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Top 20 Business Analytics Project in 2024 [With Source Code]

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As a beginner in business management, one of the most crucial skills is gathering and analyzing data to make informed decisions. Business analytics uses data and statistical methods to extract insights and make data-driven decisions. The good news is that there are countless business analytics project ideas that you can start working on to improve your skills and help your business thrive. This blog will explore the top 10 business analytics projects you can do online as a beginner or an experienced professional. So, let’s dive in and discover how you can use business analytics projects to gain a competitive advantage in today’s fast-paced business world.

Why are Business Analytics Projects Important?

Business analytics is an amalgamation of business management and data analytics. High-value projects aimed at business development add value to the profile or resume of candidates who opt for a business analytics career. Business analytics projects are important because they enable data-driven decision-making, helping businesses uncover valuable insights from their data. These projects optimize operations, identify growth opportunities, and enhance overall efficiency, leading to improved profitability and competitiveness. Moreover, they provide a foundation for predictive and prescriptive analytics, enabling organizations to proactively address challenges and capitalize on emerging trends.

List of Business Analytics Projects [Based on Levels]

Here is a list of business analytics projects based on levels of experience:

Business Analytics Project Ideas : 

• Sales Data Analysis
• Customer Review Sentiment Analysis
• Market Basket Analysis
• Price Optimization
• Stock Market Data Analysis
• Customer Segmentation
• Fraud Detection
• Equity Research
• Social Media Reputation Monitoring
• Real-Time Pollution Analysis

Business Analytics Project Ideas for Beginners: 

• Employee Attrition and Performance
• Prediction of Sales in Tourism for the Next Five Years
• Prediction of the Success of an Upcoming Movie
• Prediction of the Fate of a Loan Application

Business Analytics Projects for Intermediates: 

• Creating Product Bundles
• Life Expectancy Analysis
• Building a BI app

Business Analytics Projects Topics for MBA Students

• Predicting Customer Churn Rate
• Prediction of Selling Prices for Different Products
• Store Sales Prediction

Top 10 Business Analytics Project Ideas

Here are the top 10 projects in business analytics, each offering unique insights and opportunities for data-driven decision-making in various industries. 

1. Sales Data Analysis 

It involves the analysis of data on every aspect of a company’s sales. It determines the total number of sales, average monthly sales, demographics of customers, and patterns of selling periods. It allows the company to make informed decisions to prioritize the production of specific products and scale them. To analyze the sales data, students can use different tools and languages. Students can use SQL to extract data from the database. Excel or Google Sheets can clean and analyze data for charts and graphs. For advanced visualizations and dashboards, Tableau or Power BI can be used. Python or R is good for advanced data analysis and statistical modeling, like looking for trends or making predictions.

• Sales Analysis Source Code  

2. Customer Review Sentiment Analysis

It is the process of determining the emotional state of customers after they purchase or use the products. It allows the company to realize the possible reasons for customer complaints and measures to improve the features and quality. Students can use Python or R for data analysis. Tools like TextBlob and NLTK for sentiment analysis.

• Reviews Sentiment Source Code

3. Market Basket Analysis 

It involves the analysis of the correlation between the ales of different products when combined. It helps improve the business by identifying the best combinations and increasing the preferences of customers for the products. For this project, students can analyze data using the Apriori algorithm. They can use either Python or R programming languages.

• Market Basket Analysis Source Code

4. Price Optimization 

It involves investigating historical prices, crucial price factors, the markets where the company operates (and their economic contexts), the profiles of potential clients, etc. Programming Languages like Python or R are suitable for this project. Regression analysis and demand forecasting models are used to analyze the data.

• Tensor House Source Code

5. Stock Market Data Analysis

The project involves determining the frequency of rise and fall in price, the general trend of average monthly closing prices over the year, and trading volumes. Candidates can select a specific dataset and explore the company’s stock performance history. To analyze the data for this project, Python and R is used. Tools like Pandas and Numpy are used for manipulating the data.

