The demand for data scientists is on the rise. You have to compete with many candidates to get your desired job as a data scientist in any company. A portfolio full of fantastic data science projects can put you ahead of your competition and get you that job. Independent data science projects and machine learning projects are the best substitutes for work experience. It would be best to enrich your portfolio with various types of projects to show off your skills in data science to impress your prospective employers and land a job as a data scientist.


In this article, we will be explaining further how helpful data science projects are to a data scientist portfolio. Working on various projects not only helps you to get the job but also improves your skills. 


Importance of Having Data Science Projects in Portfolio

Employers want data scientists who are experts in their fields. They will expect you to produce effective results from your data analysis which will be profitable for business. So, how can you prove your expertise if you have no previous work experience? The answer is simple. You have to create a portfolio showing different types of individual projects.  

It would be best if you worked on projects that are related to the company you want to join as a data scientist. There are free datasets in various sites like Google’s beta dataset search engine, Kaggle, GitHub, Reddit, and Governmental sites. After you gather the data, develop questions for your project. Next, analyze the data to find out a result that answers your questions. Try to work on unique data science projects or machine learning projects, and share them with people. 

When you work on any project, you gather information and analyze it to get to an understanding. You have to work with many kinds of data; hence, you have to develop the new skills necessary to become an expert data scientist. 

Therefore, if you want to get your dream job as a data scientist, you have to create a data scientist portfolio showing the diverse projects you develop. When the employees see your experience in working on projects related to their company, they will consider hiring you. 



What Data Projects You Should Add To Your Portfolio?

Data science is a wide-ranging field. It is impossible to be good at every field of data science. Your employers also understand that. However, they prefer candidates who have experience with various kinds of data management and analysis work.

Therefore, you should add various types of projects in your portfolio to stand out from most of the applicants for the post. There are mainly four types of projects employers expect to see in your portfolio. These are: 

● Data Cleaning

● Exploratory Data Analysis

● Data Storytelling or Visualization 

● Machine Learning

Data Cleaning is the most common type of data science project. 80% of the work of a data scientist is about data cleaning. It is a process of correcting data by removing errors, deleting unnecessary data, and manually processing data to ensure accuracy. 

Exploratory Data Analysis or EDA is a process of analyzing the dataset corrected by data cleaning. Through this project, you must find out similar patterns and differences in the dataset. Then, you can develop hypotheses according to your research. 

Data Visualization is necessary to present your findings to the audience. It is a graphical representation of information. For this, you have to learn how to show your result through graphs, charts, and maps. Various visualization tools help you to create a presentation that makes the information easy to understand. 

Machine learning is crucial for getting a data scientist job. You must know how to build a system that can search data for similar patterns and take necessary actions to analyze the information. You should at least have the basic concept of machine learning if you are serious about becoming a data scientist.

When you create various unique projects, make sure to share them. Then, you should include the information in your portfolio to show how serious you are about data science.  


What Should The Data Science Projects Reflect About You?

Your project portfolio is your secret to landing a top gig as a data scientist. Employers identify certain traits and skills from the projects to select a candidate suitable for their work. So, the data science projects and machine learning projects should show your technical skills and soft skills.  

Your projects should show the following technical skills:

● Efficiency in gathering and managing data. 

● Competency in Data Cleaning projects

● Ability to visualize data and results

● Good machine learning skills to develop models with useful features

● Interpreting and developing complex and realistic metrics

● Sharing machine learning systems or apps with others for practical use 

● Setting up complex statistics and experiments

● Software Engineering skills to develop and maintain programming projects

Apart from these technical skills, employers also expect you to have soft skills. Data scientists often have to work with people. After all, it is all about understanding people and their decision-making process. So, here are some soft skills and traits which should be reflected in your portfolio: 

●     Problem-solving skills: your projects should show how you handle complex data and solve any problem related to data management.

●     Communication Skills: A data scientist must be an expert in both written and verbal communication skills. Your projects must prove your ability to communicate effectively. 

●     Team Player: Data Scientists should be team players because they often have to work with other people. Hence, try to add group-based projects to your portfolio. 

●     Curiosity: You should upload different kinds of projects in your portfolio to show your curiosity for data analysis. So, do projects to find a solution to a problem. Your projects should not be developed only for getting a job. They actually show your remarkmart intentions of learning.


Your portfolio is the first thing that shows your qualifications as a data scientist. Thus, it must have various data science projects to convince employers to hire you as a data scientist.