The Pretty Good Guide to Data Science

We breakdown data science from top to bottom with an in-depth look at the field, industry, and careers around it.


It’s estimated that over a trillion megabytes of data are generated every day. Knowing how to sort and make sense of that data, as well as knowing how to use it in a meaningful way, is more important than ever.

That’s where data science comes in.

Not only is there an unprecedented amount of data being created, but this data has more of an influence over our daily lives than ever before. That’s why the field of data science is growing year-by-year, with an increasing demand for skilled individuals in all kinds of data science roles.

If you’re interested in getting into data science, but aren’t sure exactly what it entails, this is the article for you. We’re going to cover the various uses of data science, the diverse skillset you need to excel at data science and the careers you’ll find within the field.

But first, let’s slow down and simplify it, with a clear definition. 

What exactly is data science?

Data science is the process of collecting, cleaning and sorting information, then making sense of it to extract its full value.

Data science can be separated into four fundamental areas:

  • Domain – this is the awareness of the core business of the company and how the company functions. You can have all the data in the world, but you need to know how it relates to your business or organisation to use it effectively
  • Mathematics – understanding the numbers and statistics that underpin any data science model is essential. Everything from statistics and probability to linear algebra and calculus are key
  • Computer science the technical side of things: coding, databases, machine learning and distributed computing are core areas
  • Communication – the way understanding data is expressed. This can verbal or non-verbal

What is data science used for?

Broadly speaking, data science is used to find patterns, trends and anomalies in order to make predictions and form strategies. Companies and organisations all over the world use data science to turn complex data into valuable information.

The purpose of data science can be broken down into three categories:

To discover patterns

Data science is used to find patterns and trends within information. This is done using complex algorithms and models.

To analyse and predict

Once patterns and trends are discovered, data science models – including machine learning models – can be used to determine future results.

To make decisions

At its purest form, the idea of data science is to use patterns and predictions gathered from large quantities of complex data to make the best decisions  within businesses or organisations.

Why is data science important?

Data is now the key driver of business decisions. 

There’s almost nothing that can’t be learnt from data, while the ways in which we use it are constantly evolving. From gaining a better understanding of the customer base and improving workflows, to reducing costs and increasing revenues, data plays a crucial role in the success of a 21st century business. Increasingly, the difference between a successful company or organisation and an unsuccessful one is the way it collates and makes use of data.

What skills are needed for data science?

Data science is an interdisciplinary field, in which a diverse toolkit is necessary. From complex technical skills such as expertise in programming languages, to soft skills, including creativity and the ability to collaborate, there are many abilities that are key when it comes to working within data science.

Technical skills


Whatever path you choose to follow within the field of data science, having expertise in fundamental programming languages is crucial. A well-rounded coding toolkit will enable you to understand and gain value from large complex datasets.

And while technologies and expectations are constantly evolving, there are a few pivotal programming languages that you’ll need to learn to be able to make an impact in your data science career.


With an estimated 8.2 million active users, Python is the most popular programming language in the world. It’s also a hugely important data science tool – Python is used by approximately 70% of data scientists.

The reason for Python’s popularity is down to its simplicity. Data is complex by its own nature, which makes Python’s pick up and play factor so appealing to those learning the ropes in data science. Learning Python is made even easier by the fact that it’s open source and free to use. It also has a huge community – the largest community of any programming language – that takes pride in its diversity and inclusivity.

For those with basic coding experience, learning Python usually takes a couple of months. It typically  takes around six months for those with no experience but it can take years to master the more complex features within Python. 

As it’s easier to learn and more flexible and scalable than any other programming language, Python should be top of your list when it comes to learning a new programming language.


The essential programming language for interacting with relational databases, SQL, or Sequel, is used to pull, add, delete or edit information within a database. Created over 50 years ago, it’s still a widely used programming language in data science. In fact, it’s the second most in-demand data science skill, behind only Python.

SQL is a non-procedural language, which means you only have to specify ‘what to do’ not ‘how to do it. This means its semantics are very simple, making it very easy to get to grips with. Despite its simplicity, SQL is a very powerful language that enables you to perform many functions at high efficiency and speed. 

Seeing as pretty much every successful organisation you can think of uses SQL, it’s most certainly an essential language for anyone wanting to get into the field of data science.


Used by more than 2 million data scientists and statisticians around the world, R is a statistical tool used for exploring data sets, creating data models and developing data visualisations. For the past few years, it’s probably been the fastest growing computer programming language. And, with statistics and data visualisation playing an increasingly influential role in global strategy, it’s clear that R will be an important language in the future.

