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Data Science Projects: Beginner to Professional

Landing on our data science projects page suggests that you are likely a data scientist or are yearning to become one, and you have started looking for a job in this domain. Getting a data scientist job may be challenging, and you may be applying to many data scientist jobs before securing one. Instead of learning theoretical concepts in data science and machine learning, working on live data science projects can smoothen your job application process. 

Achieve your goals with ProjectPro’s data science projects with source code

Suppose you are learning the concept of classification. Then, more than just reading and understanding the theory will be needed. You should pick up a small dataset and build a classification model using Python, R, or other programming languages. Even if you learn advanced concepts in theory like Stochastic gradient or gradient descent, would you ever know how and when to apply these concepts unless you practice them? Our panel of industry experts strongly suggests that working on real-time data science projects can help professionals looking for a job in data science. Having industry-relevant projects on data science on your data science resume will help you showcase your skill set to the interviewers.

 

Working on Data Science and Machine Learning Projects from ProjectPro's repository will help you master data science skills and help sharpen them. Implementing multiple data science project ideas will give you hands-on experience solving real-world problems by applying these skills. Designed by industry experts, ProjectPro's data science projects for beginners are a great way to kickstart your journey to become a data geek and build a marketable data science portfolio.



Beginner's Guide to ProjectPro's Data Science Projects with Source Code

The first step is the most crucial one in any journey. If you are a Data Science enthusiast who wants to build a career in the tech world by implementing Data Science projects in Python or Data Science projects in R, then you are on the right page. ProjectPro is the perfect repository of Data Science projects to help you realize your goal of becoming a Data Scientist.

ProjectPro’s Guide to Data Science Projects

Whether you are a beginner or a professional, we assure you that you will thoroughly enjoy going through our end-to-end solutions for practical Data Science Projects. Through our detailed videos and source codes, ProjectPro will help you build your Data Science portfolio, which you can use to land your dream job.

Here is a simple step-by-step guide to commence your journey as a newbie on the ProjectPro platform. Explore the three sections, highlighting a list of projects in data science that you can refer to depending on your expertise in this exciting domain.

Data Science Projects for Final Year Students/ Beginners

As a data science beginner or a student, it can be challenging to assess the best data science projects that should be tried first and which projects should be put on the back burner. We have listed the top data science projects for students and beginners to make learning data science easy. The prime advantage of these data science mini-projects for beginners is that each one is a complete full-stack data science problem. Below, we have listed data science projects for freshers to use as their first data science project.

 

1) BigMart Sales Prediction

This Python project for data science will guide you through the art of predicting sales of stores like BigMart using machine learning algorithms like Random Forest, boosting, neural networks, MLP regressor, linear regression, and Bayesian Model. You will also learn how to compare the performance of these models and prepare an end-to-end data science project report.

 

Source Code: Data Science Project in Python on BigMart Sales Prediction 

2) Customer Churn Prediction

No company wants its customers to have a bad experience with its services, and to avoid that, companies analyze customer feedback to prevent them from churning away. This project is about understanding how to use customer details to analyze the probability of unsubscribing from a telecom company's services. The project solution uses multiple machine learning algorithms to evaluate the probabilities and analyze their performance using various statistical parameters.

 

Source Code: Build a Customer Churn Prediction Model using Decision Trees 

3) Sales Forecasting 

Running a chain of stores is challenging, and one needs to ensure they have enough inventory to meet the varied demands for different products at different locations. Using machine learning algorithms for sales forecasting can assist in developing efficient business models. In this project, you will use random forest algorithms and the Arima model to estimate the sales of 45 Walmart stores.

 

Source Code: Machine Learning Project-Walmart Store Sales Forecasting

4) Loan Prediction

By analyzing historical data and customer attributes, this project predicts whether a loan applicant will likely default or repay the loan, assisting financial institutions in making informed lending decisions and managing credit risk effectively.

