Introduction
Not a single day passes without us getting to hear the word “data.” It is almost as if our lives revolve around it. Don’t they? With something so profound in daily life, there should be an entire domain handling and utilizing it. This is precisely what happens in data analytics. People equipped with the technical know-how spend hours on end muddling with datasets. But how do you get there? It may seem an intimidating area, but it is rather intriguing. All you need is a basic understanding of data technologies work, experience working on data analytics projects, and an eye for detail.
Irrespective of your place in the data journey, data analytics projects add significant value to your expertise, resume, and the real world. This article enlists and discusses the 10 best data analytics projects.
Table of contents
- 10 Data Analytics Projects with Source Code
- Best Practices for Successful Data Analytics Projects
- Tools and Technologies Used in Data Analytics Projects
- Types of Data Analytics Projects
- Steps Involved in Data Analytics Projects
- Importance of SQL in Data Science Projects
- Conclusion
- Frequently Asked Questions
10 Data Analytics Projects with Source Code
These are the data analytics projects that you must checkout:
- Customer Segmentation Analysis
- Sales Forecasting Analysis
- Churn Prediction Analysis
- Fraud Detection Analysis
- Social Media Sentiment Analysis
- Website User Behavior Analysis
- Inventory Optimization Analysis
- Employee Performance Analysis
- Product Recommendation Analysis
- Supply Chain Management Analysis
Customer Segmentation Analysis
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.
Sales Forecasting Analysis
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.
Churn Prediction Analysis
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.
Fraud Detection Analysis
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.
- Fraud Rate.
- Incoming Pressure (the percentage of attempted transactions that are fraudulent).
- Final Approval Rate.
- Good User Approval Rate.
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.
Social Media Sentiment Analysis
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.
Website User Behavior Analysis
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 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:
- Finding target personas,
- Studying purchasing (or sales) patterns,
- Identifying key locations and seasonal trends,
- And optimizing the inventory size.
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.
Employee Performance 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:
- Set goals and decide on performance metrics,
- Collect feedback data,
- Use this data for preprocessing and analysis,
- Infer who performs the best.
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.
Product Recommendation Analysis
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 Analysis
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:
- Demand forecasting,
- Inventory management,
- Analysis of supplier performance,
- Logistics optimization, etc.
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.
Best Practices for Successful Data Analytics Projects
1. Data Quality and Integrity
A data analytics expert works with vast volumes of data during the entire process of collecting data, preprocessing it, and finally using it for analysis and interpretation. This makes it vital for them to prioritize some of the steps that ensure data cleaning and manipulation is done ethically. While they are free to wrangle data in any form demanded by the project, they must retain all the information, keeping the quality and completeness intact as it directly impacts the accuracy of results.
2. Collaboration Between Teams
Fostering an environment of collaboration and alignment among the team members and different teams sets the project on a successful track. This is because different teams, and individuals, bring different skills and perspectives to the table, resulting in a more diverse and complete analysis.
3. Communicating Results Effectively
Communication is key. It is not only a mantra to success but something that keeps everyone on the same page. Good communication ensures that each team member knows the project’s goals and expectations and can pass on the project findings to all technical and non-technical stakeholders.
4. Continuous Learning and Improvement
Data analytics is an iterative process, and there is always room for improvement. Continuous learning and improvement ensure that the data analytics project results are credible and all necessary changes to improve the accuracy and relevance of the insights are taken into account.
Tools and Technologies Used in Data Analytics Projects
Programming Languages (Python, R)
Python and R are the most popular programming languages in data analytics projects. Both languages offer a wide range of tools and technologies for the same.
Python is a general-purpose programming language. It comes with a bunch of libraries and frameworks like matplotlib, scikit-learn, TensorFlow, pandas, numpy, statsmodel, and many more. These components are widely used in exploratory programming, numerical computation, and visualization.
R programming is a language specifically designed for data analysis and statistical computing. It offers numerous tools and technologies like dplyr, ggplot2, esquisse, BioConductor, shiny, lubridate, and many more.
Data Visualization Tools (Tableau, Power BI)
If you do not wish to avoid getting your hands dirty during the data analysis process, you can work with some visualization tools. As you are probably working through the data domain, you must be aware of Tableau and Power BI.
Tableau is a data visualization platform that allows users to connect to various data sources, including spreadsheets, databases, and cloud services. The platform is revolutionizing the way analysts work with data by offering features like
- Data blending,
- Interactive dashboards,
- Drag-and-drop interfaces,
- Data Mapper, etc.
