data science for non technical background

In 2020, the global revenues for big data and business analytics are set to reach $210 billion. Naturally, because of increased demand, there has been a shortage of data science professionals and firms are reaching out with open arms to those looking to embrace the industry and join the workforce. 
However, the gap between existing skill sets and required skills is very broad, which has no doubt fuelled the gap between supply and demand. That said, it is a lucrative job opportunity in a field that is set only to grow and permeate into other industries regardless of how distant they may have once been. 
Those looking to close the gap and jump from a non-technical background to data science and related fields, do not fret! These tips will guide you on how to get a foot in the door and stay at the top of recruiter’s minds when new and competitive job opportunities pop up. 

Here’s how to get into data science from a non-technical background:

Identify your dream job

Since data science is such a wide field, streamlining your ideal job role and working towards it, will allow you to set goals and eliminate skills you may not need at this point in your journey.

In Technical Jobs Most Roles Require these Skills

  • Mathematics
  • Statistics
  • Programming
  • Business knowledge

This is because of the role data scientists and analysts play, in explaining insights to stakeholders and experts from other fields. The long-term aim of data science in the context of business is to derive insights that can drive business planning and future goals like increasing revenue, powering sales and recruiting top talent. However, the technical skills that would aid in your dream job are decided only when you understand which path you want to follow. Another crucial step is evaluating what current knowledge is applicable within this field, despite being sourced from another field. This is especially true of graduates in economics, mathematics, statistics or business studies– facets of these streams are well-embedded in data science, so make use of them. The first step to learning is knowing what you need to learn!

Learn new skills through a data science course

Data science is a complex field interwoven with facets of different industries. To be able to get a headstart in the field as a novice with little to no knowledge, candidates must upskill by enrolling in a well-outlined course with top educational institutions or course providers. The ideal curriculum should cover the following topics: 

  • Basics of programming (Java, R, Python)
  • Deep learning
  • Data visualisation
  • Statistics and probability
  • Big data handling

This is the easiest, most organised way to approach learning about the field because if one were to embark on the research journey alone, it would be time-consuming to gather relevant resources and understand where to begin. Additionally, to set a mission for oneself to learn all skill sets within the data science umbrella is nearly impossible. Certain skills are also based on experience and people-handling, so the best place to get a headstart is a data science-oriented course. These courses are usually curated by veterans in the field and come with the added benefits of career counselling, placements and mentorship programmes with industry experts. 

Read also: Top 5 Technical Skills you need to become a Data Scientist

When there are a plethora of courses out there, these questions will help choose the best out of the lot:

  • Which course comprehensively covers what I want to learn?
  • Which of these courses only rehash topics?
  • Which of these courses provide practical experience alongside theoretical knowledge?
  • Which courses have the best reviews from students in similar situations?
  • Which of these courses is affordable but also worth the money being put into it?
  • What sort of reputation does the offering institution have?
  • Where have the ex-students of the course been placed within the field?

A word to the wise– do not jump directly into paid courses without asking the above questions. Sometimes, you may be lucky enough to find a free course or an open-source programme, that gives you just the jumpstart you need. Once you have gone through that and understand what you want out of the course, you can then decide which paid certification you want to opt for. 

Find mentors in the field

No matter which field you choose to start afresh in, it is always difficult to find an inroad. It is the same with data science, but finding a mentor can benefit hopefuls looking to enter the field as a non-technical individual. Here are the perks of finding a mentor with nothing less than 5 years of hands-on experience in the data science field:

  • Networking: Mentors can introduce fresh candidates to recruiters and veterans in the field and at top companies, which would help secure the candidate’s future or, at the very least provide a helping hand.
  • Industry insider tips: After years of putting theory to practice, mentors are a goldmine of industry knowledge, understanding just how to put skills to use. They can also impart useful lessons on how to develop soft skills such as people management, dealing with deadlines and collaborating with other teams in the pursuit of business goals. 
  • Sounding board for questions: Mentors may have all the answers to questions about the industry, available job roles and potential professional growth. They are also especially useful for budding ideators and entrepreneurs to pitch their ideas again and receive constructive feedback in return. 
  • Long-term relationships: Building a relationship with a mentor can be beneficial not just at the start of a career but throughout. Mentors are often people to fall back on for advice and strength. They can be invaluable in times of insecurity and doubt as they most likely would have gone through similar highs and lows and have grown from it.

Build practical experience

Practical experience is the linchpin of landing a data science job in the most reputed of firms. This does not mean a high-stakes, futuristic project, although that would help the cause. Practical experience could also be in the form of smaller personal projects that came from experiments with tools and ideas. This portfolio is a display of interest and passion to transfer into the field and a chronological statement of attempts made to learn the tools of the trade. Hosting it on GitHub opens the floor to feedback from experts and writing content around it on Medium or a personal blog spreads the word and places you on the radar of recruiters.

These practical projects could be

  • A part of organised courses: Most courses today offer a practical element where students can apply theoretical knowledge, technical skills and creative ideas to build data science-driven projects. Often, these are marked by industry experts, so such validation from a veteran in the field will add a lot of weight to any resumé. 
  • Personal undertakings: Languages are best learnt through practice and this holds through for coding languages within the data science field. Personal projects are a great way to build technical skills without the pressure of time-bound tests and being graded. This is also a good way to evaluate one’s comfort levels with the field.
  • Mentor-led projects: Creating a project with the hands-on help of a mentor is a surefire way to catapult into the industry on good terms. The perks carrying out mentor-led projects is the fact that they can provide invaluable inputs with every step of the way to understand whether this was the best way to carry out the project. When hit with roadblocks, mentors can encourage creative thinking and finding solutions without getting stuck in a rut.

Keep reading and learning

The world of data science is ever-changing and almost always in the news. It is also the subject of some interesting non-fiction and informational books, so consider reading up if intending to move into data science from a non-technical background. Reading the papers or long-form journalism articles written by industry veterans provide insights into crucial industry trends and potential job opportunities. It also draws focus on what skills recruiters want to see in their employees, which can help streamline learning goals. 

Python Books list for Beginners

  • Python for Data Analysis
  • Machine Learning for Absolute Beginners
  • Python Data Science Handbook
  • Deep Learning with Keras
  • An Introduction to Statistical Learning

Also Read: Top AI Books for Beginners and Top ML Books for 2020

To continue your learning and expand your horizons beyond textbooks and course materials, consider exploring these audio-visual and written resources:

  • 24 Ultimate Data Science Projects To Boost Your Knowledge and Skills
  • Storytelling with Data: A Data Visualisation Guide for Business Professionals
  • Webinars like Data Science Connect 

While it is not the easiest task to enter the field of data science with a non-technical background, it is not impossible either. It is a difficult path to tread since there is a lot of learning, unlearning and relearning involved.

Getting the basics right before moving on to higher applications is a good approach, as is connecting with a mentor or industry veterans to learn about the field in action. Engaging with the larger data science community and keeping abreast of developments within the field will look just as good on resumes as practical projects and course certificates!

Check out the data science courses from Great Learning to upskill in this domain.

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