• Stock Market and Analysis Source Code

6. Customer Segmentation

It refers to categorizing a company’s clients into different groups based on their purchasing behavior, financial level, interests, needs, and loyalty to the business. It helps optimize marketing campaigns and maximize the profits from each client. The K-means and Hierarchical clustering algorithms are generally used for this project.

• Customer Segmentation Source Code

7. Fraud Detection

Credit card fraud, identity theft, and cyber-attack are common fraudster challenges faced across various industries. Projects on fraud detection involve choosing a dataset and running statistical analyses to identify fraudulent operations. Machine learning algorithms such as decision trees and logistic regression are used for fraud detection.

• Fraud Detection Source Code  

8. Equity Research

Equity is the value of the returns received by a company’s shareholders after liquidating all the assets and clearance debts incurred by the company. Equity research plays a crucial role in the successful run of both shareholders and companies. Students can use Excel and Python to analyze the financial datasets for this project. Tools such as ratio analysis and financial statement analysis are in equity research.

• Equity Research Source Code

9. Social Media Reputation Monitoring

It is the process of gauging the presence and influence of a brand on customers through social media. Using analytical tools and techniques, the project audits, monitors, and interprets social media users’ opinions about the products. It helps revise social media marketing strategies to promote the business. Social media monitoring tools such as Hootsuite and Sprout Social are used to analyze the data.

• Social media reputation monitoring

10. Real-Time Pollution Analysis

It is a typical data visualization project, allowing the candidates to learn univariate and multivariate data analysis. The methodology can be reproducible to business aspects. Students can use either Python or R to build the project. Matplotlib or Plotly are used for creating visualizations.

• Air Pollution Tracker Source Code

Business Analytics Projects for Beginners

Graduates from several fields, including engineering, with an inclination for business, choose management as their career path. Business Management for beginners , augmented with business analytics projects, provide potential platforms to lay a strong foundation to build their career. The following are the most-edifying sample business analytics projects for students.

1. Employee Attrition and Performance

These projects are ideal for acquiring the qualitative analysis skills of employee attrition to find answers for the event’s who, when, and why. They also predict quantitative aspects of human resource dynamics for the organization’s next 5 to 10 years. The balance between attrition and retention is the secret to optimal human resources and talent utilization. To do this, students can use Excel to clean the data. SQL is used for data extraction. Python or R for data analysis.

• Employee Attrition Performance Source Code  

2. Prediction of Sales in Tourism for the Next Five Years

This project helps business analysts to improve their skills in applying data mining to determine patterns and correlations among tourism packages and their preferences. It has two approaches: qualitative and quantitative. Both approaches help beginners to hone their analytical and judgmental skills. To predict sales, statistical analysis tools like R or Python are used. Excel and SQL are used for cleaning and extracting data, respectively.

3. Prediction of the Success of an Upcoming Movie 

Business management professionals have a good scope in the film industry as numerous films enter the screen. These projects involve forecasting success based on the analysis of variables, including genre, language, directors, actors, actresses, budget, locations, etc. The prediction depends on the model devised based on the data of predetermined variables associated with previously released movies against their success. Like the other projects, students can use Python or R to predict the success of the upcoming movie.

4. Prediction of the Fate of a Loan Application 

These projects expose beginners to several machine-learning tools and techniques, and datasets. They also introduce the candidates to various parameters and help them gain the ability to recognize variables under eccentric circumstances. The top 3 machine-learning solution approaches for loan prediction are as follows.

• Support vector machine 
• Random forest

Pandas are the most straightforward and powerful Python libraries for beginners used for the prediction of the fate of loan applications.

Business Analytics Project Ideas for MBA Students

ECBA certificate training is among the best options to improve the profile of business analytics aspirants. A merit of this program is the opportunities for business analytics projects for MBA students. Three top business analytics project ideas are as follows.

