8 Ways To Get A Job In Data Science With No Experience

January 5, 2022by Robin
data science job search

From a recruiting standpoint, there are questions that get asked all of the time during a job search.

  • How do I get my first data science job after I graduate?
  • How do I get a job in data science after taking a boot camp or teaching myself?
  • How do I get started in data science with no experience at all?
  • What area of data science should I go into?
  • What are employers expecting from me in a data science role?

This is taking into account that one does not have a job after completing an internship or is a first-time job seeker. After school or training, one question remains in the job process. Where to go from here to actually land a job?

Suggested 8 Steps to Get a Job in Data Science

  1. Learn a programming language in the area of data science job you are interested in.
  2. Get an internship either through school, personal connections or job boards.
  3. Work on industry projects that allow you to use real-life situations to build a portfolio of work.
  4. Be able to explain what interested you in the field.
  5. Make a list of resources for continuous learning to stay on top of technology and the industry you are interested in.
  6. Practice communication and interpersonal skills.
  7. Practice data modeling and data visualization with your projects.
  8. Your mathematics and statistics skills need to be practiced.

Learning a Programming Language

The most common technical skills employers are looking for are Python, SQL, R, Scala, Java, Javascript, MATLAB, C, C++, TensorFlow, and Julia.

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.

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 work that will align with your skillset.
  • 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

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? Do you truly have a deep understanding of what you say you know?

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.   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 professional experience or use it to work with potential employers who can convert that experience into a full-time job.

Working on side projects allow one to practice a skill set by coding, using mathematical skills, gaining domain knowledge, and have an online presence to show future employers through a portfolio profiling your best work.

Create a List of Resources

This list of resources is key for any data scientist/machine learning engineer! You should have a list of resources that you go to for answers.  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.

You can also continue your education by engaging in free or paid online courses to keep up relevant skills.

Practice Communication and Interpersonal Skills

This is a 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 apply. You can self-evaluate how well you worked with 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?

You can also ask others where your weaknesses are like an instructor, friend, or past co-worker.

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, and get the most value out of it and at the same time 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, calculus are all used to create algorithms to make predictions.

Additional Tips:

  • To get ready for interviews consider reading Q&A’s interview questions online.
  • Consider doing a few projects or basic courses before entering a bootcamp in order to 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 resumes URLs to view the path others took.