 

Source Code: Loan Prediction using Machine Learning Project Source Code 

5) Customer Segmentation 

This project divides customers into distinct groups based on similarities in their purchasing behaviors and demographics, aiding businesses in tailoring marketing strategies and product offerings to specific customer segments.

 

Source Code: PyCaret Project to Build and Deploy an ML App using Streamlit 

6) Insurance Prediction

Data Science can help insurance business stakeholders foresee the claims they are likely to receive. In this project, you will use an insurance company's data and ensemble machine learning algorithms to estimate the number of possible insurance petitions after an unfortunate event.

 

Source Code: Insurance Pricing Forecast Using XGBoost Regressor

7) Uber Data Analysis

The Uber Data Analysis Project uses machine learning techniques to analyze ride data, uncovering patterns and trends to optimize driver allocation and improve service efficiency. By exploring factors such as rider demand, traffic patterns, and geographical trends, the project aims to enhance the overall user experience and operational performance of the Uber platform.

 

Source Code: End-to-End Uber Data Analysis Project Using Machine Learning

8) Predicting Passengers' Survival on Titanic

Social interactions are fundamental to the decision-making capabilities of humans. In Titanic, many believed certain people had a higher probability of survival because of their social status. In this Python project for data science, you will use logistic regression to predict the chances of survival of various passengers.

9) Data Visualization

This project presents data in a visually appealing and comprehensible manner through charts, graphs, and interactive dashboards, enabling stakeholders to gain valuable insights and make more effective data-driven decisions.

10) Exploratory Data Analysis

This project involves analyzing datasets to summarize their main characteristics, often through visual methods, to understand the underlying patterns and relationships within the data before applying more advanced analytical techniques.

Intermediate-level Projects in Data Science

If you are a data science enthusiast who has worked on simple data science project examples and is interested in exploring more challenging problems, refer to the projects below.

11) Speech Emotion Recognition

This end-to-end data science project aims to use Artificial Neural Network (ANN) to determine a human's emotion through their speech audio files. You will implement the solution in Python using the TensorFlow framework to build the layers of an ANN. Additionally, the project will help you understand how to extract features from audio data.

 

Source Code: Speech Emotion Recognition using ANN 

12) Recognizing Human Activity

This project in data science involves building a model that can classify six human activities (walking, walking upstairs, walking downstairs, sitting, standing, and laying) by analyzing smartphone-sensors data. You will use the PCA algorithm to deduce the essential features from the dataset and then feed them to an artificial neural network to solve the classification task.

13) Face Recognition System

In this Data Science project, ProjectPro experts will introduce you to Python libraries like OpenCV and FaceNet, which you will use to make a face recognition system in Python. You will learn to use Haar Cascade classifiers to implement the face recognition code.

 

Source Code: Building a Face Recognition System Data Science Project

14) Image Segmentation using Deep Learning

Here is a data science project in Python that will help you understand the application of data science tools in the medical industry. This project will introduce you to the application of the Convolutional neural network model, VGG-16, in detecting tissue growths called Polyps from colonoscopy videos.

Source Code: Medical Image Segmentation Deep Learning Project

Real-World Data Science Projects for Advanced Professionals

As a professional, try the following advanced data science projects to enhance your knowledge in this domain further. 

15) Credit Card Fraud Detection

This project is the perfect data science project for beginners as it will introduce them to the basics of machine learning and its various types. You will understand how to solve a simple classification problem using a biased dataset. In this project, you will use machine learning algorithms, including Random Forests, K-Nearest Neighbour, and Logistic Regression. You will also learn to analyze their performance using statistical metrics like Precision, Recall, Accuracy, etc.

 

Source Code: Credit Card Fraud Detection Project

16) Market Basket Analysis

Analyzing customers' preferences is crucial to the growth of any business. In this project for data science, you will predict which products a customer is likely to buy along with specific products and help companies develop better product placement strategies. You will use Apriori and Fpgrowth algorithms to perform the analysis in Python.