1. Predicting Customer Churn Rate

It involves predicting the decline of customer rates. It has scope for stakeholders to identify setbacks in the business. It helps learn several statistical tools, such as SHAP (Shapley Additive exPlanations), RandomSearch, and GridSearch, for univariate and multivariate analysis on a retrieved dataset.

• Customer Churn Analysis Source Code

2. Prediction of Selling Prices for Different Products

It refers to the determination of the price of a product that attracts customers with an optimal profit margin. Further, it also helps companies to determine the offers to improve business. These projects help acquire skills to employ machine learning algorithms like Gradient Boosting Machines (GBM), XGBoost, Random Forest, and Neural Networks that use different metrics to test each of their performances.

3. Store Sales Prediction

These projects involve working with numeric and categorical feature variables and performing univariate & bivariate analysis to find the redundancy in variables associated with the store chain of a company. They help the candidates learn machine learning models such as the ARIMA time series model. 

• Store Item Demand Forecasting Source Code

Business Analytics Project Topics for Intermediate

Business analytics project ideas for experienced professionals should involve a complex combination of statistical parameters and real-world scenarios to enhance their skills significantly. Following are the business analytics project examples suitable for the intermediate levels.

1. Creating Product Bundles

It is a method that combines different products from the same company and sells them as a single unit. Under these projects, candidates learn market basket analysis and time series clustering methods to identify product bundles using sales data.

Here is the Product Bundle Source Code  

2. Life Expectancy Analysis

These projects aim to determine the monetary value of the potential consumer of the products and services of a company. Traditionally, government organizations utilize life expectancy analysis to determine the correlation between life expectancy and a nation’s GDP.

• Life Expectancy Analysis Source Code  

3. Building a BI app 

Business intelligence apps or tools play a critical role in finding urgent solutions to issues that are high for the business. Low to no-code custom apps for decision-making and long-term strategies are invaluable for an organization.

Here is the Business Intelligence Analysis Source Code  

Key Tools for Business Analytics Project

Here is a list of top tools that are required business analytics projects: 

• Data Visualization Tools (e.g., Microsoft Power BI, Looker) 
• ETL/ELT Tools
• Data Warehousing (e.g., Amazon Redshift, Google BigQuery, etc.)
• Data Analysis and Manipulation Tools
• Data Mining and Machine Learning Tools (e.g., Scikit-learn, RapidMiner)
• Data Quality Management Tools
• Data Integration Tools
• BI Suites 
• Data Catalog Tools (e.g., Collibra)

Are Business Analytics Projects Difficult to Complete?

Business analytic projects face several challenges that hamper their successful implementation. Technological advancement expands the options for tools and techniques. Still, they create a grey zone wherein the new tools emerge with overlapping functionalities interfering with decision-making. Other reasons for the failure of business analytics projects are: 

• Lack of well-defined and explicit goals 
• Poor data integration 
• Lack of conversion of insights and outcomes into actions.
• Poor adaptations to the ongoing development

Final Thoughts

Business analytics is blooming parallel to technological advancements, and every business is leveraging analytical tools and techniques to optimize its actions. Whether experienced or fresher, diverse business analyst projects for resume help you upgrade your profile. KnowledgeHut Business Management for beginners is highly recommendable for a firm foundation before undertaking business analytics projects, as it provides top-quality augmentation to your aptitude for the discipline.

Frequently Asked Questions (FAQs)

The common challenges faced in business analytics projects are: 

• Changing requirements or business needs 
• Conflicts with stakeholders 
• Poorly documented processes 
• Unrealistic timelines. 

Predictive analytics is a branch of analytics that predicts future outcomes using models based on historical data. Businesses use customer data and transaction information to predict the performance of the products and make strategies to optimize profits. 

Popular business analytics tools are SAS business analytics, Sisense, Microstrategy, KNIMETIBCO Spotfire, Tableau big data analytics, Power BI, and Excel.

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5 Best Business Analytics Projects for Your Resume (Updated for 2024)

Introduction.

