How to get a job in data science without a developer background

Data scientists jobs are plentiful however many find it difficult to get a job without a developer or computer science background. Here are my four key tips for getting into data science without a traditional background.

Stephen Allwright
Stephen Allwright

Data scientist, machine learning engineer, and data analyst are jobs that are extremely in demand at the moment, and according to many in the industry that demand exceeds supply, which must mean that opportunities are plentiful. This is not the experience of many however. Especially for those who do not have the typical CV.

I myself had a difficult transition into data science having not come from the typical background and having an atypical CV. So this blog post is my advice to those of you who are looking to get into the industry but don't currently have the background to achieve it. I will outline the four key areas that I would advise anyone looking to get into the industry to focus on.

Data science bootcamp courses

Good data science courses do not need to cost the earth. I highly recommend starting your journey with a good low-cost bootcamp course, of which I can highly recommended this fantastic course on Udemy which I myself began with. A good bootcamp course will cover all the key areas in data science, from data preparation to deep learning, and give you that broad understanding of the subject matter before you dive deep into specific areas of interest

All the courses you require can be found on platforms such as Udemy and Coursera. Do not feel pressured into spending thousands of dollars on expensive programs from well known universities, this is more often than not unnecessary, and there are many cheaper ways of learning the skills you need that still look as good on your CV.

Python for Data Science and Machine Learning Bootcamp

Python for Time Series Data Analysis

Deep Learning with Python and Keras

Probability and Statistics for Business and Data Science

The Complete SQL Bootcamp 2020

Complete Guide to TensorFlow for Deep Learning with Python

The importance of a data science portfolio

When your CV doesn't display data science experience, you need to show it to them another way, otherwise recruiters will assume you don't have the skills. Full stop. It is therefore incredibly important to put together a comprehensive portfolio of projects that display what you can offer them. Ideally this portfolio would be hosted on GitHub and show a range of skills from basic data preparation to an end to end machine learning project with real world data. As an example, my data science portfolio is available to see on GitHub and it displayed all the skills I had learned prior to applying for jobs and I made sure to attach this to CV's and applications wherever possible.

Master the basic statistical and machine learning skills

This is rarely pointed out, and greatly undervalued as an area of focus for people wanting to enter the industry. When learning data science through courses, books, or projects it is commonplace to set a significant amount of focus and time on learning machine learning modelling and the construction of neural networks. Whilst important skills, they are - for the most part - the icing on the cake for a data scientist and not the cake itself. A commonly stated statistic is that 80% of a data scientist's job is the preparation and understanding of data. I can testify to this, and would therefore rather hire a data scientist who understood how to transform data, had a solid statistical understanding, and could quickly visualise insight rather than someone who was just interested in creating models, this type of candidate may be better suited to a machine learning engineer role. Therefore I highly recommend that after gathering a broad understanding of data science topics, you focus on mastering the basics, such as:

  • Handling outliers
  • Feature engineering
  • Distributions
  • Data wrangling
  • Visualisation
  • Hypothesis testing
  • Metric understanding

Another benefit to mastering these is that this is often what technical interviews focus on, rather than the development of machine learning models.

Networking

The last key area of focus will bring forth groans from most readers due to how often it is mentioned. Networking. Some people love it, others love to hate it. There is no denying, however, the benefit it brings to job hunting. Employers like to hire people from recommendation, or even better from people they already are aware of, as it seems like a candidate with less risk. As an outcome from this understanding, try to make it easy for employers by being active in your local data science community and also online, Twitter especially is a great way to network.

Settle in for the long term

Transitioning into the field of data science is not something that happens overnight, it will often require months or years of work to achieve. And so it is key to have a long term mindset with the areas listed above. Studying a little bit consistently every day will have a huge impact on your career, however studying for a whole day once a month will have little or no impact. So keep at it, keep plugging away, and good luck!

Industry

Stephen Allwright Twitter

I'm a Data Scientist currently working for Oda, an online grocery retailer, in Oslo, Norway. These posts are my way of sharing some of the tips and tricks I've picked up along the way.

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