Data science requires no single skill set and many data science degree programs are new and few of them are of questionable value. Considering the diverse skillset of a data scientist, the next important question that prospective students have is how can one get into the field of data science after pursuing data scientist training? At DeZyre our objective is to be responsive to all student queries and answer every possible query that our students or prospective students have regarding a career in data science. When certain questions come up with more frequency, we try to answer them through a detailed blog post to help professionals decipher the answer to their question in a clear and concise manner. One common question that has come up lately from professionals pursuing a career in data science is - “What is a good way for a data scientist to construct an online portfolio and get hired as a?” And with all that said, here is a blog post on how to build a data science portfolio that will get you a data science job. This post will convince you that building a data science portfolio will benefit you and the community.
Many hiring managers and clients who interview prospective data science professionals say that the candidates rarely bring in any samples of the data science projects they have worked and this is what puzzles them. Anybody can put together a boilerplate resume content containing full of fancy keywords and assertive verbs like – Blah Blah enterprise blah blah architect blah blah machine learning engineer. The advantages of “show, don’t tell” are much more convincing when it comes to landing a top gig as a data scientist.
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If you are of the thought that a data science portfolio is same as a data science resume then you are fallacious. A data science portfolio is a pride of ownership of one’s learning of data science skills far more than any data science resume can engender. It is an opportunity for a professional to create a brand for oneself so that the prospective employer can form an opinion about their data science skills. Your portfolio should say “This is me, and this is what I can do for you”, it’s all about selling yourself and your data science skills.
A data science portfolio is an online presence that consists of a collection of data science projects that you have worked on, speaks about yourself and showcases your data scientist skills to prospective employers, clients and hiring managers. Data science is a multi-disciplinary in nature where the data product is creative and empirical, where telling a hiring manager you can do it is not values the same as showing them through a portfolio. A data science portfolio helps you stay focussed, motivated so that you can leverage more data science projects to add on to it. Building a data science portfolio with the collection of all the data science projects that you have worked on is essential as employers today look at the portfolio much before they consider a candidate for a data science job. Take sufficient time to produce a portfolio that will create a lasting impression on them about the various data science skills you have – it will pay off.
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If you have a data science degree from a top-tier university then it is easier to get a data science job. A data science degree from a prestigious institution inculcates trust amongst employers on valuable data science skills a candidate can bring in to the organization, considering the fact that it is in a subject that is relevant to the work you would do in the company. However, considering the cost and time required to do a data science degree from a prestigious university, not many students opt for it. Candidates look for multiple MOOC’s from various eLearning providers like Coursera, Udacity and DeZyre that can help them hone relevant data science skills at economical cost. If you are among those candidates who does not have a relevant data science degree from a prestigious institution then you have to build that trust yourself among prospective employers and a data science portfolio is the key to it.
Suppose that around 100 applicants have applied for a particular data scientist job opening. Let’s say that the employer spends total of 5 hours for filtering the received applications and then takes a decision on who should be invited for a telephonic interview. This implies that every application is evaluated for 3 minutes on average. The employer begins evaluating your application for the data science job with no trust that you add value to an organization, and you have 3 minutes to build that trust that will convince them to invite you for a telephonic interview. A portfolio will help employers judge someone’s real-world data science skills.
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Building a data science portfolio is as important as taking a data science training. Often, after completing data science training at DeZyre, students ask DeZyre industry experts on how to gain experience required to land a data science job. Industry experts at DeZyre suggest that they work on several small data science projects then put them online. Working on small data science projects deepens the understanding of various analysis methods and also helps learn new data science techniques. Publishing data science projects online in the form of a HTML page or a website or a collection of code repositories builds a data science portfolio of your work, showing potential employers that you can play with data and discover meaningful insights from it.
A strong data science portfolio should demonstrate wide range of data science skills, including hands-on project experience with Python Pandas, NumPy, Sci-Kit, SciPy, R programming language or any other related analysis tools like MATLAB. The portfolio should clearly highlight your working experience with large datasets (that cannot be fit into an excel spreadsheet) or unstructured data. The data science projects that you mention in your portfolio should be a clear reflection of your strong quantitative reasoning and problem solving skills using concepts of math and statistics.
To flesh out your data science portfolio with projects you will have to work on small data science projects hosted on Kaggle or enrol for DeZyre Hackerday where you can work on 4 projects every month under expert guidance. In Kaggle data science competitions data is cleaned and formatted and the candidates only have to focus on building the model whereas DeZyre Hackerday focuses on data science projects that begin with collecting data and navigate through the entire data science project lifecycle. Working on data science projects with diverse datasets across various domains shows employers and hiring managers that you have passion and love for data and like to continue improving yourself.
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Above all this, if a data science portfolio can effectively convey the message to the employer on what made your past data science projects fun and challenging to work on, then heck- you are hired.
Check out DeZyre’s Data Science Hackerday that will help you build an effective data science portfolio by working on multiple data science projects across various business domains.
While the data science job hunting process might take time, this is the best time to pursue a certified data scientist training. Use the above tips as you traverse through different data scientist jobs and make sure you build an effective data science portfolio and a great network within the community. Data science is an exciting field to work where you can grow your skills over the time and build a rewarding career as a top data scientist.