Expected to exceed $23 billion in 2032, analytics as a service (AaaS) and business analysis are essentially about extracting patterns—and ultimately monetary value—from user data. As a skilled business analyst, your job is to improve decision-making, analyze new products, enhance customer experience, optimize processes, and manage risk in more sensitive cases.

When approaching a business analytics interview, it’s imperative to recognize that your responsibilities will include understanding the business, identifying requirements, communicating, and sometimes, making presentations and managing.

These responsibilities are only given to candidates who demonstrate competence with existing business analytics projects.

However, we understand how challenging it is to find and get started on an analysis project to add to your resume. So, we’ve done the hard part for you.

We’ve compiled the 5 best business analytics projects that have the most detailed and realistic datasets.

Market Basket Analysis

What is this project about.

Cross-selling and upselling are powerful tools used in retail to maximize sales potential and increase customer lifetime value. Market basket analysis strives to identify distinct patterns of items frequently purchased together.

This type of analysis is often applied by retail companies, such as Target and Best Buy, and e-commerce industries, like Amazon.

This particular project uses Instacart’s anonymized data on customer orders over time to allow you to predict which previously purchased products will be in a user’s next order.

The project involves taking account of customer demographics, order history, and product information to build a machine-learning model that accurately predicts patterns.

As a business analyst, your goal is to optimize the company’s revenue and profit potential. For example, you might identify that customers often purchase butter and bread together. With this insight, the store could place these items together or offer bundle discounts to increase sales.

Further, by analyzing which products are often bought together, the store can optimize inventory levels and product placement to ensure availability and minimize stockouts.

Why is it a must for your resume?

Successful completion of the project will highlight your proficiency in data manipulation, advanced data mining, and data visualization techniques. The specific skills will depend on your chosen tools, but SQL, pandas, Apriori, and Power BI/Tableau are likely to be involved.

Mastering these skills is essential for extracting valuable insights from data, which can then be applied to enhance product placement, promotions, and overall sales—all outcomes that are highly valued by companies like Amazon and Best Buy.

Sales Forecasting with Python

Sales forecasting leverages past sales data, market trends, and economic indicators to estimate future sales for a specific period, such as a month, quarter, or year.

Accurate sales forecasting can help businesses make better profits, optimize resource allocation, improve inventory management, and set realistic sales targets.

This Python project dataset , sourced from a retail store, expects you to build and evaluate models to predict national store sales by holidays and department, focusing mainly on the seasonal nature of the business.

An example of the project’s significance could involve an automotive company like Ford, which can successfully leverage sales forecasting to predict future demand for a particular car model, optimizing production schedules and reducing unsold units.

Another industry where sales forecasting is heavily used is insurance sales forecasting, where companies promote insurance policies based on demographic trends, the economy, and regulatory changes.

For instance, favorable regulatory changes can cause an upsurge in the number of young drivers, which may lead to an increase in car insurance sales.

As sales forecasting is key to every industry, finding room for this project in your resume can prove to be significant.

This project allows you to showcase expertise in time series analysis and forecasting using statistical models, such as ARIMA and machine learning.

It highlights your ability to transform historical data into accurate predictions, leading to improved business efficiency and decision-making. This skill set is highly valuable for roles requiring strategic forecasting and analysis throughout the market.

Sentiment Analysis of Customer Reviews

Understanding human sentiments is critical to industries that rely heavily on customer feedback, preferences, and behavior to recommend products or improve their services.

Sentiment Analysis of Customer Reviews focuses on determining the emotional tone behind text using natural language processing, whether positive, negative, or neutral.

This project utilizes a dataset containing user ratings for various products to create a model that can suggest items to users based on their purchase history and preferences. You’re expected to build a model using item-to-item collaborative filtering, a technique commonly used by e-commerce platforms like Amazon.

Apart from the cited example of Amazon on the business analytics project, you could consider a telecommunication company, say, Verizon. They might segment the customers based on their usage and satisfaction patterns to recommend personalized offers.

Social media platforms like Facebook or Twitter also use sentiment analysis to track public opinion on various topics, including brands, products, and events, which gives them leverage to recommend relevant content.

