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Reading list

Machine Learning Basics for a Newbie
6 Steps of Machine learning Lifecycle Introduction to Predictive Modeling
Introduction to Exploratory Data Analysis & Data Insights Descriptive Statistics Inferential Statistics How to Understand Population Distributions?
Reading Data Files into Python Different Variable Datatypes
Probability for Data Science Basic Concepts of Probability Axioms of Probability Conditional Probability
Central Tendencies for Continuous Variables Spread of Data KDE plots for Continuous Variable Overview of Distribution for Continuous variables Normal Distribution Skewed Distribution Skeweness and Kurtosis Distribution for Continuous Variable
Central Tendencies for Categorical Variables Understanding Discrete Distributions Performing EDA on Categorical Variables
Dealing with Missing Values Understanding Outliers Identifying Outliers in Data Outlier Detection in Python Outliers Detection Using IQR, Z-score, LOF and DBSCAN
Sample and Population Central Limit Theorem Confidence Interval and Margin of Error
Bivariate Analysis Introduction
Covariance Pearson Correlation Spearman's Correlation & Kendall's Tau Correlation versus Causation Tabular and Graphical methods for Bivariate Analysis Performing Bivariate Analysis on Continuous-Continuous Variables
Tabular and Graphical methods for Continuous-Categorical Variables Introduction to Hypothesis Testing P-value Two sample Z-test T-test T-test vs Z-test Performing Bivariate Analysis on Continuous-Catagorical variables
Chi-Squares Test Bivariate Analysis on Categorical Categorical Variables
Multivariate Analysis A Comprehensive Guide to Data Exploration The Data Science behind IPL
Supervised Learning vs Unsupervised Learning Reinforcement Learning Generative and Descriminative Models Parametric and Non Parametric model
Machine Learning Pipeline Preparing Dataset Build a Benchmark Model: Regression Build a Benchmark Model: Classification
Evaluation Metrics for Machine Learning Everyone should know Confusion Matrix Accuracy Precision and Recall AUC-ROC Log Loss R2 and Adjusted R2
Dealing with Missing Values Replacing Missing Values Imputing Missing Values in Data Working with Categorical Variables Working with Outliers Preprocessing Data for Model Building
Understanding Cost Function Understanding Gradient Descent Math Behind Gradient Descent Assumptions of Linear Regression Implement Linear Regression from Scratch Train Linear Regression in Python Implementing Linear Regression in R Diagnosing Residual Plots in Linear Regression Models Generalized Linear Models Introduction to Logistic Regression Odds Ratio Implementing Logistic Regression from Scratch Introduction to Scikit-learn in Python Train Logistic Regression in python Multiclass using Logistic Regression How to use Multinomial and Ordinal Logistic Regression in R ? Challenges with Linear Regression Introduction to Regularisation Implementing Regularisation Ridge Regression Lasso Regression
Introduction to K Nearest Neighbours Determining the Right Value of K in KNN Implement KNN from Scratch Implement KNN in Python
Bias Variance Tradeoff Introduction to Overfitting and Underfitting Visualizing Overfitting and Underfitting Selecting the Right Model What is Validation? Hold-Out Validation Understanding K Fold Cross Validation
Introduction to Feature Selection Feature Selection Algorithms Missing Value Ratio Low Variance Filter High Correlation Filter Backward Feature Elimination Forward Feature Selection Implement Feature Selection in Python Implement Feature Selection in R
Introduction to Decision Tree Purity in Decision Tree Terminologies Related to Decision Tree How to Select Best Split Point in Decision Tree? Chi-Squares Information Gain Reduction in Variance Optimizing Performance of Decision Tree Train Decision Tree using Scikit Learn Pruning of Decision Trees
Introduction to Feature Engineering Feature Transformation Feature Scaling Feature Engineering Frequency Encoding Automated Feature Engineering: Feature Tools
Introduction to Naive Bayes Conditional Probability and Bayes Theorem Introduction to Bayesian Adjustment Rating: The Incredible Concept Behind Online Ratings! Working of Naive Bayes Math behind Naive Bayes Types of Naive Bayes Implementation of Naive Bayes
Understanding how to solve Multiclass and Multilabled Classification Problem Evaluation Metrics: Multi Class Classification
Introduction to Ensemble Techniques Basic Ensemble Techniques Implementing Basic Ensemble Techniques Finding Optimal Weights of Ensemble Learner using Neural Network Why Ensemble Models Work well?
Introduction to Stacking Implementing Stacking Variants of Stacking Implementing Variants of Stacking Introduction to Blending Bootstrap Sampling Introduction to Random Sampling Hyper-parameters of Random Forest Implementing Random Forest Out-of-Bag (OOB) Score in the Random Forest IPL Team Win Prediction Project Using Machine Learning Introduction to Boosting Gradient Boosting Algorithm Math behind GBM Implementing GBM in python Regularized Greedy Forests Extreme Gradient Boosting Implementing XGBM in python Tuning Hyperparameters of XGBoost in Python Implement XGBM in R/H2O Adaptive Boosting Implementing Adaptive Boosing LightGBM Implementing LightGBM in Python Catboost Implementing Catboost in Python
Different Hyperparameter Tuning methods Implementing Different Hyperparameter Tuning methods GridsearchCV RandomizedsearchCV Bayesian Optimization for Hyperparameter Tuning Hyperopt
Understanding SVM Algorithm SVM Kernels In-depth Intuition and Practical Implementation SVM Kernel Tricks Kernels and Hyperparameters in SVM Implementing SVM from Scratch in Python and R
Introduction to Principal Component Analysis Steps to Perform Principal Compound Analysis Computation of Covariance Matrix Finding Eigenvectors and Eigenvalues Implementing PCA in python Visualizing PCA A Brief Introduction to Linear Discriminant Analysis Introduction to Factor Analysis
Introduction to Clustering Applications of Clustering Evaluation Metrics for Clustering Understanding K-Means Implementation of K-Means in Python Implementation of K-Means in R Choosing Right Value for K Profiling Market Segments using K-Means Clustering Hierarchical Clustering Implementation of Hierarchial Clustering DBSCAN Defining Similarity between clusters Build Better and Accurate Clusters with Gaussian Mixture Models
Understand Basics of Recommendation Engine with Case Study
8 Ways to Improve Accuracy of Machine Learning Models
Introduction to Dask Working with CuML
Introduction to Machine Learning Interpretability Framework and Interpretable Models model Agnostic Methods for Interpretability Implementing Interpretable Model Understanding SHAP Out-of-Core ML Introduction to Interpretable Machine Learning Models Model Agnostic Methods for Interpretability Game Theory & Shapley Values
Introduction to Machine Learning Interpretability Framework and Interpretable Models model Agnostic Methods for Interpretability Implementing Interpretable Model Understanding SHAP Out-of-Core ML Introduction to Interpretable Machine Learning Models Model Agnostic Methods for Interpretability Game Theory & Shapley Values
Introduction to AutoML Implementation of MLBox Introduction to PyCaret TPOT Auto-Sklearn EvalML
Pickle and Joblib Introduction to Model Deployment
Deploying Machine Learning Model using Streamlit Deploying ML Models in Docker Deploy Using Streamlit Deploy on Heroku Deploy Using Netlify Introduction to Amazon Sagemaker Setting up Amazon SageMaker Using SageMaker Endpoint to Generate Inference Deploy on Microsoft Azure Cloud Introduction to Flask for Model Deploying ML model using Flask
Model Deployment in Android Model Deployment in Iphone

