Beginner’s Guide to Top 10 Data Science Projects


You’ve probably heard the suggestion “conduct data science projects” a thousand times as an aspiring data scientist.

Data science projects not only help you learn more about the profession, but they also help you stand out from the crowd of data science aficionados wanting to break into it.

However, not all data science projects can help you stand out on your CV. In fact, putting the wrong projects in your portfolio can be detrimental to your career.

In this essay, I’ll go over the projects that you should include on your resume.

For each project, I will also offer you with sample datasets to work with, as well as tutorials to assist you in completing the project.

As a data scientist, one of the most crucial skills to have is data gathering and pre-processing.

The majority of my work in my data science profession entails Python data collecting and cleaning. We need to get access to important data on the Internet when we grasp the business necessity.

APIs or web scrapers can be used to do this. After that, the data must be cleaned and organised into data frames that can be given into a machine learning model as input.

This is the part of a data scientist’s job that takes the most time.

I recommend completing the following tasks to demonstrate your data gathering and pre-processing abilities:

Web Scraping – Website for Food Reviews

Zomato Web Scraping with BeautifulSoup (Tutorial)

Language: Python

Scraping customer evaluations from a food delivery service is a fun and useful endeavour to include on your resume.

Simply create a web scraper to capture all of the review data from this site’s web pages and save it in a data frame.

If you wish to take this project any further, you may utilise the information gathered to create a sentiment analysis model that will classify which reviews are favourable and which are negative.

Choose a restaurant with the best overall sentiment the next time you’re looking for something to eat.

Web Scraping — Online Course Site

Tutorial: Build a Web Scraper with Python in 8 Minutes

Language: Python
Do you want to take the top online course in 2021? It’s challenging to sift through hundreds of data science courses to find one that’s both inexpensive and well-reviewed.

This can be accomplished by scraping a website for an online course and storing all of the results in a data frame.

You may take this project a step further by visualising variables like price and rating to locate a course that is both reasonable and of high quality.

You can also create a sentiment analysis model to determine how each online course is perceived overall. Then you can select the course that has the best overall rating.


Create some projects in which you use an API or another external tool to collect data. When you first start working, these talents will usually come in handy.

Most organisations that rely on third-party data buy API access, and you’ll have to acquire data with the help of these external tools.

Here’s an example of a project you could do: Collect data relating to a given hashtag using the Twitter API and save it in a data frame.

Skill 2: Exploratory Data Analysis

After you’ve collected and saved your data, you’ll need to analyse all of the variables in your data frame.

You must observe how each variable is distributed and comprehend their interrelationship. You must also be able to respond to questions using the information available.

As a data scientist, you’ll be doing this a lot, probably even more than predictive modelling.

Here are some EDA project ideas:

Heart disease risk factors must be identified.

The Framingham Heart Study was used as a data source.

Tutorial: The Framingham Heart Study: Decision Trees

Language: Python or R

This dataset includes indicators such as cholesterol, age, diabetes, and family history that are used to forecast when a patient may develop heart disease.

You may examine the associations in this dataset using Python or R, and come up with solutions to questions like:
Is it true that diabetic persons are more likely to acquire heart disease at a young age?
Is there a demography that is more susceptible to heart disease than others?

Is it true that regular exercise reduces the risk of heart disease?
Is it true that smokers have a higher risk of heart disease than nonsmokers?
A data scientist’s ability to answer these questions with the help of accessible data is a crucial talent.

This assignment will not only help you improve your analytical skills, but it will also demonstrate your ability to gain insight from massive datasets.

World Happiness Report

Dataset: World Happiness Report

Tutorial: World Happiness Report EDA

Language: Python

To evaluate worldwide happiness, the World Happiness Report looks at six factors: life expectancy, economics, social support, lack of corruption, freedom, and generosity.

When running an analysis on this dataset, you can answer the following questions:

Which country in the world is the happiest?
What are the most essential variables that contribute to a country’s happiness?
Is your overall happiness rising or falling?
Once again, this is an assignment that will help you enhance your analyst skills. Curiosity is a quality I’ve noticed in the most successful data analysts.

Data scientists and analysts are constantly on the lookout for potential contributors.

They are continually on the lookout for correlations between variables and are inquisitive.

Doing tasks like this will help you develop an analytical mind if you want to be a data scientist.

Skill 3: Data Visualization

When you first start working as a data scientist, the majority of your clients and stakeholders will be non-technical.

You’ll have to deconstruct your insight and deliver the results to a non-technical audience.

Visualizations are the most effective approach to accomplish this.

Because graphs are simple to understand at first glance, presenting an interactive dashboard will help you better communicate your thoughts.

As a result, many employers consider data visualisation to be a must-have ability for data science employment.

Here are some projects to include in your portfolio to show off your data visualisation abilities:

Building a Covid-19 Dashboard

Dataset: Covid-19 Data Repository at Johns Hopkins University

Tutorial: Building Covid-19 Dashboard with Python and Tableau

Language: Python

You’ll need to use Python to pre-process the dataset above. Then, using Tableau, construct an interactive Covid-19 dashboard.

Tableau is one of the most popular data visualisation programmes, and it’s a requirement for most entry-level data science jobs.

Building a Tableau dashboard and displaying it in your portfolio will help you stand out because it showcases your knowledge of the platform.

Building an IMDB-Movie Dataset Dashboard

Dataset: IMDb Top Rated Movies

Tutorial: Exploring IMDb Top 250 with Tableau

You can use Tableau to create an interactive movie dashboard using the IMDb dataset.

As I previously stated, showing your own Tableau dashboards can help your portfolio stand out.

Another fantastic feature of Tableau is that you can share the link to your dashboard with anyone who wants to utilise it.

As a result, potential employers will be able to engage with your dashboard, which will pique their interest. You’re already a step closer to getting the job if they’re interested in your project and can play around with the final output.

If you want to learn more about Tableau, check out my tutorial.

Skill 4: Machine Learning

Finally, you’ll need to show off projects that illustrate your machine learning expertise.

Both supervised and unsupervised machine learning projects are recommended.

Sentiment Analysis on Food Reviews

Dataset: Amazon Fine Food Reviews Dataset

Tutorial: A beginner’s guide to sentiment analysis with Python

Language: Python

Machine learning is heavily reliant on sentiment analysis. Businesses frequently utilise it to assess overall customer response to their products.

Customers frequently discuss items on social media platforms and in customer feedback forums. This information can be gathered and evaluated to see how different people react to different marketing methods.

Companies might reposition their products or adjust their target audience based on the results of the sentiment study.

Because practically every business has a social media presence and the need to evaluate client reaction, I recommend including one sentiment analysis project in your portfolio.

Life Expectancy Prediction

Dataset: Life Expectancy Dataset

Tutorial: Life Expectancy Regression

Language: Python

You will be forecasting a person’s life expectancy based on variables such as education, neonatal mortality, alcohol usage, and adult mortality in this project.

I’m adding a regression challenge to the list because the sentiment analysis project I stated before is a classification problem.

It is critical to include a variety of tasks on your CV to demonstrate your knowledge in various fields.

Breast Cancer Analysis

Dataset: Breast Cancer Dataset

Tutorial: Cluster analysis of breast cancer dataset

Language: Python

You’ll use a K-means clustering technique to detect the existence of breast cancer based on target attributes in this project.

Unsupervised learning technique K-means clustering

Because most real-world data is unlabeled, having clustering projects in your portfolio is critical.

Even large datasets gathered by businesses are frequently devoid of training labels. You may need to do the labeling yourself as a data scientist utilizing unsupervised learning approaches.

This article was originally published here

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