 

Source Code: Market Basket Analysis using apriori and fpgrowth algorithm

17) Taxi Demand Prediction

Taxi, taxi! One no longer needs to scream their lungs to get a ride because of the technological solutions provided by companies like Ola and Uber. In this project, you will learn how to solve a supervised learning problem using the random forest machine-learning algorithm. The goal is to predict the ride requests for Ola's bike drivers. 

 

Source Code: Taxi Industry Analysis - Ola Bike Rides Request Demand Forecast 

18) Recommendation Systems

A notification on your phone from an application containing incredible recommendations for you will likely gain your attention. That is why businesses are keen on developing efficient recommendation systems. In this project, you will learn about recommendation systems and their different types: rule-based, collaborative-filtering-based, content-based, market-based, hybrid, etc. 

 

Source Code: Recommender System Machine Learning Project for Beginners 

19) Object Detection in Python

Suppose you are looking for a data science end-to-end project in Python to help you understand the advanced application of deep learning algorithms in the medical industry. In that case, this is the project for you. You will use the detectron2 model to build an image segmentation system that detects inhibition zones (the areas without bacterial growth) in the input images. 

 

Source Code: Detectron2 Object Detection and Segmentation Example Python

Different Types of Projects for Data Science

A dreadful challenge lies ahead to land a top gig as a data scientist. You must master diverse data science skills, ranging from machine learning to business analytics. However, the rewards and perks are worth it. If a data scientist career is your calling, bookmark this page to explore novel and interesting data science project ideas compiled by our industry experts to hone impactful data science skills. Below, you will find data science projects categorized based on which subdomain of Artificial Intelligence they belong to.

Machine Learning Projects in Data Science Projects with Source Code

This list contains Python machine learning projects for beginners with source code in Python and R.  If you are preparing for a machine learning interview, practice at least two of these project ideas.

Machine Learning Projects with source code

1) Retail Price Optimization

This project is one of the most interesting and exciting, with Python as its programming language. In this Python data science project, you will learn how to optimize the prices of luxury items by using the price elasticity of demand.

Source Code: Retail Price Optimization Machine Learning Project

2) Sales Forecasting

Rossmann Store is a drugstore chain in Europe. In this Data Science project, you will use the store's customer data to forecast sales for the chain's stores in Germany. It is one of the simplest projects in machine learning. So, make sure you add it to your data science project portfolio.

 

Source Code: Sales Forecasting for Rossmann Stores

3) Price Recommendation for Retail Stores

In this Data Science end-to-end project, you will use the machine learning data obtained through a Japanese app, Mercari, to predict the prices of the products listed on the app.

Source Code: Recommender System for Retail Stores

4) Predicting Avocado Prices

This project is one of the most fantastic Python data science projects you will ever work on. In this Data Science project, you will learn how to predict the average price of Hass avocado to help farmers of Mexico estimate the expansion of avocado farms.

 

Source Code: Avocado Price Prediction

5) Ultrasound Nerve Segmentation

If you have a thing for Neuroscience and want to work on a data science project using Python, then this project is a must. In this project, you will build a model to identify nerve structures in a dataset of ultrasound images of the neck.

 

Source Code: Ultrasound Nerve Segmentation Machine Learning Project

6) Breast Cancer Classification

Using machine learning algorithms, this project categorizes medical imaging data to identify whether a tumor is benign or malignant, assisting healthcare professionals in accurate diagnosis and treatment planning for breast cancer patients.

NLP Projects in Data Science Projects with Source Code

Below are a few of the best NLP Projects with source code, where NLP stands for Natural Language processing. These NLP project ideas will help you upgrade your skills for data science projects that utilize text datasets. If you are particularly interested in NLP Projects in Python with code, get excited because your search is finally over.

NLP Projects with source code

7) Building a ChatBot  

Chatbots are the need of the hour for any website, as they are interactive, communicate effectively, and are time-saving. Work on this project to learn how to build a chatbot for an eCommerce website. This NLP project is a must for all Data Science enthusiasts. In this NLP end-to-end project, you will learn to build a ChatBot in Python.

