4 Tips to switch from a non-IT background to data science

How to switch from a non IT background to data science

Data science is an evolving and exciting space that presents diverse opportunities across an array of industries and businesses. Every year, thousands of people around the world are upskilling and becoming data literate. Gaining skills in data science, data analytics, machine learning and AI, will enable you to either innovate by incorporating these skills into what you already do, or give you the opportunity to dive into a specific data based role, where you can make a significant contribution using your new data skillset.

But, what if you are from a non-IT background? Or, do not currently have any technical data skills at all?

If you are passionate about understanding data and are willing to build the necessary interpersonal and technical skills required to work in the data industry, it is entirely possible to upskill and a lot easier to make the career transition than you might think.

Here are 4 tips to guide your career transition if you are considering breaking into the data industry:

1. Determine why you want to make a career change to data science

The data industry is facing a global skills shortage and needs data professionals with practical skills training and the willingness to adapt to meet individual business needs.

However, employers in the data space are also seeking to hire data professionals from a range of industry backgrounds and skill levels, to help achieve their immediate business goals and stay competitive for the long term as they build out their teams, systems, processes and products.

So before you begin your upskilling journey, here are 20 questions to ask yourself to understand your own mindset and your short and long term career goals as well:

    1. What interests you about working in the data industry?
    2. What did you enjoy most about your last role / what are you looking forward to in your next role?
    3. How essential is it for you to expand your job prospects?
    4. Do you have previous experience learning a new tool / technique on the job?
    5. How would you like to be using data on the job everyday?
    6. What data related tasks do you think you would be good at?
    7. Do you enjoy problem solving and critical thinking?
    8. Would you describe yourself as a fast learner?
    9. How would you approach a project that required a tool you haven’t used before?
    10. Are you excited by the possibility of finding previously unknown insights from mass amounts of data?
    11. How do you handle pressure / working in stressful situations?
    12. Can you work within a team and also independently to meet deadlines?
    13. Are you confident in your soft skills or do you need to develop them further?
    14. What are 3 things that make you valuable team member?
    15. Are you someone that is open to all job opportunities in the data space or do you have a narrow pathway in mind?
    16. Are you willing to build your skillset to become job-ready?
    17. If you could choose, where would you like to be working in 3-5 years? (does your answer involve a particular company or are you more focused on what you’ll be doing on the job?)
    18. What would you like to achieve using skills in data science, analytics and machine learning?
    19. How do you define career success?
    20. What was the last thing you learned about the data market?

If you have taken the time to honestly answer the above questions, you will now have a better understanding about why you are looking to upgrade your skills and this will enable you to confidently begin your upskilling journey!

2. Learn about the data industry and your future job requirements

Strong business acumen and communication skills are vital if you want to work in data science. Data professionals must be able to relay complex information simply and be able to describe the data insights they are uncovering and formulate solutions based on this insight.

Every business wanting to keep up in today’s market is seeking to use data to inform everything they do. Industries currently trending towards a strong growth in the space include financial services, healthcare, marketing, retail, security, manufacturing, education, logistics, and government.

However, if you’ve been exploring various job boards trying to make sense of what a data professional actually does on the job – you are not alone. The data industry is still evolving and job descriptions and titles are still being defined across industry sectors based on their emerging needs. You may be surprised to learn that many hiring managers and recruiters in the data space, are also still learning how to determine the best data candidates to hire for entry-level, mid and senior data roles and projects.

Here is the general breakdown of what a data professional is expected to do:

  • Understand the business model and data flow processes
  • Data wrangling – collection, cleaning, verification, organising of data
  • Make sense of structured and unstructured data sets using advanced data analytics
  • Analyse real-time and historic data to identify trends and patterns using algorithms
  • Use predictive data modelling / exploratory data analysis to extract deeper insights
  • Automate processes using machine learning to optimise costs and resource allocation
  • Communicate / report / present findings using data visualisation to technical and non-technical stakeholders
  • Keep up with industry trends, emerging technologies and best practices
  • Maintain a solutions focused mindset with the ability to design experiments, conduct tests, evaluate outcomes and scale data strategies
  • Contribute to the business decision making process by providing applicable insights into how the client can optimise their products / processes to increase customer retention and revenue, improve experiences, adapt to market trends, plan for the future, and achieve their business goals.

