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Data Science

Data science projects you should try

Admin | August 19, 2022

Key takeaways:

  • Data science is thriving as a great career path in the market. 
  • Data science projects can be the best way to upskill yourself and make you a better data scientist. 
  • With the help of data science projects, scientists can learn, deal and solve real-life problems. 

Data science is the study of large amounts of data using advanced tools and techniques to discover previously unseen patterns, elicit meaningful information, and make business decisions. The data used for analysis can come from various sources and be presented in various formats. 

Data Science is now one of the most sought-after fields in today’s data-driven world. A Data Science programme is not enough to become proficient in Data Science. So what other option do you have? 

One of the best ways to improve one’s performance is to practise the skills one has learned. Working on data science projects is a perfect solution as you get a chance to learn and hone your skills. It gives you a good insight and appreciation of key concepts in Data Science, regardless of whether you are a beginner or an experienced professional.

Importance of data science projects 

A data science project allows you to put your skills in data collection, cleaning, analysis, visualisation, programming, machine learning, and other areas to good use. It enables you to apply your knowledge to real-world problems. After completing the data science projects, you can add them to your portfolio to demonstrate your skills to potential employers.

Data science project ideas

Data science projects are undoubtedly beneficial for data scientists as they get a chance to enhance their skills. 

  • Increase productivity 

Data scientists can process data in various forms to receive the information needed by the company. Using data science projects helps data scientists make decisions and gives them confidence because statistics and details back them up. This gives them a competitive advantage and boosts productivity.

  • Fewer possibilities of errors

It is essential to ensure that data collection, facts, and figures are completed quickly and accurately. Data science projects help data scientists hone their skills, which means there will be fewer errors in the future. Data science projects have a low to negligible possibility of errors or mistakes.


Best data science projects for beginners

You can use your data science expertise to solve real-world data science issues with such data science projects for beginners. 

  • Fake news detection

Fake news does not need an overview. It is very easy to spread false information across online platforms in today’s interconnected world. Fake news is sometimes spread via the internet by unauthorised sources, causing problems for the targeted person, causing panic and even crime. 

To counteract the spread of fake news, evaluating the legitimacy of the information is vital, which this Data Science project can help with. Python can accomplish this, and a model was developed using TfidfVectorizer. PassiveAggressiveClassifier can be used to classify between real and baseless news. Python packages suitable for this project include Pandas, NumPy, and scikit-learn, and the dataset can be News.csv.

  • Sentiment analysis

The sentimental analysis assesses words to identify sentiments and viewpoints that may be optimistic or pessimistic in polarity. This is a type of segmentation in which the classifications are either binary (optimistic or pessimistic) or multiple (positive and negative) (happy, angry, sad, disgusted, etc.). The project is written in R, and the dataset from the Janeausten R package is used. It is one of the best data science projects for beginners. 

  • Forecasting web traffic

Forecasting time series can be a significant subject in machine learning. It is a popular aspect of time series prediction because it allows web servers to better manage accessible resources and avoid shutting down. Web traffic forecasting could be even more efficient if wavelengths were used instead of neural networks. The power of a wavelength is found in “causal dilation convolution,” which is liable for increasing the efficiency of any neural network.

  • Detecting breast cancer 

Breast cancer cases have increased in recent years, and the best way to tackle it is to detect it early and take appropriate preventive measures. To create such a system in Python, the model can be trained on the IDC dataset, which contains histology images of cancer-inducing malignant cells. Convolutional Neural Networks can be used in this breast cancer detection project. You can include and try this as a data science mini project. 

  • Creating a chatbot

Chatbots are useful for businesses because this project can answer all client queries and provide information without slowing down the operation. The procedures that are fully automated have reduced the customer support workload. 

This process is simple to achieve by utilising Machine Learning, Artificial Intelligence, and Data Science techniques. Recurrent Neural Networks trained on the intentions JSON dataset can be used to train the chatbot, which can then be implemented in Python. The chatbot’s goal will determine whether it is domain-specific or open-domain.

Data science projects in Python

Python has achieved fame in data science over the years. It is popular among data enthusiasts because it provides a simple introduction to data science and machine learning. Here are some data science projects in Python: 

  • Detecting credit card fraud

Credit card scam is more common than you might think, and it has recently increased. By 2022, we will have metaphorically crossed a billion credit card users. However, thanks to technological advancements such as Artificial Intelligence, Machine Learning, and Data Science, credit card companies have successfully identified and intercepted these frauds with high accuracy. 

Simply put, the idea is to examine a customer’s regular spending pattern, including the location of such spending, to classify fraudulent and non-fraudulent transactions. For this project, R or Python programming languages can be used to ingest the customer’s recent transfers as a dataset into decision trees, Artificial Neural Networks, and Logistic Regression.

  • Insurance claim severity

Insurance claim severity is a prominent data science project for data analysts and data scientists to practise their skills. It enables predicting the types of claims received by an insurance company almost daily. This data science project with source code is written in Python and includes machine learning algorithms.