 

Source Code: Building your own ChatBot

8) Analyzing Customer Reviews

In this NLP project, you will learn to use the K-means clustering machine learning algorithm to group customer reviews through topic modeling.

 

Source Code: Clustering Customer Reviews

9) Building a Resume Parsing Application

This Data Science project is the most practical NLP project in Python. It will guide you to build a Resume classifier application using the Spacy NLP Python library. Recruiters and various HR Managers can use the resulting application to save time.  

 

Source Code: Resume Parsing Application System

10) Similar Quora Questions

This project is one of the most popular Natural Language Processing Projects, and the website Quora launched a competition around this. In this NLP project, you will learn how to pair questions (asked on the website) with similar content so that their readers can access all relevant answers.

 

Source Code: Pairing Similar Quora Questions

11) Sentiment Analysis

This project will be perfect if you are searching for NLP mini projects with source code. In this project, you will investigate people's sentiments about movies based on the reviews they submit.

 

Source Code: Analyzing Customers' Sentiments 

12) Fake News Detection

The Sequence Classification with LSTM RNN in Python project uses recurrent neural networks to classify news articles, distinguishing between genuine and misleading information. By analyzing the sequential nature of textual data, the project aims to enhance media literacy and combat the spread of fake news, contributing to a more informed and trustworthy information ecosystem.

Source Code: NLP and Deep Learning For Fake News Classification in Python 

Deep Learning Projects in Data Science Projects with Source Code

In this subsection, you can see data science projects that use deep learning algorithms. This list includes simple neural network projects with source code, as the neural networks algorithm is one of the most popular algorithms used in deep learning data science projects. Most of these projects will be data science projects with source code in Python because of the popularity of Python among Deep Learning users.

Deep Learning Projects with source code

13) Optical Character Recognition 

In this Data Science Project, you will use YOLO v4 and Google Tesseract to design your custom OCR Application from scratch in Python.

 

Source Code: Optical Character Recognition Application 

14) Look-Alike Modelling in Python Project

In this Data Science project, you will work with deep learning algorithms to identify similar images. You will use locally sensitive hashing to search for customers more likely to click on an advertisement.

 

Source Code: Look-Alike Modelling

15) Forest Fire Prediction

This project analyzes environmental and weather data and implements Image Segmentation using TensorFlow to predict the likelihood of forest fires occurring in specific areas. The solution aids forest management agencies in implementing preventative measures and mitigating the risk of wildfires. This data science project is one of the most challenging Python projects from the ProjectPro repository.

 

Source Code: Image Segmentation with TensorFlow

16) Handwritten Digit Recognition Project 

MNIST is one of the most popular datasets in Deep Learning. This data science project will train a convolutional neural network to distinguish between handwritten digits. To explore neural networks, refer to Neural Network Project Ideas.

 

Source Code: Digit Recognition

 

The list now presents you with two data science real-time projects, meaning the project solutions are usually deployed on live data streams for immediate analysis and action.

17) Traffic Signs Recognition

This real-time project utilizes image processing algorithms to recognize and interpret traffic signs from camera footage or images. By providing real-time traffic sign information to drivers, the project solution contributes to autonomous vehicle navigation systems and enhances road safety.

18) Driver Drowsiness Detection

This project uses computer vision techniques to monitor drivers' facial expressions and eye movements in real-time to detect signs of drowsiness. The solution enhances road safety by alerting drivers when they are at risk of falling asleep behind the wheel.

Data Science Projects for Final Year Students in R and Python

Data Science projects are often classified based on the language that one is using, as they are a great tool if you want to understand R programming and Python programming. ProjectPro's data science mini-projects with source code in Python and R cover diverse industry use cases. You will find something you love or a problem you want to solve and apply your data science skills. Each data science project will let you practice and apply data science skills to real-world business problems. In every project on Data Science, you will perform end-to-end analysis on a real-world data problem using data science tools and workflows. Check out the projects from ProjectPro's repository that will guide you through language learning projects. If you are interested in R programming projects for beginners and Python data analysts projects, these projects will also help you.