As you develop your data experience, in addition to building and keeping your technical and soft skillsets up to date, consider specialising in an area of data that you are passionate about as the more you enjoy the data tasks you are completing, the more rewarding your day to day will be.

Bonus tip: Employers actively searching to hire are very interested to understand why you want to work in the data industry but they are also looking for professionals that can demonstrate their ability to perform on the job. This is where your communication skills and developing your portfolio of project work will serve you well as an aspiring data professional.

3. Understand the level of technical skills required for data science can be learned

Many professionals looking to upskill and work in the data science industry are taken aback when they learn data science involves programming and statistics.

This is the part where you take a deep breath.

Yes, data science, data analytics and machine learning involve the use of python programming and computational mathematics but the level required for your first entry-level role is achievable and teachable.

In fact, if you had an aptitude for maths / stats or basic coding in high school or university – you already have the basic level of skills required to upskill to data science!

Naturally, there will be a learning curve involved but if you are someone that is motivated, enjoys problem solving, has the patience to build their data skills, and you are trained by an industry expert in a structured learning environment – you will find you are not only able to retain the information you are learning but you are also able to quickly apply the knowledge in a practical sense.

Here are the steps you can take to become confident with the level of python and statistics skills needed for data science:

  • Conduct independent research into the topics, to start, click here to read this article about learning how to learn data science
  • Test your current skill level and complete some free online tutorials (observe how you feel when learning these topics – are you enjoying the challenge?)
  • Find an industry mentor / speak with a career consultant to plan out your upskilling steps
  • Build your support network – join a free online group and start a conversation with other aspiring data professionals
  • Start a study group and work on mini projects together!
  • Build your own data based project that solves a data problem in an area that interests you
  • Attend a data science industry event (online or in person) to learn more from experts and if there is a Q&A section, come prepared with your questions!
  • Enrol into a structured practical training program with real-time support
  • Keep building your portfolio with projects, document your progress and practise verbally and visually presenting your work

Employers will expect you to have applicable knowledge of key data science tools and techniques for quantitative and qualitative data analysis, but you will not be required to be an expert mathematician or advanced programmer to land your first entry-level data role and progress your career. Another thing to note is, although a data role in the industry will require you to find business solutions using data, a data role will not require you to relentlessly code on the job.

Remember, your objective as a data professional is to extract insight from data, analyse your findings, and present these insights to key stakeholders to make data driven decisions and improvements, so don’t stop learning, troubleshooting and practising your skills.

4. Become trained and certified by data science industry experts

The one thing everyone agrees on when it comes to breaking into the data science industry is, you can’t do it alone.

The fastest way to gain industry relevant experience that you can use on the job from day one – is to become trained by experienced industry experts.

In a learning environment, experienced data science experts have a wealth of knowledge they can share with aspiring data professionals but most importantly, they also possess the strategic insight to get you trained to the level required to land your first entry level role in the data job market.

For any professional looking to upskill, it is essential to determine how you learn best:

  • Are you a visual learner?
  • Are you a verbal learner?
  • Are you an aural learner?
  • Are you a social learner?
  • Do you enjoy hands-on training?
  • Do you retain processes faster when taught by example or by troubleshooting?
  • Do you thrive in a group environment?
  • Do you hold yourself accountable?
  • How do you feel about real-time feedback?
  • Would you prefer a combination of all or some of the above?
  • How long are you willing to commit to achieve your training?

It is critical to find a training program that can match your learning needs and can provide you with hands-on experience using data science, data analytics, and machine learning tools and techniques the data industry is demanding and using right now.

The Institute of Data works with experienced data science experts to train ambitious professionals. Our training programs feature the following to prepare you to work in the data industry and accelerate your pathway to a new role:

  • A practical curriculum that uses tools and techniques relevant to the data industry and teaches you how to use data to solve business problems.
  • A delivery method that is in real-time – so you can engage with your instructor and have their support as you are learning to apply your new skills.
  • A small class size and an instructor that is an industry expert with industry experience, not just an academic.
  • A certification process that focuses on your project work, the practical skills you have learnt (soft skills + technical skills), and your ability to apply these skills in an industry setting.
  • A tailored job outcomes program that includes a personal career coach to help guide you through your job outcomes journey.

If you are looking to make a career move into data and want to become certified in Data Science & AI – click here to learn more or schedule a call here today.

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