  • Face recognition system

Facial recognition software generates numerical depictions of human faces and compares them to other human faces to verify a person’s identity. It employs machine learning techniques to recognise, collect, store, and analyse face characteristics to match them to photos of people in a database. 

Machine learning algorithms, for example, can quickly find, capture, collect, analyse, and retrieve various facial features and nuances to match them with pre-existing images and form a connection. Machine learning in face recognition has already proven its worth in a variety of fields, including but not limited to security and biometrics.

  • E-commerce product reviews

Since the early 20s, the eCommerce industry has grown in tandem with the expansion of internet services. Using data science to learn about customer shopping habits and product reviews is a great way to boost sales. 

When working on e-commerce data science projects, you already have access to internal data. However, holistic data collection necessitates collecting both internal and external data. The augmented data could be stored in a data lake or a data warehouse, where you can perform your analysis and train your machine learning models.

  • Detecting banking fraud

Banking is one of the fortunate industries that has historically collected a large amount of structured data and is one of the first to apply data science technologies. Data science is a requirement for banks to compete, attract more customers, increase the loyalty of existing customers, and make more efficient data-driven decisions. 

Fraud detection refers to proactive steps to detect and prevent fraudulent activities and financial losses. Machine learning is a critical component of fraud detection.

Data science projects with source code

Trying to create some data science projects to boost your resume but feeling overwhelmed by the size of the code and the proportion of concepts used? If so, here are some data science projects complete with source code.

  • Chatbot with Python

Chatbots are an important part of any business. Many businesses must provide their customers services, which requires a significant amount of manpower, time, and effort. Chatbots can automate customer interactions by answering some of the most frequently asked questions. You must smartly customise it to work effectively in your domain. Because open-domain chatbots can be asked any question, massive amounts of data are required to train them.

  • Stock market prediction

Stock price analysis has become a significant area of investigation and is one of the top machine learning uses. Stock Price Prediction with Machine Learning facilitates you in determining the future value of a company’s stock and other economic assets traded on an exchange. The entire point of predicting stock prices is to make large profits. It is difficult to predict how the stock market will perform. 

Other factors influencing prediction include physical and psychological factors, rational and irrational behaviour, and so on. All of these factors work together to make stock prices dynamic and volatile. This makes accurate stock price forecasting extremely difficult.

  • Wine quality prediction

Wine quality prediction is one of the most popular data science projects using the R programming language. The primary goal of the wine quality project is to investigate the various chemical properties that influence the quality of red wines. One can also brush up on data science concepts such as exploratory data analysis, dataset components, data munging, and many more.

  • Detecting Parkinson’s disease

Parkinson’s disease is a cognitive disorder of the brain. It causes shaking of the body, hands, and stiffness of the body. At this advanced stage, no proper cure or treatment is available. The project will be created using the Python programming language. Python libraries such as scikit-learn, NumPy, pandas, and XGBoost will be used in this. 

The XGB Classifier model is designed to work on the goal. A UCI ML Parkinson’s dataset can be downloaded for free from the internet. To classify the datasets, gradient boosting algorithms are used. The project’s accuracy is calculated, and thus the outcome is determined. The project will analyse data from patients’ symptoms to predict strokes.

Steps to preparing a data science project

Steps to prepare a data science project

No one appears to be able to provide a solid answer to how the overall process works when we talk about data science projects. Hence from data collection to analysis and presentation of results, we’ve got you covered here. 

  • Look for interesting questions

Begin writing down questions, irrespective of whether you believe the data to answer them exists. You may ask a question and instantly think to yourself, “No, this can’t be solved,” and then cross it off your list. But don’t do it! Put it on your list.

  • Gather the data

Once you’ve decided on a question to focus on, it is time to search the world for data that might be able to solve it. As mentioned above, data can come from various sources; thus, this step can be very creative!

  • Explore the data

Once this step is completed, the analyst typically spends several hours learning about the domain, manipulating and exploring the data with code or other tools, and has a good sense of what the data is trying to tell them.

  • Model the data

This step entails the application of statistical and machine learning models. Here, we not only fit and select models but also implement mathematical verification metrics to quantify the models’ potency.

  • Conclude and visualise

This is, without a doubt, the most important step. While it may appear obvious and simple, the ability to summarise your findings in a digestible format is far more difficult.

READ MORE: The data science roadmap explained

Bottom line

With the right set of expertise, guidance, and tools, you can learn to deal with any type of data science project. No level is too difficult for students to master. As a result, these projects are ideal for honing one’s skills and making rapid progress toward mastery.

If you are new to the Data Science field and want to learn and build a stunning career in this promising domain, we recommend online master’s programme in Data Science offered by the Manipal Academy of Higher Education (MAHE). Delivered through Online Manipal, M.Sc. in Data science helps you acquire all the data skills to emerge as an in-demand professional. For working professionals, who are interested in ascending to managerial roles, Online Manipal offers an MBA with Data Science.

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