Sentiment analysis is considered pretty essential in customer-facing products such as Netflix, Spotify, and YouTube that strive to deliver personalized feeds to cater to each user.

This project showcases your skills in text mining, web scraping, and data processing with Python and pandas, along with expertise in machine learning for natural language processing.

It also allows you to demonstrate your ability to manage unstructured data from various sources, extract insights, and provide actionable recommendations.

Price Optimization Analysis

Pricing is a considerable factor in today’s competitive markets with global players. Price optimization analytics uses data and analytical techniques to identify a model that dynamically adjusts the price of a product to maximize revenue and profit.

This ClearSpark takehome challenge expects you to create a recommendation engine that involves handling missing values, processing data for analysis, understanding patterns, and building a recommendation model to facilitate the recommendation algorithms.

One of the most prominent use cases of price optimization involves ride-sharing companies such as Uber and Lyft. They use dynamic pricing models to adjust fares based on demand, traffic, and supply.

Retailers, such as Amazon and Walmart, also leverage vast amounts of data to adjust product prices in real time.

Airlines, for instance, dynamically adjust ticket prices based on factors like booking time, demand, competition, and fuel costs.

Price optimization analytics uses regression analysis to study historical data and understand pricing patterns. This project shows your ability to perform detailed analysis and model pricing scenarios, combining analytical skills with business understanding.

It also demonstrates your ability to use data to influence decision-making, including setting prices based on customer segments and planning promotions. Tech companies and industries focusing on pricing strategies value this experience, as it helps maximize revenue and profitability.

Life Expectation Analysis

Life expectancy analysis is about studying and interpreting data related to the average lifespan of a population. By examining age, race, sex, income, education, access to healthcare, and various other factors, analysts can design models that can accurately describe the life expectancy of a demographic.

This project , while not directly correlating with business analytics, has you identify key factors influencing life expectancy, clean data, build statistical models, and evaluate the performance of the ML models. Data visualization of the outcomes can also significantly enhance your resume.

The most substantive industrial use of life expectancy analysis revolves around determining insurance premiums and performing risk calculations against health insurance policy applications. Other examples include identifying potential target populations for new drugs and detecting regional and global health trends.

Apart from direct healthcare, non-profit organizations and epidemiological studies may employ life expectancy analysis to identify areas of interest.

Completing this project showcases your skill in handling diverse datasets from various sources, such as mortality rates, health indicators, and demographics. It highlights your expertise in statistics and domain-specific models to analyze data, identify trends, and draw meaningful conclusions.

Moreover, it demonstrates your proficiency in data exploration, regression analysis, and predictive modeling, making you a valuable asset for strategic planning and policy-making through data-driven insights.

Tips to Add More Business Analyst Projects to Your Resume

If you’re interested in expanding your resume with more business analytics projects or seeking machine learning project ideas , check out our articles. Still, if you can’t find a project that fits your interests, don’t hesitate to create your own, following the tips below:

Find Interesting Business Analyst Projects

Don’t stop at online projects!

Identify a question or problem you’re passionate about solving, one that addresses a common business need, possibly in the particular company you’re interested in.

By pursuing and completing a project relevant to a company’s challenges, you demonstrate how your skills are applicable in real-world scenarios, making your skills more relevant to potential employers.

Come Up with Creative Solutions

Once you’ve decided on a problem, think creatively about how to solve it. The project should highlight your unique approach and analytical skills. The more innovative your solution, the more likely it is to grab an employer’s attention.

For example, if you’re interested in real estate, you might consider projects that will help a company decide which city to expand into by analyzing market demand, property prices, rental yields, and economic conditions.

Practice Takehome Projects

If you need more practice, we offer takehome projects that allow you to tackle real-world problems and sharpen your skills in analytics, machine learning, and statistics.

