How to get into data science

Ironhack Data Analytics

Put simply, data science is the process of collecting, questioning and analyzing data. Data scientists formulate questions, which form the basis for data gathering or "mining". The data they collect is then trimmed down, analyzed, visualized, and used to create a model that attempts to answer the original question as thoroughly as possible.

Who can get into data science?

Sounds complex? Don't underestimate your abilities. Entry-level posts in data science are achievable for anyone with an interest in tech, and obtaining data science credentials can open a lot of career pathways.

You don't even need to be working in tech already. Whether you're fresh out of education or considering a career change, IronhackBootcamp courses give you the knowledge and skills you need for working with data.

Making a change is easier than you think. If you've decided to join the world of data science, here are seven tips to get started.

1. Decide on your focus

Careers in data science can follow several pathways such as data analysis, machine-learning engineering, or product development. When you start your training, you won't know exactly where your career will take you. However, as your course progresses you'll probably begin to develop a clear interest in a specific area. When you identify this area, invest time and effort in mastering it. Having a specialist niche where you're truly an expert will only benefit your career.

2. Brush up those maths skills

Entry-level posts in data science are usually for analysts. This involves some degree of mathematical knowledge such as plotting data points on graphs and finding trends and correlations between different variables. These mathematical topics are generally covered by data analysis courses, but having some initial knowledge of concepts such as statistics, probability, and linear algebra will help. If you're waiting to start a training course, use the time profitably by brushing up your maths skills.

3. Get to know the language of programming

Knowing the fundamentals of programming languages such as SQL and Pythonis a necessary skill for data scientists. A data science course provides basic training in these and other programming languages, but it is worth focusing specifically on one. Aim to acquire an in-depth mastery of its structures, functions, and uses. This gives you a good basis for developing further skills and knowledge such as machine-learning algorithms.

4. Take a data analytics course

While there is a lot of overlap between data analysis and data science they're not the same and can offer separate career pathways. Data analysts manage data collections and identify trends while data scientists do a more detailed interpretation of data, applying coding skills and undertaking mathematical modeling.

Getting an entry-level post as a data analyst is a great way to start a data science career. At Ironhack, our Data Analytics Bootcamp is a hands-on nine-week full-time course or a 24-week part-time course that equips you to start working in the tech industry. It gives you an excellent grounding in statistics, Tableau, SQL, Python, APIs, and web scraping. As your course progresses, you'll work on individual and collaborative projects that can form the basis of your resume and portfolio.

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5. Get some practical experience

Finding a first job can be a challenge as most posts advertised are looking for some practical experience. Take every opportunity you can to get this experience even if it's poorly paid or even unpaid. Search platforms such as Fiverrand Upworkfor freelance work that fits your skill-set or apply for part-time jobs or internships using job boards and social media sites. At the same time, use platforms such as Leetcodeto enhance your skills and practice technical interview questions. You can even have some fun solving Leetcode's coding conundrums.

6. Showcase what you know - build a portfolio and more

As your training comes to an end and you start to apply for technical interviews, a portfolio will be your biggest asset. Resumes and covering letters are important, giving interviewers a chance to review your education and training as well as understand your reasons for applying for a post. However, your portfolio showcases your skills, allowing you to stand out from the crowd.

Take time creating a concise portfolio with examples of the projects you worked on during your training or any freelance work you have done. Use the best-quality images you can and accompany one or two of the projects with a back story. Explain why you undertook it and the stages you went through. Point out anything you are particularly pleased with, talk about any difficulties you overcame, and say what you might do differently in the future. It's also a good idea to create both a physical and online portfolio and add links to it on your social media pages.

7. Never stop learning

You'll acquire a lot of skills and knowledge during your training course but don't stop learning once you've landed your first entry-level data science post. Focus on areas where you need to improve as well as learning more about subjects that interest and motivate you.

Take training courses at work or online courses in your spare time as well as reading around your subject in books, blogs, and periodicals. Kagglecompetitions are a fun way to hone your data gathering skills while taking part in open-source projects allows you to collaborate with others.

Put some data science conferences in your diary such PyCon US. They keep you up-to-date with the latest tech developments while also offering great networking opportunities. And don't be afraid to sign up for future data science bootcamps with Ironhack. New skills emerge all the time, and mastering them can make or break your career.

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