R is open-source and has a large, vibrant community of supportive developers, making it great for beginners. Pre-set R packages can be adapted when learning the ropes, making it a really fun and interesting language to learn. Once you do learn it, you’ll soon be able to build intelligent, intricate data analytics systems. 


While most data scientists prefer to use Python or R for most data science tasks, JavaScript is still a valuable language to know, particularly when it comes to creating data visualisations, building deep learning architectures, asynchronous tasks and handling real-time data. 


Important libraries and frameworks

Within each of these programming languages are key libraries and frameworks. These can be thought of as shortcuts or simplifiers that enable you to skip the basic building blocks and get to the creative part. For the modern coder, they save valuable time and effort, allowing you to create more effective code.

Here are some of the most important frameworks and libraries you need to know about:


A fundamental Python package used by most data scientists, NumPy is an array-processing package used for numerical and scientific computing. It can implement a wide variety of mathematical operations, from data analysis to data cleaning and provides array objects that are 50 times faster than Python lists.


Another important Python library, handles three types of data structures – DataFrame, panel and series – and is used for exploring, cleaning, transforming and visualising. It’s very user-friendly, so it should be a key library when learning Python.


A very useful framework when it comes to building deep learning architectures, Tensorflow was developed by Google and is used by some of the biggest companies in the world. It’s more of a platform of frameworks and libraries rather than a single tool. If you’re interested in getting into machine learning, Tensorflow should be at the top of your list.


Another excellent machine learning library, scikit-learn is a very popular open-source Python framework that features lots of efficient tools for machine learning, such as various classification, regression and clustering algorithms. Another essential framework for those interested in machine learning.


Used for plotting histograms, scatterplot, 3D plots, bar charts and more, Matplotlib is the most used framework for creating data visualisations. It’s a powerful tool that makes data easy to interpret, which is why it’s one of the most essential libraries and frameworks in the Python collection.


Alongside Matplotlib, Seaborn is the most important framework for data visualisation. While Matplotlib is used for simple visualisations, Seaborn is a key tool for creating more advanced graphical representations. 

Data visualisation 

Having access to large amounts of data is one thing; but if you can’t demonstrate what the data actually means, then it’s essentially useless. That’s where data visualisation comes in.

When it comes to taking complex data and explaining it in a simple, easily digested way, visualisation is the go-to method in data science. Using visualisation tools such as Matplotlib, seaborn, Datawrapper and Tableau, visualisation specialists can create charts, graphs, maps and interactive presentations to essentially tell a compelling story, often to people with little or no understanding of data science. That’s why a good data visualisation specialist is able to bring simplicity from complexity, in order to influence key strategic decisions. 

Statistical knowledge

One of the cornerstones of data science, statistics are used to collect, analyse and present data in a meaningful way. In tandem with mathematical theories, statistics help you to understand large data sets and search for patterns within the data which, in turn, enables you to make predictions.

If you’re starting out in data science, statistical experience isn’t crucial. However, you’ll definitely need the passion and determination to immerse yourself in this complex topic, if you’re to fully explore your own potential.

Machine learning 

One of the key emerging areas from within the data space, machine learning is the driving force behind AI systems, enabling machines to learn, adapt and evolve. Used by the most successful and sophisticated organisations in the world, machine learning is one of the major innovation tools of our age and is a key skill for any aspiring data scientist. 

In essence, machine learning enables us to gather more information from data and to use it more efficiently. Whether it’s used for recommendation engines, traffic predictions, healthcare analysis or robotics, machine learning is and will continue to be an important tool in the field of data science.

Soft skills


Having an in-depth knowledge of the technical intricacies of data science is one thing; knowing how to make sense of data and communicate findings in a clear and concise way is another. Being able to influence others and effectively communicate complex ideas is a significant part of data science. 

The key decision makers within an organisation, many of whom will have little knowledge of data science, base company strategy on data findings. That’s why it’s so crucial to be able to convey the results of data in a way that’s easy to understand. Structured thinking and speaking skills are key, as is the ability to listen to and learn from the ideas of others.


Once you understand the methods, theories and all the other tools at your disposal within the field of data science, it’s up to you to decide how to use them to get the best results. There’s so much scope for using creativity – the idea is to bridge the gap between the kind of data you have and the data that you want to have.

Henry Ford once said “If I had asked people what they wanted, they would have said faster horses.” Imagination is key. Being able to break free from pre-imposed constraints is a special ability. You’ll develop your own special brand of creativity from a mixture of learning and gaining experience on the job. 