Data Science Projects with Source Code in Python

If you are biased against Python for data science, this section will help you. ProjectPro has an exclusive list of projects on Data Science using Python, so you can stick to your bias and build data science applications without any worries. Those looking for Python data analysis projects can also use the list.

1) House Price Prediction

Estimating the prices of houses is fundamental to the real estate industry. In this project, you will see how machine learning regression algorithms like Linear Regression, Random Forest regression, XGBoost Regressor, etc., are used to predict the price of houses. You will learn about various methods for hyperparameter tuning.

 

Source Code: Using Zillow Dataset for Estimating House Prices Data Science Project in Python:

2) Similar Images Finder

Imitation is the best form of flattery, hands down. But, businesses must ensure customers can differentiate the original products from their dupes. In this project, you learn how computer vision and the KNN algorithm assist in comparing images of different products and deducing their similarity. 

 

Source Code: Image Similarity Application Data Science Project in Python

3) Product Reviews Analysis

Enhancing product quality is usually at the top of most business leaders' task lists. They rely on customers' feedback for it. In this project, you will use NLP tools like TF-IDF for data preprocessing, language detection, gibberish identification, etc., on product reviews of an eCommerce website.

 

Source Code: Analyzing Ecommerce Products Reviews Data Science Project in Python

4) Insurance Price Forecasting

Evaluating the monthly premium for an insurance plan is not a trivial task. But, with the help of machine learning algorithms, it can be made simpler. In this project, you will use regression models to estimate insurance prices.

 

Source Code: Forecasting Insurance Prices Data Science Project in Python

Data Science Projects in R with Source Code

If you are more comfortable with R programming and have worked on R projects, go for this section containing R Data Science Projects. This list will be helpful for beginners in R searching for R language projects, as ProjectPro experts help you get comfortable with the language through interesting R Projects with source code from ProjectPro's repository. So, check out the list below, which has insightful R projects for Data Science prepared by ProjectPro experts.

5) Census Income Prediction

This data science project with source code will help you understand working with an imbalanced dataset. You will use a vanilla deep neural network to predict the salaries of specific individuals by classifying them into two categories: >50K <=50K.

 

Source Code: Predicting Census Income Data Science Project in R

6) Wine Quality Prediction

Buying wine alone can be confusing when you are unaware of how its chemical composition signals its quality. In this data science mini-project, you will use regression models to assess the quality of wines.

 

Source Code: Predicting the Quality of Wines Data Project in R

7) Click Fraud Detection System

Click, Click, Boom! No, we are not talking about the trending song; rather, frauds that happen online. This data science live project will guide you in detecting online frauds by implementing models like Decision trees, random forest, logistic regression, SVM, boosting, bagging models, cart, and neural networks in the R programming language.

 

Source Code: Click Fraud Detection Data Science Project in R

8) Financial Market Forecasting

This project is the best data science project for learning applications of machine learning models in the finance industry. You will work on the dataset of Two Sigma company and learn how to use these models to estimate market financial movements.

 

Source Code: Financial Modeling Data Science Project in R

 

Make sure to distinguish data science projects using Python from Big data projects using Python. They are both different and if you are interested in the latter, you can explore Big Data Projects | Hadoop Spark Projects.

How to Build a Data Science Project from Scratch? (Idea to Production)

The answer to how to start a data science project is more relaxed than one might expect. Each data science project comes with its challenges and requirements. But, a few basic steps are followed across most data science-related projects. These are listed below.

  • The first step usually involves converting the business problem to a data science problem. For example, a marketing manager might request a data scientist to understand which marketing channel needs the highest investment. A data scientist will have to raise a request for marketing data and then prepare a pipeline that takes the budget for various channels as input and outputs the number of customers that the business is likely to reach with that specific budget profile.

  • The second step involves closely looking at the data by plotting various graphs and using statistical tools. In the example mentioned in the previous step, the data scientist will look at the revenue generated from each marketing channel. They may also use the data to closely observe the significance of gender, geographical location, etc., on the revenue.