On the other hand, Power BI is a business analytics service by Microsoft that works similarly and helps in data visualization. However, it is a bit more sophisticated than Tableau and hence, has a steeper learning curve. Power BI offers:
- Natural language querying,
- Interactive dashboards,
- Data modeling, etc.
Big Data Technologies (Hadoop, Spark)
Big data technologies like Hadoop and Spark are widely used for data analytics projects, especially when organizations need to process and analyze big data.
Hadoop is an open-source software framework that enables distributed processing of large data sets across clusters of computers. Hadoop offers:
- Hadoop Distributed File System (HDFS),
- YARN (for resource management),
- MapReduce, etc.
Spark, on the other hand, is an open-source, distributed computing system that is designed for processing large-scale data sets. Surprisingly, Spark is built on top of Hadoop. Data analysis tools and techniques that Sparks offers:
- Spark SQL (for data processing SQL queries),
- MLlib,
- Spark Streaming, etc.
Types of Data Analytics Projects
There are four primary types of data analytics projects: descriptive, diagnostic, predictive, and prescriptive. Each type has its own goals and objectives. Read on to learn more about each explicitly.
Descriptive Analytics Projects
Descriptive analytics is one of the most widely used types of analytics, primarily because it conveys “what is there and what has happened.” Consequently, descriptive projects focus on using historical data and getting an understanding of trends/patterns for future use. The main goal is to gain insights into trends and patterns to help inform future decisions.
Descriptive analytics projects may include the following.
- Social media analytics for platforms like Instagram.
- Marketing campaigns’ performance analysis to study sales patterns.
- Stock market analysis.
Diagnostic Analytics Projects
As the name suggests, diagnostic analytics refers to identifying a problem and then seeking its root causes. As a result, the projects involve analyzing data to understand why something happened and what factors contributed to it.
One of the most standard applications of diagnostic analytics is in the cybersecurity domain. Cybersecurity specialists utilize the same to study data breaches and find a connection between them and security ratings.
Examples:
- Examining Market Demand
- Improving Company Culture
- Identifying Technology Issues
Predictive Analytics Projects
The subsequent step to any descriptive analytics task involves predictive analytics. The latter is all about using statistical methods and machine learning models to predict future states. Consequently, predictive analytics projects aim to use these predictions to make more informed decisions and optimize business processes.
Such projects often involve:
- Root-cause analysis: to think “why?” (implying that predictive projects also involve diagnostic analytics).
- Data mining: to find any possible correlations between data from different sources.
- Sentiment analysis: to determine the sentiment associated with the text.
Prescriptive Analytics Projects
Prescriptive analytics combines predictive analytics with several optimization techniques to recommend or “prescribe” specific tasks or remedies. These projects aim to optimize and improve business processes, resource allocation, and strategic decision-making.
These tasks are tailored to achieve the desired outcome. Prescriptive analytics is widely used for resource allocation, designing personalized marketing campaigns, energy grid management, and a lot more.
Steps Involved in Data Analytics Projects
Time needed: 1 hour
Follow these steps to solve a data analytics project:
- Defining the Problem
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?
- Data Collection and Preparation
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.
- Exploratory Data Analysis
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.
- Model Building and Testing
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.
- Model Deployment and Monitoring
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.
Importance of SQL in Data Science Projects
If you’re not familiar with how to store structured data, manage its access, and retrieve it when required, you’ll have a hard time working as a data analyst or scientist. SQL is the most famous programming language for storing structured data in relational databases (containing data in a tabular format). As data science is a field brimming with tonnes of data, SQL comes in handy in the manoeuvring of data and storing operations.
In fact, many job positions require analysts to be proficient with SQL querying and manipulation. Moreover, several big data tools like Hadoop and Spark offer explicitly designed extensions for SQL querying just because of how extensive their usage is.
Conclusion
You must now know the vitality of data analytics projects. While they are vital, driving an entire project to success can be challenging. If you need expert guidance to solve Data Science/Analytics Projects, you’ve landed at the right destination. Analytics Vidhya (AV) is a career and technology-focused platform that prepares you for a promising future in data science and analytics while integrating modern-day technologies like machine learning and artificial intelligence. At AV, we realize the importance of staying up to date with recent technologies and hence, offer comprehensive courses. To fuel your career in the domain, we provide a Blackbelt Program in AI and ML, with one-on-one mentorship. Enrol and witness the best learning experience and interview guidance.
Frequently Asked Questions
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.
