Last Updated on November 2, 2022 by Robin
Data scientists are still in high demand. They’re needed everywhere from startups to Fortune 500 companies, but navigating your career as a data scientist in this job market can be tough because most employers require years of experience.
This problem has a simple solution. All you need is a little bit of creativity and a lot of hustle. I’ll share my observations as a recruiter about landing a data science job without any prior experience.
I’ve reviewed 1,000’s data scientist resumes and talked to dozens of hiring managers. I’ve learned exactly what makes a candidate stand out above the rest, so I’m going to tell you what I have learned.
What is data science exactly?
The easy way to explain the definition of data science is the application of math and computer programming to large amounts of data for predictive modeling to gain insight into which to make decisions. The focus here is on prediction to figure out something that hasn’t happened yet by looking at either trend data or having the ability to draw conclusions based on data.
What to consider at the beginning of your job search?
This is taking into account that you do not have a job after completing an internship, boot camp, or are a first-time job seeker after online self-study.
The hiring process can be brutal and the job application process can be long with many job postings having several applicants, so you need to stand out! Start with your most transferable skills. Take inventory of what you already know and what you can leverage to build on.
Consider starting out as an entry-level data analyst at a consulting company or small organization which will usually have a more senior person to walk you through what is expected. You can also get your hands on several projects.
These jobs will get you in the door understanding the types of business insights, visualizations, analytical tools, and gets your hands on real projects you can leverage. This will allow you to build a solid skillset.
Suggested 8 Steps to Get a Job in Data Science
- Learn a programming language in the area of data science job you are interested in and get certified.
- Get an internship either through school, personal connections, or job boards.
- Work on industry projects that allow you to use real-life situations to build a portfolio of work.
- Be able to explain what interested you in the field.
- Make a list of resources for continuous learning to stay on top of technology and the industry you are interested in.
- Practice communication and interpersonal skills.
- Practice data modeling and data visualization with your projects.
- Your mathematics and statistics skills need to be practiced.
Learning a Programming Language
You can accomplish learning a programming language via a boot camp, an online course, or a university setting.
The applications used to build and analyze data will require at least one or two of these. It is suggested that 2 or more are learned to be more versatile for roles that are open.
The interviewer will definitely ask several questions on how you have applied the language or give you a sample project or code exercise. A job application will not go into the level of coding details needed for the roles on job boards. It will be just used to weed out those that don’t have it.
A technical certification will prove to an employer that you have mastered a certain level of skill.
Explain What Interested You in the Role
This topic is extremely important and goes beyond the application process and just a job title. There are thousands who are just getting started in the field, but what makes you different is going to stand out. If candidates are in it just because it’s the trend right now, that is not going to be beneficial to an employer and can hamper your interview process.
Decision-makers want to know the path you want to go down so they can determine where you fit in the organization. Is it machine learning or business intelligence? Is it predictive analytics you want to work with or just reporting and dashboards? Why are you passionate about it?
Work on Industry Projects/Build Portfolio
You may not have previous experience in an actual working environment, but there are ways to participate in projects that a real data scientist would work on for practical experience. You can create your own project, enter competitions, and try to solve a problem or ask others to join or assist in creating your own learning path. This is an area where you can create your own relevant work experience or use it to work with potential employers who can convert that experience into a full-time job.
Working on side projects allows one to practice a skill set by coding, using mathematical skills, gaining domain knowledge, and having an online presence to show future employers through a portfolio profiling your best work.
Internships for Hands-On Experience
There are several ways to take advantage of an internship to show your value to a company as this is the ultimate gateway into a first job in data.
- Show your ability to utilize the tools they are using.
- Prove your ability to work with teams.
- Take advantage of having interaction with users of your project.
- Have regular talks with the hiring manager about the company and opportunities.
- Participate in or ask to shadow other workers that will align with your skill set.
- Start a track record of being dependable, curious, and a problem solver.
- Use it to learn about the day-to-day life of a data scientist
Create a List of Resources
This list of resources is key for any data scientist/machine learning engineer! It’s important to stay on top of the latest tools, methodologies, programming techniques, open-source languages, industry applications, industry insights, company write-ups for those that you are interested in, practice sites, and where you can go to get answers to questions.
- Dataset sources
- Online Courses
- Mentors or industry leaders to follow
- Practice sites
Practice Communication and Interpersonal Skills
This is one key area employers are seeking, so if you have no job experience, there are ways you can gain communication and interpersonal skills.
