
- Sentiment Analysis on Social Media
- Exploratory Data Analysis (EDA)
- Product Recommendations Analysis
First, let’s start by introducing some important facts regarding data analytics.
Where is Data Analytics Used?
Data analytics encompasses a set of techniques used by data analysts to analyze raw data, identify trends, and answer questions.
Data analysis is used in various fields, including retail, healthcare, manufacturing, marketing, and banking services.
There are many available data analysis tools, with some of the most popular ones being Tableau, Power BI, Google Analytics, and Qlik Sense.
Following up, let’s take a more detailed look at some important data analytics projects you can start as a beginner and further enhance your skills.
3 Data Analytics Projects for Beginners
Some interesting data analytics projects you can start are the following ones:
Project #1: Social Media Sentiment Analysis
Sentiment analysis is the process of analyzing digital text to determine whether the emotional tone of the message is positive, negative, or neutral.
With the vast amount of textual data available today, companies have abundant sources of text data, including email messages, customer support chat transcripts, social media comments, and customer reviews of services or products.
Sentiment analysis is a particularly useful technique in natural language processing (NLP) and is widely used by data analysts to improve customer service and enhance a company's reputation.
To start this data analytics project, you can begin by exploring websites such as:
- Amazon (for product data and reviews)
- Rotten Tomatoes (for movie data and reviews)
Let’s move on to the next project on our list.
Project #2: Exploratory Data Analysis (EDA)
The ability to conduct exploratory data analysis (EDA) is crucial for a data analyst.
EDA involves examining the structure of data, allowing a data analyst to identify various patterns and characteristics.
It also plays a significant role in data cleaning, another critical process in a data analyst's work.
Through this project, you can extract important variables, identify outliers and anomalies, and, in general, check underlying assumptions using Python libraries such as Pandas, NumPy, matplotlib, and Seaborn.
Let's look at an example for better understanding.
A particularly useful project for exploratory data analysis is the analysis of data from American universities to identify students' preferred characteristics when choosing a university.
Such a project was conducted using tools like Numpy and Pandas, as well as Matplotlib and Seaborn for data visualization and exploration.
Additionally, another example of an in-depth exploratory data analysis project is the analysis of the global population.
Through this project, you can explore various columns, visualize the least and most densely populated countries, and examine population density and growth rates.
You can also create a map showing the ranking and correlation of countries.
Project #3: Product Recommendations Analysis
This project involves collecting and analyzing data related to user behavior, such as purchase history, browsing history, product reviews, and ratings.
In recent years, with the rise of platforms like YouTube, Amazon, and Netflix, the recommendation engine market has grown rapidly and is expected to reach over $16 billion by 2026.
As expected, product recommendation analysis is widely used by e-commerce sites, as the presentation and recommendation of a product can influence buyer behavior.
In this data analytics project, an analyst can visualize sales, purchases, and browsing history using Python libraries like Seaborn and Matplotlib.
So, we've seen some data analytics projects that you can start to further enrich your resume and stand out in your job search.
The field of data analysis is particularly popular, constantly evolving, and offers many opportunities and job positions.
As we've seen, data analytics is an integral part of some of the biggest companies like Netflix and Amazon.
Thus, if you are intrigued to learn more about the fascinating world of data analytics and all the emerging trends surrounding it, follow us and we will keep you posted!