Take on Volunteer Projects

Another great way to build your portfolio is by taking on volunteer projects. Non-profits and small businesses often need help with data analysis but don’t have the resources to hire full-time business analysts. By offering your skills, you gain valuable experience and help a good cause.

The Bottom Line

And there you have it—the top 5 business analytics projects you can use to highlight your skills and boost your resume. Remember, every project you take on brings you one step closer to mastering the skills top employers seek. Showcasing these projects can set you apart in today’s competitive job market.

Top 13 Business Analytics Projects To Enhance Your Resume & Portfolio

Introduction

The fascinating world of data and AI has brought forth many scientific tools, algorithms, processes, and knowledge extraction systems to identify meaningful patterns from structured as well as unstructured data. The boom in data analytics in the last couple of years is only growing and will reach the next level with so many innovations in the artificial intelligence domain.

If Data Analytics is something you fancy and want to get a solid foundation on this topic, then you must have a portfolio of data analytics projects to showcase. If you are wondering how to start with data analytics, we have here data analytics project ideas that are good for beginners as well as those who are in intermediate or higher levels. If you are a student, then our ideas could also be used for data analytics projects for students.

Why Data Analytics Projects?  

Data Analytics projects are the best way for aspiring Data Science professionals to gain hands-on experience. In these projects, you will get to deploy various Data Science and Machine Learning algorithms in real-world scenarios to uncover connections between data points and understand how different variables may impact each other. The more you practice on Data Analytics projects, the stronger you build your portfolio to showcase your expertise to potential employers.  

Tips To Include Data Analytics Projects To Enhance Your Resume  

Showcasing all the Data Analytics projects you’ve worked on can help you create a resume that distinguishes you from other candidates with similar job experiences and academic credentials. Below are some tips for including Data Analytics projects on your resume.    

• Adjust As Per The Job Description : Go through the job description keenly, identify what skills the recruiting manager is looking for, and then choose relevant projects that demonstrate your abilities in those areas.  
• Highlight Under A Separate Project Section: If you have worked on a diverse range of projects, try including a separate projects section on your resume to showcase them. Alternatively, you can try including projects in the job experience or education sections.  
• Adding A Link To Your Portfolio: You might provide a link to your portfolio and your contact information to urge hiring managers to look into other projects you’ve worked on.   

Data Analytics Projects (Easy, Medium, Hard)

To get started with data analytics project topics, you would first need to understand what level you are comfortable in and then decide whether you want to get on with data analytics projects for beginners, intermediate, or higher levels. Let us take a look at what it entails to do a project in these 3 levels:

• Beginner level – If you are someone who is just starting with data analytics, you must go through the data analytics project examples in the beginner section. These projects do not employ heavy application techniques, and their simple algorithms would let you move forward smoothly.
• Intermediate – Here, medium to large data clusters are taken and need you to have a sound foundation of data mining projects along with machine learning techniques. If this is something you are well-versed with, then you can work on the projects outlined in the intermediate section.
• Expert – This section is for industry experts where neural networks and high-dimensional data are worked with. If you have the blend of creativity and expertise required for such projects, then the data analytics mini project in the advanced section is for you.

Easy or Beginner level projects

Intermediate level projects, advanced level project.

