Becoming a Data Scientist in 2021

Demand for data analytics has boomed. Here’s how to engineer a shift into data science

Back in 2017, an IBM study found that 90% of all data in the world at that time had been created in the last two years. In today’s increasingly digitised world, there is no denying that big data has become a highly prized resource. Plus, the amount of data is increasing exponentially. Indeed, in the last year alone it has been estimated that every person on the planet generated 1.7 megabytes of data per second.

Featuring data from a variety of sources – digital media, web services, business apps and IoT connected machines – analysis of big data is being used to prevent money laundering, optimise disease management, streamline construction projects, predict weather patterns, optimise manufacturing production, and anticipate what customers will want next.

Today’s industries and governments increasingly depend on data to anticipate trends, understand behaviours, streamline market or audience engagement, define and visualise strategies, right size operations, predict machine failures, make better and smarter decisions – and more.

This data analytics boom means that demand for data scientists has skyrocketed.

Making sense of data

Organisations everywhere need data scientists to make sense of the massive data pools they create and collect. Back in 2019, the LinkedIn co-founder Allen Blue estimated that demand for data-science based jobs in sectors like education, marketing and manufacturing alone had jumped by a staggering 15-20 times in just three years.

Staying relevant in today’s fast-moving world means businesses and governments constantly need to innovate, making highly informed – and right – decisions about which products and services they need to invest in. Which is why turning data into valuable and actionable insights has become mission-critical.

However, while organisations collect and store tons of information in their databases, a much larger amount of data that requires handling is unstructured in nature. In other words, it comes in many different forms, sizes and shapes which makes it difficult to manage and analyse. Which is where data scientists come in.

Problem is, there are not enough data scientists to go round. Which makes entering the data science field one of the hottest career options on offer today.

What it takes to engineer a career shift

In recent years there has been a strong focus on encouraging more people into higher education in STEM subjects. But the expertise for becoming a data scientist requires study in developing skills that mix both art and science – STEAM (science, technology engineer, art, and maths). In other words, data scientists require a unique blend of creative, academic, and technical skills sets: you’re an investigator, a coder, a scientist, a mathematician, and a storyteller!

Organisations now recognise that a wide range of individuals have the right thinking processes to work in data science. As well as people from computer science and analytic backgrounds such as statistics and engineering, this includes people with backgrounds in physics, chemistry, and biology. Big tech firms have also found that liberal arts graduates are also proving highly successful in data science roles.

With organisations eager to reskill people and retrain workforces in data science, let’s take a look at the five critical steps that will need to be mastered to make the shift to a career in data science.

1) Start with science: maths skills

The ability to learn and understand machine learning techniques is key to data science. To do this, you will need to be good at maths. Statistics is king in data science, especially probability theory, so gaining an understanding of these fields at a fundamental level will be essential.

Linear algebra forms the basis for machine learning algorithms, like those employed in Spotify’s song recommendations, so getting to grips with the basics will be important. Similarly, aspiring data scientists will need a strong grasp of calculus in order to understand how machine learning neural networks use backpropagation to learn new patterns.

Finally, data scientists will need to confidently handle things like functions, variables, equations, and graphs as basic skills, while more complex and broader scientific knowledge – like binomial theorem and its properties – is also important.

2) Leverage the right tools

Today’s data scientists need to be confident at utilising a range of data analytics tools. Start by mastering the top basic tools first before diving into exploring other relevant tools that might be helpful for more specialist data analytics or data visualisation challenges.

The top basic tools in use today include Power BI, which features a drag and drop interface that makes it an easy data visualisation tool to use. Tableau, which supports the creation of simple and attractive models that make it possible for anyone to understand data. Finally, become familiar with and adept at working with AWS; this tool makes it easy to build visualisations and perform statistical analysis in the moment to generate business insights from data fast.

3) Grow the muscle: acquire a coder’s mindset

The sooner you can start learning and mastering programming languages, the better. Python, R and SQL are considered the tools of the trade and data scientists use them regularly.

One of the most commonly used programming languages used today, Python is ideal for those just starting out in data science. Offering an intuitive and easy to learn syntax that makes it a popular choice for beginners and professionals alike, Python is the preferred language in areas of machine learning, deep learning, AI, and other data science fields.

Prized for its ease in handling statistical analysis and programming, R provides large sets of libraries and frameworks that makes it ideal for developing machine learning algorithms and creating statistical models. Any company that wants a large collection of its data to undergo analysis and visualisation will be looking for developers proficient in R.

Finally, used for updating, querying, and manipulating databases, SQL is the lingua franca of data analysis and is the programming language that is used to ‘talk’ to relational databases. Easier to learn than general purpose programming knowledge, it is straightforward to become proficient in SQL in a matter of months.

4) Never stop learning: find a mentor

Finding the right professional mentor will enable you to reach your data science goals in a matter of years, not decades. While acquiring the basic skills will help set you on the path to entry-level roles where you can learn rapidly on the job, guidance from someone who really knows what they are doing will help you build on your strengths and identify which future skills to develop.

A good mentor will help you navigate work situations, provide constructive criticism, point you at technical resources and expertise, and share different perspectives on solutions. If good in-person mentors aren’t available to you then take ownership of your continuing development by seeking out virtual mentorship.

5) Tell data’s rich story

Business and leadership skills separate the best data scientists from the rest. As well as knowing their own field and the industry in which they work, data scientists also have to communicate effectively with non-technical people across the organisation.

To tell data’s rich story, those working in data science need to develop their communication, collaboration and proactive problem-solving skills in relation to specific business cases. Ultimately, it is this integration of technical know-how with soft skills that makes a well-rounded data scientist who is respected as a trusted adviser to the business.

With big data now a force to be recognised, industry leaders are eager to tap into its many benefits. Today’s data scientists deploy their technical and creative skills to tackle the real-life problems organisations face and becoming effective in the role depends on being able to communicate data findings in a language that lay audiences will understand. Increasingly, that means becoming adept at working in cross-disciplinary teams in which everyone can deploy their individual talents to generate true value from an organisation’s data.