  • Next, the data scientist will select suitable algorithms to make the predictions. Depending on the nature of the data, they will decide whether it is a supervised, unsupervised, or reinforcement learning-based problem.

  • The last step is the most fun part of a data scientist's job. It involves storytelling the solution in a way that most people with a background in data science can understand.

A data science project may have more steps depending on the complexity of a problem. To explore that, we suggest you proceed to the next section.

FAQs on Data Science Projects with Source Code

How many data science projects are there in Python and R?

We have no exact number of data science projects in Python and R as we keep growing our repository of end-to-end data science projects every month. ProjectPro's repository is updated every month with new data science projects in Python and R based on the advent of novel machine learning tools, technologies, libraries, and frameworks. Our library of data science projects has different types of end-to-end independent projects that help you showcase versatile data science skills -data cleaning, data wrangling, data storytelling, model building, optimization, etc. to employers, making them an excellent fit for building a job-winning data science portfolio. However, if you still would like to have clarity on the number of data science projects available in Python and R as of today, check them out here -

Data Science Projects in Python

Data Science Projects in R

Do you provide a lab environment?

We do not provide a custom lab environment, as digging into live AWS, Azure, and GCP environments is better for practicing and applying real-world data skills. For our repository of big data projects, ProjectPro users can practice and apply big data skills using the AWS Free Tier and Azure Free Tier to enable lab environments in the cloud. The cloud lab environments provided by AWS, Azure, and GCP are safe in real-time and provide risk-free trial and error for use on a home network or corporate network connection. To practice and apply real-world data science and machine learning skills, beginners and experts can use the potent tool Jupyter Notebook to interactively develop, present, and streamline end-to-end data science workflows.

What kinds of datasets are used in the projects, and what is their source?

When working on a personal data science project, if you are still looking for a suitable dataset for your next project, ProjectPro is for you. You need to spend less time browsing the web for exciting machine learning datasets to work with; all our end-to-end data science and machine learning projects are developed using real-world data. Whether you just want to practice your data science and machine learning skills or strengthen your data science portfolio, we have all sorts of datasets to help you practice.

Can I get a trial period for a week?

There is no free trial for ProjectPro's data science and machine learning projects. However, we offer a "Proof of Value" period, a 30-day no-questions-asked refund policy that allows our users to explore the actual value of the projects. Talk to our Project Advisors to learn more about the various subscription plans.

Can I get assistance from the industry expert who developed the project?

Yes, Of Course, you will get assistance from any of our industry experts whenever you wish. The experts will help you complete the project if you face difficulties executing the code or need guidance on developing the end-to-end data science pipeline. Talk to our Project Advisors about one-to-one mentorship from an industry expert.

What can I expect from ProjectPro's Data Science Project subscription?

The ProjectPro subscription has exciting features and access to attractive data science project solutions. As a ProjectPro subscriber, you can seek advice from industry experts on writing your resume for specific job roles. Additionally, if you get stuck while browsing the data science project solution videos, you can request a one-on-one session with an industry expert who will guide you. For data science beginners, there is a learning path based on experience level in the industry. Subscribers also get access to customized learning paths to smoothen their learning process. 

Can I work on these data science projects even if I don't have a system with suitable hardware?

When starting with data science and machine learning, beginners usually think they need to burn a hole in their pocket and upgrade the hardware to train a machine learning or a deep learning model as they run on millions and millions of data points. Experts at ProjectPro recommend having just the proper hardware for running these end-to-end data science and machine learning projects. The choice of a computing machine or a laptop ultimately depends on the amount you are willing to shell out. However, experts recommend having a machine with a quad-core processor. Most computations for these projects on data science and machine learning depend on the RAM. To ensure that when working on these real-world data science projects, you don't run out of RAM, upgrade your computing machine with maximum RAM to the extent possible.

Can we download the videos for every project?

Owing to piracy concerns, videos for these Python and R data science projects are unavailable for download on a computer. However, ProjectPro's all-access annual subscription plan gives you unlimited access to the videos, reusable solution code, dataset, and documentation. You can access them 24x7,365 days with your login credentials.

