Ready to elevate your data analytics game? Dive into our curated list of the top 10 data analytics projects, each with source codes. Perfect for both budding data scientists and seasoned analysts, these projects cover everything from data cleaning to predictive modeling. Get hands-on experience and unlock your full potential with these exciting real-world challenges. Let’s explore this data adventure together!
Data analytics projects come in various forms, each serving a unique purpose in leveraging data for insights and decision-making. From understanding historical trends to predicting future outcomes and optimizing strategies, these projects can be categorized into following categories:
Each type serves different purposes in leveraging data to inform decisions and improve processes.
Now, let’s look at the most common data analytics projects, divided based on their difficulty level.
Imagine pitching premium products to a customer who shops economically or offering bundled products to someone who prefers a single yet priced product. Will this convert?
Probably not. None of the policies checks out the one-size-fits-all criterion, as customers have unique needs and expectations. This is where customer segmentation analysis can save a lot of time and ensure maximum results.
A customer segmentation project aims for data analysts to identify different groups of customers with similar needs and behaviors so that companies can tailor their marketing, product development, and customer service strategies to meet their needs better. This can be done by clubbing them as per: marital status, new customers, repeat customers, etc.
Today, over 60% of companies are inclined toward customer choices, making them an advocate of customer segmentation and platforms (or tools) like Google Analytics, Customer.io, etc.
Luxury car manufacturers like Rolls Royce often use lifestyle-centric segmentation analysis to segment their top customers. Clearly, a data analyst familiar with customer segmentation would be a great asset to such businesses.
You can find the source code for customer segmentation analysis projects here.
Estimating future sales, or revenue for that matter, is a pronounced and essential business practice. As per Hubspot’s research, more than 85% of B2B companies use such data analytics, making sales forecasting projects well-decorated project ideas for analysts.
These projects estimate the revenue the company expects to earn over a pre-decided period, usually 1 year. This amount is computed using several factors, including previous sales data, market prices, demand, etc. As sales forecasting is an ongoing process, the work involves constant updates and bug fixes. Working as a sales forecasting data analyst would be a great option if you are proficient and prompt with constantly running data pipelines.
Companies like BigMart, Amazon and Flipkart rely heavily on sales and revenue forecasting to manage inventory and plan production and pricing strategies. This is primarily done during peak shopping seasons like Black Friday or Cyber Monday.
You can find sales forecasting analysis source code here.
Customer behavior is still a mystery for all. More often than not, businesses need to predict whether customers will likely cancel their subscription or drop a service, also known as “churn.” Churn prediction analysis aims to identify customers at risk of churning so companies can proactively retain them.
A data analytics project based on predicting customer churn has to be highly accurate, as many people, including customer success experts and marketers, depend on the project findings. This is why data analysts work with high-performing Python libraries like PyPark’s MLIB and some platforms and tools like Churnly.
You can find churn prediction analysis source code here.
The next on our list of analytics projects deals with fraud detection. Fraud detection analysis aims to prevent financial losses and protect businesses and customers from fraud. This is done using several KPIs (key performance indicators) mentioned below.
Data analysts are expected to calculate these metrics using historical customer and financial data and help companies detect fraud. One example of a company hiring data analysts for fraud detection is PayPal. PayPal uses manual review processes to investigate suspicious transactions and verify user identities.
You can fin fraud detection analysis source code here.
Sheerly, because of the vast number of people using social media to voice their opinions and concerns, it has become increasingly vital to analyze the sentiment behind it. Many companies undertake sentiment analysis to ensure these platforms are safe and sound for society.
Working on real-life big data projects as a learning data analyst gives an idea of how the knowledge is relevant and applicable to the real world. Moreover, social media is transforming into a highly sought-after area of work as social media giants like Facebook, Instagram, etc., are rapidly hiring professionals to analyze sentiments.
You can find social media sentiment analysis source code here.
Analyzing how users behave and interact with a product/service on your website is vital to its success. Once you understand their behaviour more deeply, you can discover more pain points and tailor a better-performing customer experience. In fact, 56% of customers only return if they have a good experience.