Top 10 Data Analytics Projects with Source Codes

A
Analytics Vidhya Last Updated : 02 Dec, 2024
9 min read

Introduction

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!

In this article, you will find different data analytics projects for final year students. These data analysis projects help students learn and get ready for jobs in data analytics.

Table of contents

Types of Data Analytics Projects

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:

  • Descriptive Analytics Projects
    • Focus: Understanding historical data trends and patterns.
    • Examples: Social media analytics, marketing campaign analysis, stock market trends.
  • Diagnostic Analytics Projects
    • Focus: Identifying causes behind specific outcomes or issues.
    • Examples: Cybersecurity analysis, market demand examination, technology issue identification.
  • Predictive Analytics Projects
    • Focus: Using models to predict future outcomes.
    • Examples: Predicting sales trends, sentiment analysis, root-cause analysis.
  • Prescriptive Analytics Projects
    • Focus: Recommending actions to optimize future outcomes.
    • Examples: Resource allocation optimization, personalized marketing strategies, energy grid management.

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.

Data Analytics Projects for Beginners

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.

Visual Representation of Customer Segmentation

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.

Sales Forecasting Analysis

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.

Churn Prediction Analysis

You can find churn prediction analysis source code here.

Intermediate Level Data Analytics Projects

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.

Credit Card Fraud Detection

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.

Social Media Sentiment Analysis

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.

Website User Analysis

Here is the source code for website user behavior analysis.

Data Analytics Projects Advanced Level

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.

Inventory Optimization Analysis

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.

Employee Performance Analysis

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.

Product Recommendation Analysis

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.

Supply Chain Management Analysis

You can check the source code for supply chain analytics here.

How to Solve Data Analytics Projects?

Time needed: 1 hour

Follow these steps to solve a data analytics project:

  1. 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?

  2. 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.

  3. 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.

  4. 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.

  5. 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.

Best Practices for Successful Data Analytics Projects

  • Data Quality and Integrity: Ensure data cleaning and manipulation maintain quality and completeness for accurate results.
  • Collaboration Between Teams: Foster collaboration among team members and different teams for diverse and complete analysis.
  • Communicating Results Effectively: Maintain good communication to keep everyone aligned on project goals and findings.
  • Continuous Learning and Improvement: Embrace continuous learning and improvement to enhance the accuracy and relevance of insights.

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.

Hope you find this information helpful for your data analytics projects, especially for final year students pursuing impactful data analysis projects!

To fuel your career in the domain, we provide a Blackbelt Program in AI and ML, with one-on-one mentorship. Enroll and witness the best learning experience and interview guidance.

Q1. Do you need programming skills to do data analytics projects?

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.

Q2. What are some popular tools for data analytics?

A. Some prominently used data analytics tools used are Python, R, SQL, Excel, and Tableau.

Q3. What are some good data analytics projects for the intermediate level?

A. Some good data analytics projects for the intermediate level include predicting stock prices, analyzing customer churn, and building a recommendation system.

Q4.Which project is best for a data analyst?

Top 3 Project Ideas:
Predictive: Build models to predict.
Visualization: Create clear visualizations.
ML: Apply ML algorithms to solve complex problems.

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