In school, you worked on projects. If you trained outside of school this can also apply. You can self-evaluate how well you worked on team projects. Were you open with information? Did you help the team or let others do the majority of the work? Did you have leadership roles? Were you able to communicate your thoughts or ideas clearly? Were you an overall good team player and asset? You can also ask others where your weaknesses are like an instructor, friend, or past co-worker.
Most of all, do you have passion for what you do? You can have the best skills but if you are a lackluster communicator, employers will not hire you. They look for those who want to learn, will add energy to their teams, and has the foundations to build upon.
Practice Data Modeling/Data Visualization
Every project or task in data science or machine learning will have some aspect of data modeling or data structuring and how to visualize the data. It’s important to be able to create a diagram that shows the relationships of stored data. It is also imperative to know how to present a graphic representation of data.
An interviewer will want to know that you have the ability to fully understand how to use data, show the data, get the most value out of it, and understand the connections of the structure.
Keep Up Your Mathematics and Statistics Skills
Most people don’t realize how much math and statistics play in data science/machine learning. The job boards don’t exactly scream mathematics, but it really is the core. Be sure you understand how to set up calculations for statistics, linear algebra, and calculus are all used to create algorithms to make predictions.
Frequently Asked Questions
Which degree is best for a data scientist?
Data scientists are experts in statistics, computer programming, and machine learning.
A bachelor’s degree in these fields is generally required for entry-level positions, but many employers prefer candidates with advanced degrees. Master’s programs are available in both computer science and statistics, and PhDs are offered in statistics, mathematics, and physics. Some schools offer joint programs combining statistics and computer science, such as Stanford University’s CS program.
These programs take two to four years to complete and require extensive coursework in both areas. Most students enter graduate school right after completing undergraduate studies. However, some employers may hire graduates without any formal training.
Regardless of the type of degree you pursue, you should expect to invest six months to a year studying for exams and working full-time during your time studying.
Can you get a data science job with just a certificate?
YES it’s been done so get certified if you can. Not only will it help you master certain skills but will give you an extra hireability boost someone else might not have. There are many certificate programs that will help you in the first step of gaining credibility. If you go this route, I suggest getting at least 2 complimentary certifications. A programming one and a database-related one.
Data Scientists need certifications to prove their skills and expertise. Certification programs are designed to test candidates’ knowledge of specific technologies, software tools, and programming languages. They’re also meant to help companies identify qualified applicants.
There are many different types of certifications available, including those offered by Microsoft, Amazon Web Services, Google Cloud Platform, Oracle, IBM, universities, and independent companies.
Each offers unique benefits, so be sure to consider them when deciding on a certification program.
What should I know before learning data science?
There are many different ways to approach data science, but the basic idea behind it is to take massive sets of numbers and turn them into meaningful insights.
First decided if you want to be on the analyst side or the programming side.
To succeed in either career path, you’ll need to master both the math theory and basic practice of data science related programming.
What area of data science should I go into?
Data Science is a broad field, encompassing many different areas within computer science.
There are many subfields within data science, such as Machine Learning, Statistics, Computer Vision, Natural Language Processing, etc. These fields overlap with each other, meaning that you could study any number of these topics and still end up working in a data science role.
It’s best to narrow down your focus to something that interests you, and that you feel like you’d enjoy learning. Once you’ve narrowed down your options, you can look into the job descriptions for the roles you’re considering and see what skills employers are looking for.
What are employers expecting from me in a data science role?
Outside of being able to have strong math and programming skills, employers expect data scientists to be able to communicate effectively with non-technical audiences.
While many data scientists may excel at communicating technical concepts, others struggle to explain complex ideas to non-technical people. To succeed in a data science job, you should be comfortable explaining data science concepts to both audiences.
Additionally, you should be able to translate complicated statistical models into plain English be able to demonstrate your ability to solve problems creatively.
If you are looking for career opportunities in data science, then you need to start thinking about what kind of data science position you would like to pursue.
- To get ready for interviews consider reading Q&A’s interview questions online. Could you have answered the question fully and understood the concepts behind the question?
- Consider doing a few projects or basic courses before entering a boot camp in order to be better prepped and get the most out of it.
- Go on job boards and see what employers are asking for. Look at the descriptions for skill sets and soft skills that are common.
- View online resume examples or LinkedIn profiles to examine the path others took.