Gaining experience without a job
Tags: womenintech, python, codenewbie, hackathon
Published: 2021-03-19 21:46:10

Once I felt that I was at a reasonable level of competence in Python I began applying for jobs, but every job wanted experience and there was no experience without a having had a job!

It was an ugly cycle that was difficult to get out of. So how can you make yourself employable without the elusive 'experience'. 

##Record your progress

![Alt Text](https://dev-to-uploads.s3.amazonaws.com/uploads/articles/9ymv2i87kvgkpbns7u1y.jpg)

The best piece of advice I was given is to use GitHub to showcase your work and I wish I had used it more and also more constructively. A well laid out GitHub account with regular code commits can be an amazing way to show what you have done and can do.

I created a GitHub account and uploaded the coding tasks set in my Udemy courses. Slowly I was able to build a picture of my skills, which was useful for me to see as well as for any prospective employers.

##Practice

Coding is like Math, it just takes practice. So it's important to find projects to work on, the more meaningful they are the more you will enjoy and learn from them. 

[Codewars](https://www.codewars.com/dashboard), [Codility](https://app.codility.com/programmers/lessons/1-iterations/) and [HackerRank](https://www.hackerrank.com/dashboard) are all excellent platforms to practice coding in a variety of languages. They provide coding tasks to solve which are available at different levels of complexity so you can build your skill set.

![Alt Text](https://dev-to-uploads.s3.amazonaws.com/uploads/articles/tez1n09qreudpxorggt9.png)

It can be difficult to think of 'what' projects to do. For data analysis [Kaggle](www.kaggle.com) is a treasure trove of ideas to work on and has pointers to lots of useful datasets. You can attempt to replicate analysis workbooks that you see on the site or submit your own work to Kaggle. Commit your work to GitHub at the same time as it maintains a picture of your journey.

##Use opportunities to learn from others

![Alt Text](https://dev-to-uploads.s3.amazonaws.com/uploads/articles/382vz9i64lw13g3kekth.png)

Hackathons are a great way to practice your skills and network with like minded people. Pre-pandemic they were also a free food-fest and late night coding session. 

I took part in the TrueCue Women in Data Hackathon in late 2020 which was conducted remotely with 200 participants from over 40 countries. This was my first experience of working with 'real' data analysts and gave me a unique opportunity to learn how data analysis worked in practice. It was a lot of fun and it felt meaningful to work on a project with an objective. Hackathons are also great places to network and build contacts in the industry. 

[7 best places to find Hackathons](https://dev.to/deveshb15/7-best-places-to-find-hackathons-2d10)

LinkedIn, Twitter and other social media platforms are great places to link with others in the area you are interested in. During the pandemic there were an abundance of free courses online. It just takes a bit of research and time to find the right thing to focus on. Follow your interests, you can't possibly learn everything and follow everyone, it's counter productive. Find the people that push you to be inquisitive and make you look at your next steps. And take courses that fit into your long term plan or add wider understanding to the work that you are doing.

##Volunteer

A friend mentioned a voluntary position on a data insight group who needed a secretariat, which I took on. While in this position I was in contact with data analysts in the charity sector and gained valuable insight into the way data was being used and the nuances and complexities that surrounded data usage. 

Through this voluntary position I was approached by someone in the group to conduct an analysis of funding in the charity sector. It was an invaluable first job, for which I had already gained some background. 

For Data Analysts [DataKind](https://www.datakind.org/) is also an amazing place to volunteer on data analysis projects. It's valuable work and experience combined.