Career Change: Become a Data Scientist

6 ways of re-training to become a data scientist

• Online courses
• Bootcamps
• Kaggle
• GitHub
• Self-education
• BSc or MSc in data science

3 strategies to get that data scientist job as a lateral recruit

1. Get further education (as an autodidact, in bootcamps, online classes, or at university). Andapply for data scientist jobs right away.
2. Build data science skills by training on-the-job. Then switch into a data scientist role at your current place of work.
3. Cut your teeth working as a data analyst. Getting hired into data analyst positions will be much easier for lateral recruits than getting a data scientist job right away. Having gatherd work experience as a data analyst, the odds of you landing that data scientist job in one or two years are much higher.

Not every data scientist has earned a data science degree. Not every data scientist has worked as such for their whole career. So how easily can you pivot to a career in data science? It depends. Laterally entering data science is very feasible and won't take that long if you already have a good command of coding and/or statistics. But even without these skills, it's still possible–with a good plan to quickly close your gaps and effectively play to your existing strengths.

In this blog post, we'll take a closer look at three strategies for pivoting to a data science career: which one suits which type of career changer? What learning and training opportunites do career changers have? Which ressources can they tap into?

How to find a data scientist job as a career changer? (3 + 1/2 strategies)

Strategy 1: Apply for data scientist positionsThe

Just apply, nothing to lose.

For which type of career changer does this strategy work?
This strategy is most promising if your background is in fields related to data science (ideally STEM. Economics, business studies, sociology, and psychology also ok). You can code, you know statistics, and you may already have professional experience with data analysis. Ideally, you can showcase data science-specific coding skills through your Kaggle rankings and/or GitHub profile.

At what kind of companies does this strategy work?
At companies with an established data department. Most of the time this means larger companies. the stragey might also work with tehc-forward medium-sized companies. You won't be their first data hire. This means that someone int their staff is able to properly assess your data science skills. Therefore, such companies are more likely to hire career changers as data scientists. Companies that aren't digital-minded will find it difficult to recognize qualifications other than a relevant university degree.

Strategy 2: grow into your new role as data scientist–on the job

Try to get more exposure to data science in your current job. By means of advanced training or new data projects.

Strategy 2a: persuade your company to support your data science re-training

Enroll in advanced training on data science if offered at your company. If not: look for trainistrategyng opportunities yourself and persuade your company to support you (e.g., by allowing you to dedicate a share of your working hours to learning, or by covering the costs of external training courses).

For which type of career changer does this strategy work?
You've been with the company for a while, you work a lot with data and your Excel expertise is valued. You've read up on how data scientists work and what they need to be able to do. And you have the confidence to catch up.

At what kind of companies does this strategy work?
At companies that already employ data scientists and are looking to hire more. These also tend to be large companies and tech-savvy SMEs. This stragey will work best at companies that already provide for advanced training on data science topics (you won't have to do as much persuading).

Stratgy 2b: initiate data science projects at your company

Pitch your company a data project to which you may dedicate a share of your working hours. Obviously, the data project should align with the company's goals (e.g. improve workflows in your team). If you get the OK, you can improve your statistics and coding skills on your current job. If the if the project succeeds, it'll put yourself in a good spot when applying for a data scientist role.

For which type of career changer does this strategy work?
Similar to 2a: you've been with the company for some time and you've already worked with data. You've started reading up on data science theory and you've programmed one or two scripts for data analysis. You're confident to carry out your own data project.

At what kind of companies does this strategy work?
At small and medium-sized enterprises that haven't done any data science projects yet, and would like to change that (without immediately hiring an entire data team).

Strategy 3: work as a data analyst… to become a data scientist

Apply for jobs as data analyst. In this role, you'll gain experience in working with data. Ideally, you'll learn to code on the side. This professional experience will increase your chances of scoring a data scientist job later on. Either at your current place of work or at another company.

Compared to career changers without any experience in data, data analysts are much more likely to get hired as data scientists (data analysts who know how to code even more so).

For which type of career changer does this strategy work?
Data analyst jobs come with fewer requirements than data scientist jobs. You should be familiar with descriptive statistics and you should be able to read and create statistical graphs (programming experience not necessary).

At what kind of companies does this strategy work?
At companies hiring data analysts (a pretty large set).

Data science for career changers (6 resources)

Regardless of which platform or resouces career changers rely on, they should always prioritize the basics: statistics and Python.

• Data science online classes are available on many platfroms auf vielen (e.g., Coursera, edX). From 101s to advanced classes. Some free, some paid.
• Data science bootcamps are crash courses, usually held over the timespan of several weeks up to a few months. Many bootcamps are available online, plus a few offline-options (price tag: usualy several thousand euros).
• Getting a data science degree is the most time-consuming option, but it will earn you the most knowledge and skills. In addition, university degrees carry way more weight with employers than bootcamp certificates or certificates from online courses. You can pursue a Bachelor's or Master's in data science part-time, extra-occupationally (i.e. while continuing to work), and at distance learning institutes.
• Kaggle is perfect for learning by doing. As soon as you have the absolute basics down, you can enter Kaggle competitions. You can check out how other users have solved previous competitions and discuss current competitions with the community. (Welcome side effect: high rankings on Kaggle look good on applications.)
• GitHub is another place, where people share code for data science projects. Super useful, if you just started coding.
• Self-study is another way to learn data science and coding. Many textbooks on data science, Python, statistics, etc. are available online for free. Do also check out university websites: lecture materials are sometimes made publicly available. On YouTube, you'll find lots of recorded data science lectures as well as coding tutorials.

Who can change careers to become a data scientist?

The more of the essential skills you already have, the better your chances of finding a data scientist job as a lateral recruit:

• Very viable chances (limited effort required): it'll be easiest and quickest for candidates with solid knowledge of statistics and coding skills. This applies to many career changers with a STEM background, as well as economists and social scientists who know how to code. To-dos: Not a lot of catching up to do, in order to be able to work as a data scientist. You can go through the most important data science models (possibly also machine learning methods) and check out the most common tools and Python packages.
• Feasible (some effort required): pivoting will be more difficult, but still totally feasible for sociologists, psychologist and economists who have hardly coded before, but know their statistics (+ Excel and statistical software). The same goes for computer scientists who have not really worked with data before.
  To-dos: If you lack coding skills, get coding (focus on the most popular data science packages) and check out the most common data science tools. If you know how to code but you're not particularilly well-versed in statistics, catch up on data science theory and applications.
• Tricky (a lot of effort required): Entering the data science profession will be much harder for those who neither know how to code nor how to coduct statistical analysis. They'll have to work on all aspects of the data scientist's skillset–difficult but not impossible!
  To-dos: The surest way to becoming a data scientist in this case: entroll in a bachelor's or master's program for data science (or a related major).

Data scientist without studying–can it work?

You don't have to study data science, but you probably need to study something (the more closely related, the better). Data scientists work with scientific methods and standards, so it'll be tough without any academic background. In addition, the vast majority of companies do expect data scientist to have earned at least some university or college degree. (Unfortunately, there's no such thing as data science apprenticeships.)