• Fake News Detection – If you know python, then you could develop this data analytics project in python which can detect a hoax or false news that is generated to fulfill some political agenda. This news is propagated through social media channels and other online media. The model is built using the python language, which can accurately detect the genuineness of a news item. You could use a PassiveAggressiveClassifier to build a TfidfVectorizer, which can classify news into “fake” or “real.”
• EDA or Exploratory Data Analysis Project – This is the first thing a data analyst needs to do as part of their job. In this project, we look into data to recognize and identify patterns. Using data modeling techniques, you can summarize the overall features of data analysis. EDA could be done with or without the help of graphics. You could also use univariate or bivariate quantities to perform EDA. The IBM Analytics community is valuable if you want to delve into an EDA project.
• Sentiment Analysis – This analysis is used widely in online communities for brand reputation management or to perform competitor analysis using the R framework. This data analytics project in r will try to understand the opinions and sentiments of viewers based on the words they use. In this classification, classes are either binary (positive or negative) or multiple (happy, angry, sad, confused, disgusted, etc.). You could use the “Jane Austen” package with a relevant dataset. Using general-purpose lexicons like bing, Loughran, and AFINN and performing an inner join, you could build a word cloud for the final display of the data analytics project report.
• Colour Detection Project – This is a good data analytics project for students where they can build an interactive app to detect the selected color from an image. Many of us can not recognize or remember the name of color since there can be around 16 million colors based on RGB values.
• Forecasting Sales for a New Car Design – This project requires a thorough examination of consumer needs and desires. You can work on a project to see if a new automotive design, color, or form will appeal to the target demographic. There are numerous autos on the market to assist you in determining the most popular vehicle.
• Predicting a Product’s Success – In this project, you will use your analytical skills to analyze whether a specific product will sell well in a given market. You may, for example, concentrate on the entertainment industry. With thousands of hours of content distributed daily, determining which music or movie will do well is difficult. To make forecasts, you will need to leverage previous data and models.
• Insights From Employee Performance and Resignation Statistics – In this assignment, you will provide statistics to a corporation that can explain why employees depart. The intent is to use these insights to improve the company environment. You can consider the employee’s proximity to home, work culture, or job description. It would be best to weigh each element concerning the likelihood of resignation.
• Chatbots – Chatbots are an extremely useful tool in businesses as the huge surge of customer queries and messages can be handled by chatbots without slowing down business. Artificial Intelligence, Data Science, and Machine Learning are the three pillars of designing a chatbot. Chatbots can be trained using recurrent neural networks and intent JSON datasets. The main implementation could be done in python.
• Handwritten digit recognition – Machine learning enthusiasts widely use the MNIST datasets of handwritten digits. You use convolutional neural networks and do the real-time prediction of digits drawn on a graphical user interface.
• Gender and Age detection – You can build this interesting data analytics project in python which can predict gender and age by analyzing just one image. To do this project, you would need to know about computer vision and its principles.
• Movie recommendation system – The concept of recommending movies is complex and is based on the abstract click method. It requires a huge implementation of machine learning and accessing humungous datasets that include users’ movie browsing history, preferences, etc. You would need to use collaborative filtering to get a hang of user’s behavior and the R Framework, along with the MovieLens dataset, is a good fit for such projects. To channel through the datasets, you could use surprise model selection and matrix factorization too. Brands like NetFlix use this method, and is a lot of grueling work even for industry experts.
• Credit Card Fraud Detection – Another data analytics project in r will need you to work with decision trees, gradient boosting classifiers, logistic regression, and artificial neural networks. By using the card transactions dataset, you can classify transactions on a credit card into fraudulent or genuine categories.
• Customer Segmentation – This is one of the most popular data analytics projects for companies as they need to create various groups of customers at the beginning of any of their campaigns. This project is an implementation of unsupervised learning and uses clustering to identify different segments of customers so that companies can target the customer base they need to. Customers are divided into groups based on age, gender, preferences, spending habits, etc. This is done to market to each group more effectively. You can use K-means clustering and visualize gender and age distributions.

We know that finding a perfect idea for your Data Analytics project could be more daunting than actually working on the project. We hope the above-mentioned Data Analytics Project ideas will be just the inspiration you’re looking for. The bottom line is that Data Science has high growth potential and continues to increase, promising in-demand opportunities for people proficient in the subject. Including projects on your resume is a definite way to make it stand out.  

Finding the right place to learn and become proficient in all these skills and languages is also important. UNext, recognized as one of the Top 10 Data Science Institutes in India, is the right place for you. UNext in collaboration with IIM Indore, offers the Integrated Program In Business Analytics for enthusiasts in this field. The course runs for 10-month and is conducted live online to aid interested learners in mastering the tricks and trades of the domain.

• Role of Business Analyst: Key Responsibilities of a BA

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