Do you have a demo video on your site?

Here's a short video that will give you an overview of ProjectPro's Big Data and Data Science Project subscription -

However, you can talk to our project advisors anytime and schedule a demo based on availability. We would be happy to help!

Do you provide placements?

No, ProjectPro's subscription plan is designed to help you find a top gig as a data professional (data engineer, data analyst, business analyst, data scientist, machine learning engineer). These projects will help you build an excellent job-winning portfolio and maximize your potential and chances of finding your dream job.

What are the top projects on data science that I can practice?

The top projects on Data Science for practice introduce you to various popular methodologies Data Scientists use daily. The ProjectPro repository has projects designed by industry experts so that you don't miss out on any data science techniques used in the industry. The project's library is updated every month to keep data science enthusiasts up to date with recent technologies. Here are a few of the recent projects from our repository.

MLOps Project for a Mask R-CNN on GCP using uWSGI Flask

Time Series Project to Build an Autoregressive Model in Python

Multi-Class Text Classification with Deep Learning using BERT

FEAST Feature Store Example for Scaling Machine Learning 

Build a Multi-Touch Attribution Machine Learning Model in Python

How do I enrol for ProjectPro's subscription?

Enrolling in ProjectPro's subscription is pretty simple. You can pay through Stripe, RazorPay, PayPal, Debit/Credit Cards, Internet Banking, and UPI. You can also pay the subscription fee through easy monthly installments (EMI). After successful payment, the subscription is activated in 24-48 hours.

Who should enroll for ProjectPro's subscription?

ProjectPro projects are designed to suit various audiences in the Big Data and Data Science industry, from beginners to seasoned professionals. Whether you are a beginner yet to learn how to kick start your voyage in this industry or an intermediate professional looking for a transition to Data Science/upgrading their skillset, there is a customized learning path for everyone. Working on these hands-on projects will smoothen your transition into the industry as a Data Scientist, Data Analyst, Machine Learning (ML) Research Engineer, Computer Vision (CV) Engineer, NLP Research Engineer, Deep Learning expert, or any other data.

What kind of careers can I pursue after practicing data science projects?

If you want to pursue a career in data science, practicing as many projects as possible to hone relevant skills is the best way. This approach will prepare you for various jobs in the industry, including -

  • Data Analyst:  Applies exploratory data analysis tools to analyse massive datasets.

  • Data Scientist: Uses many algorithms (machine learning, deep learning, NLP, etc.) on massive datasets to solve business problems.

  • ML Research Engineer: Understands all machine learning algorithms deeply and uses them to solve different problems.

  • Data Science Course Instructor: Educates masses about different algorithms used in the Data Science industry

  • NLP Engineer: I use machine learning and natural language processing techniques to build application systems, such as chatbots.

  • CV Research Engineer: Builds computer-vision-based application systems for solving business problems

  • Data Science Researcher: Works on advanced-level problems in data science; the problems are highly specific.

 

Learning and exploring data science techniques by doing data science projects alongside your job will prepare you for the challenging tasks that will come up in the future. It is one of the best ways to upskill and is beneficial for career growth.

How do you choose which project to start first? 

As a newbie in Data Science, it is challenging to browse different Data Science problems and figure out the simplest one to start working on. And, if you are an intermediate expert, it becomes even more challenging to filter out projects as per your needs. Therefore, we will soon launch the option of availing a customized learning path for our subscribers so that they can start their Data Science learning journey with our projects without any hassle. The personalized learning path will consider the algorithms that you have understood thoroughly and introduce the projects related to the ones you should start learning one by one.  Besides this, our dashboard already contains a learning path that outlines a track of understanding data science from scratch through ProjectPro data science projects.

Can a beginner in the industry get a job after working on these hands-on data science projects?

Yes, a novice in Data science who aspires to enter the industry as a professional will be able to get a job after working on ProjectPro's Data Science projects. Being patient while learning and investing time is the key to climbing the success ladder. So, work on as many data science projects as possible to prepare yourself for challenging roles in the industry.

