The key to becoming a data scientist without any experience

We all hope we have prior knowledge of selecting the right profession and plan accordingly to get there, but the real world is not always a straight road, and that's exactly what makes it interesting. With new businesses and lines of employment developing all the time, there is a rapid technological transition. And in this rapidly changing world, data science offers the chance to have job stability, a high-paying income with the potential for expansion, and the ability to work from anywhere in the globe. However, for those curious souls, specializing in data science is a no-brainer.

Who is a data scientist?

Data scientists gather and clean vast volumes of data, maintain dashboards and databases that are easy to use, analyze data to solve issues quickly and do experiments, develop algorithms, and present data in appealing visualizations to stakeholders.

Since data science is an elevated, in-demand career field with great job opportunities, it's a great time to examine whether the right career path for you is to become a data scientist or not.

The fantastic news is, to become a data scientist, you do not need past experience. There are several ways to develop a set of skills in data science on your own.

Start writing and become an efficient writer

Thanks to their ability to transform vast data sets into persuasive visualizations that tell tales to the masses, data scientists are natural storytellers. Because of this, it only makes sense that aspiring data scientists should write about their job in order to show potential employers their communication skills. The advantages of starting a blog or writing on a forum like Medium have been touted by many data scientists. The advantages of writing don't stop making you a happier, more stress-free person, despite what many say - writing can also improve your career in data science. The published articles will become a part of your professional resume. Moreover, posting on a website that pays you for your job will show recruiters that your experience is so respected by people that you're actually being paid for it.

Build your own startup

Just go with the breeze, rather than constantly fighting an uphill war, build your own consultancy company for data science. We all know how frustrating it is when you have sent out a hundred applications, just to get letters of denial and total silence. So, if you're not hired by someone, recruit yourself!

Freelance work is one of the most frightening things people can do to make money quickly, and it's certainly not for everyone. However, at the end of the day, it's a decent alternative to bash your head against a wall waiting for prospective employers to get back to you (or not).

Why not take on some freelance customers if you have the expertise and confidence? It is a win-win scenario. Without having to go through the misery and anguish of the recruiting process, you get actual experience (mind you, there can be just as much pain and suffering doing freelance work, which is why it's not for everyone). The beauty of recruiting yourself is that you will be able to accumulate job offers from coveted businesses due to real-world experience and can walk away any time.

Learn some math

Data science can be a quick change if you come from a quantitative background. You need to get to the base of data analysis before examining data with high-tech software, which begins with projecting data points on graphs along the X and Y axes and identifying associations and patterns between various variables.

Here are some suggested math concepts to learn in order to make sure you can write successful code and draw correct conclusions:

Regression analysis
Bayesian thinking and modeling
Markov chains
Statistical methods and probability theory
Probability distributions
Multivariable calculus
Linear algebra
Hypothesis testing
Statistical modeling and fitting
Data summaries and descriptive statistics

Build your portfolio

It makes sense that an organization does not want to recruit someone from a data set who has not solved any issues or who has not been able to draw any conclusions. You can show employers that you have that inherent interest and motivation that is necessary for data scientists to be successful on the job by developing your own projects.

To direct business effect, data scientists need to be able to link their prototypes. While in your cover letter and resume you should certainly concentrate on your data science expertise, you should address previous roles where you used Microsoft Excel or built business, interaction, teamwork, and other useful skills.

As long as you're prepared to take opportunities that are out there for you to obtain job experience you will be able to succeed. Luck appears to favor the bold, and that isn’t more valid than for people looking to make it big. You'll be well on your way to landing the first desirable job in data science by practicing a little imagination, grit, and perseverance (and also maybe some patience).

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