Having done a bunch of data science courses and certifications will not let you go far enough when it comes to landing a data science job. All you need is a portfolio of projects that you can showcase to prospective employers. You may have all the desired data science skills under your belt, but without a portfolio of projects, you might not be able to convince the hiring manager that you are the best fit for the open data science job.
A data science portfolio is an asset that demonstrates a data scientist’s skills and approach to solving a business problem.
Often when creating a data science portfolio, beginners and experienced professionals have this question as to what kind of data science projects should be included in a portfolio. Landing a top data science gig is not easy but include a variety of data science projects in your portfolio can increase your chances of getting hired.
Data Cleaning Projects
One of the key roles of a data scientist is to clean and arrange the messy data of any company. So it is a better idea to add a project that involves data cleaning. Make sure to use existing messy data sets, then perform exploratory data analysis to clean it up and do data analysis to glean better insights. Data cleaning is one of the most crucial tasks a data scientist performs, and add a couple of data cleaning projects to your portfolio will definitely give prospective employers a knick-knack of how good you are at working with messy datasets because real-world is often messy.
Data Storytelling Projects
Due to the expansion in data for any company, there is a huge amount of raw data that is not arranged and does not make any sense. Being a data scientist, it is your job to find the correlation in the existing data and to fit them into a story narration, so they make some sense. You can add an example where you have implemented this approach.
Machine Learning Projects
There are different types of machine learning projects you can include – NLP projects, computer vision projects, image processing projects, deep learning projects or any other specialized category of projects that you’ve worked on. The different types of projects on your portfolio would showcase your know-how about different sub-domains in machine learning.
Data scientists are in great demand and require a versatile skill set to succeed in the industry. Having theoretical knowledge is not enough to land a top data science job if you do not know how to put theory into practice. So instead of having theoretical knowledge, you must focus on practical one as recruiters focus on how a candidate is able to apply that knowledge. So having a portfolio will help both the candidate and the recruiter. Candidates will be able to showcase their talents, and the recruiter will get a base for selecting the candidate based on the portfolio. So what are you waiting for? Get started working on real-world data science projects to build a fantastic portfolio that will get you hired at one of the FANG companies in 2021.