What industries leverage data science the most?

Various industries benefit from data science methodologies. These include  Healthcare, Banking, Retail, Agriculture, Smart City, Education, Transportation, etc. If you are curious about how these industries utilize data science, look at the projects below.

Healthcare: Medical Image Segmentation Deep Learning Project

Retail: Create Your First Chatbot with RASA NLU Model and Python 

How do I start a data science project?

After learning about the algorithms used in data science, start using free online datasets on platforms like Kaggle, Github, etc., to build a data science project. Understand the dataset using various libraries of a programming language like Python and then implement multiple algorithms to deduce the values of the output variable.

What is the lifecycle of a data science project?

The life cycle of a data science project is a series of steps you must follow to finish and release a data product to your customer. The lifecycle of data science projects is a modified version of the CRISP-DM workflow approach. It mainly involves the following steps-

  • Data Gathering- Data gathering entails collecting information from various sources that can assist in developing the product. Keeping track of the actual data source is critical throughout the life of a data science project since data may need to be re-acquired to test new theories or implement revised experiments.

  • Data Processing—This step is also known as data cleaning or wrangling. The data collected in the first step of a data science project often may have missing items, inconsistencies, and semantic errors. At this point, exploratory data analysis is crucial since summarizing the clean data can help find outliers, anomalies, and trends that can be used in the following steps. This stage assists data scientists in determining what they want to do with the data.

  • Data Modeling and Prediction—This data science project entails building, deploying, and revising programs to analyze and generate significant business insights from data. These programs are usually developed in languages such as Python, R, MATLAB, or Perl. The data undergoes various machine learning approaches to determine the machine learning model that best suits the business's needs.

  • Model Assessment—Each performance metric uses different evaluation metrics. When analyzing the efficiency of a machine learning model, one of the most typical questions experts have is which dataset to use to assess the model's performance.

  • Model Deployment—Data scientists may prefer Python, but the operational setting supports Java. Thus, you need to rework machine learning models before deploying them. Then, you can deploy the machine learning models in a pre-production or testing environment before deploying them in production.

  • Maintenance—This phase involves defining a strategy for long-term analysis and maintenance of the data science project. During this phase, you must carefully examine the model's performance and note any performance decline. Data scientists can record their insights from specific data science projects to share with others and to help them complete similar data science tasks faster in the future.

  • Model Optimization— is the last step of any data science project. It entails retraining the machine learning model every time additional data sources are introduced and implementing the required actions to maintain the model's efficiency.

Where can I practice end-to-end data science projects?

To practice end-to-end data science projects, check out websites like Kaggle, ProjectPro, GitHub, etc. All these websites have solved data science problems, but ProjectPro has solutions with explanations from industry experts.

Where can I find datasets for data science projects?

You can easily find the datasets for data science projects on websites like Kaggle, GitHub, Google Cloud Public Datasets, UCI Machine Learning Repository, etc. If you are a subscriber to ProjectPro, you can explore all these sites to find datasets for data science projects, as the platform provides easy access and download options for the dataset used in each project. 

What are some excellent healthcare and finance data science projects? 

In healthcare, try the two projects, Polyps detection and Bacteria inhibition zones detection, from the ProjectPro repository. For the finance section, try out the library's stock price prediction and financial modeling projects.

Can you be a data scientist without coding?

There is no definite yes/no answer to this question. Although most data scientist roles demand programming skills, some organizations hire individuals with zero to beginner-level programming knowledge. Most people give up on becoming data scientists since their coding skills need to be at par. However, many data scientists began their careers without programming experience or skills. If you need to improve at programming, there are other areas you need to focus on, such as statistics and probability, data analytics, etc., and most importantly, you must be passionate about working with data. Once you have mastered these areas and entered the data science domain, you can acquire programming skills and knowledge by working on industry-level Data Science projects. Working on real-world projects will enhance your data science skills (including programming skills) and prove beneficial in growing your data science career.

 

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