To ensure everything sails smoothly on a website, data analytics projects involve visualizations (using heatmaps, graphs, etc.) and statistical analysis of user survey data. You will use Python libraries like matplotlib, seaborn, and NumPy, R libraries like ggplot2, dplyr, etc., to map proper user behavior.
Tech companies like Google and Microsoft and medical research companies like Mayo Clinic hire data analysts to work, especially on user behavior analysis.
Here is the source code for website user behavior analysis.
Inventory optimization can be an example of a data analytics project for students with an advanced level of expertise. As inventories are massive, inventory analysis becomes a pervasive, especially in the retail markets. Inventory optimization analysis involves collecting and analyzing data on inventory levels, sales trends, lead times, and other relevant factors. Simply put, the aim is to ensure the right products are in stock when needed.
The process can also involve forecasting demand for each product, analyzing inventory turnover rates, and identifying slow-moving or obsolete products. You will be:
With experience in inventory analysis, you can seek professional opportunities in e-commerce companies like Amazon, Myntra, Nykaa, etc.
You can find the source code for inventory optimization analysis.
As the name suggests, employee performance analysis is a process of analyzing employee data to identify patterns and trends that can help improve employee productivity, engagement, and retention. It can be an excellent practice area as you will deal with data containing different data types, like numerical (attendance, turnover rates, etc.) and categorical (job satisfaction, feedback, etc.).
In such a project, you will need to:
You can also work with visualization tools like PowerBI and create dashboards for each department. Or you take up a proper data analytics workflow and do exploratory analysis using Python’s Pandas, NumPy, matplotlib, and Seaborn. Getting good at this analysis will open doors for a promising career in almost any field.
You can checkout the source code for employee performance analysis here.
This is one of the most common data analytics projects. It involves collecting and analyzing data on customer behavior, such as purchase history, browsing history, product ratings, and reviews. The practice is so common that the recommendation engine market is bound to reach over $15,13B by 2026!
It is widely used by e-commerce websites that believe a product display influences shoppers’ behaviour. It has been researched that over 71% of e-commerce websites now offer recommendations after a comprehensive review of historical website data. Analysts spend days and weeks visualizing sales, purchases, and browsing histories using Python libraries like Seaborn, matplotlib, etc.
Proficiency in this data analytics segment can help you build a promising career in companies like YouTube, Netflix, and Amazon.
You can checkout source code for product recommendation analysis here.
Supply chain management involves the planning, execution, and monitoring of the movement of goods and services from suppliers to customers. Following the same, a data analytics project on supply chain management requires you to work on the following:
The main idea is to study all the factors and see how each one of them affects the chain. Many companies are indulging in supply chain analysis. For example, PepsiCo utilizes predictive analytics to manage its supply chains. As a result, the company actively hires seasoned data analysts familiar with supply chain management. The main idea is to study all the factors and see how each one of them affects the chain.
You can check the source code for supply chain analytics here.
Time needed: 1 hour
Follow these steps to solve a data analytics project:
The first step of any data analytics project is to frame a comprehendible problem statement or a question. This question should answer the following— what is the intent of doing this project, and what are the stakeholders expecting?
Once you know the problem, the next step is to gather relevant data that will be used for analysis. You can use any publicly available dataset belonging to the domain. This stage also involves working with various data-cleaning and wrangling techniques to transform it into a usable format.
The next step is about exploring the data visually. In this stage, analysts often work with Python libraries like Pandas, Sklearn, and matplotlib to get various insights into the dataset. They can get statistical summaries and visual representations like scatter plots, bar charts, etc., to understand and interpret the data.
Once the data has been explored, analysts can build statistical models and ML algorithms to analyze the data and use the findings for decision-making. These models must be tested and validated to ensure accuracy and reliability.
This is the last stage of a data analytics project. Here, analysts put the machine learning models into the actual workflow and make the outcomes available to users or developers. Once the model is deployed, they observe its performance for changes, like data drift, model degradation, etc. If everything appears operational, the project can be deemed successful.
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A. Having programming skills can be helpful for data analytics projects, but it’s not always necessary. There are tools like Tableau and Excel that allow you to analyze data without coding.
A. Some prominently used data analytics tools used are Python, R, SQL, Excel, and Tableau.
A. Some good data analytics projects for the intermediate level include predicting stock prices, analyzing customer churn, and building a recommendation system.
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