Data science is not a solo pursuit. Collaboration is key – whether it’s fellow data scientists, designers, engineers or key decision makers within the company, the results of effective data arise from good teamwork. 

The ability to work with others in a team involves empathy and understanding the viewpoints of others. As with every role within a company, within data science, teamwork is a key trait that goes a long way towards creating a successful company.


Data science can be a volatile field, where the unexpected can catch you off guard. Unpredictable results can often derail a project, so it’s vital to be able to stay open-minded and flexible, ready to learn from problems and improve with experience.  

It’s also a field that’s constantly evolving, with emerging theories, methods and technologies driving progress. Being able to adapt to a changing market is a core soft skill within data science.  


We create machines that learn and adapt, yet one of the most crucial data science attributes is a distinctly human quality. Curiosity is probably the most crucial soft skill of all. 

As data science is constantly evolving, it’s important to have that inquisitive nature that keeps you on the front foot. Being aware of breakthrough methods and technologies, while constantly asking questions and having a drive to discover new ways of doing things makes for a hugely valuable asset for any company.  

Beyond that, curiosity underpins every other soft skill. Curiosity for new ideas and solutions drives creativity and imagination, while curiosity of people drives empathy, communication and teamwork.

Are data science roles in-demand?

Absolutely. The number of data science jobs has grown nearly 46% over the last two years, and predictions show that, in 2022, data science will still be in-demand

What careers are there within the field of data science?

There are many different roles that make up data science, each with their own nuanced responsibilities and specific skill sets. Here are just a few of the top data science roles available, as well as their average salaries throughout Europe.  

*average salaries taken from cwjobs.co.uk

Data scientist

One of the most technically complex and demanding roles within data science, a data scientist designs and produces predictive data models and generally oversees all aspects of a data science project. A data scientist will have a good knowledge of key programming languages like SQL, Python and R, as well as a deep understanding of machine learning methods, data visualisation and business strategy. 

Average salary


Data analyst

From transforming and manipulating data to creating data visualisations and web analytics, data analysts are tasked with finding and making sense of trends and insights from complex data sets. A large part of the role is to influence the decision making process, improving efficiency and increasing revenue, while gaining an insight into key market trends. Knowledge of SQL, Python and Tableau are essential, as are an understanding of machine learning and probability.

Average salary 


Machine learning engineer

A key part of the research and development team of any organisation, a machine learning engineer builds predictive algorithms in order to better understand patterns and trends. By building ML frameworks and deploying data models, as well as writing clean, maintainable code, a machine learning engineer develops AI systems with self-learning capabilities to provide invaluable insights for companies and organisations.

Average salary


Data engineer

A data engineer deals with data in its rawest form. Their responsibilities include building and maintaining data pipelines – essentially gathering and preparing data, making it readily available for data scientists and data analysts. One of the most challenging data science roles, data engineers work with unformatted data that often contains lots of problems. As they work at the base of the data ecosystem, they are a critical part of any organisation. 

Average salary


Data architect

A particularly in-demand job within the data science field, the role of data architect is usually a progression from data engineer. A very senior and influential role within data science, a data architect designs, builds and manages the entire data architecture of an organisation. Everything from data warehousing and data modelling to data development and data visualisation is key for a data architect.

Average salary


Business intelligence (BI) developer

The bridge between the data and the decisions, a business intelligence officer is tasked with simplifying complex data, to make it accessible to executives, managers and other key decision makers within a company. Knowledge of SQL, JavaScript and data visualisation techniques are important for the role, as are data mining and data reporting skills, as well as an understanding of business and the specific business strategy of the company.

Average salary


How do you get started in data science?

The best way to equip yourself with the fundamental skills necessary to get into data science is to enrol in a data science boot camp. A data science bootcamp helps you make the transition into a long term data science career. Not only will you be able to master the core abilities, but you’ll also be exposed to real world issues and you’ll be able to get career support to help you make your first steps within the industry.

At CodeOp’s Data Science Bootcamp, you learn through hands-on, real-life application cases. The course is taught by expert instructors from diverse backgrounds, both from academia and from within the data science industry.

You’ll gain an in-depth knowledge of the main programming languages, statistics, machine learning and advanced data science methods, as well as the soft skills necessary to make a well-rounded data science professional.

After you’ve acquired an industry-ready toolkit and various portfolio pieces to showcase to potential employers, you’ll be given bespoke career advice and 360 support, including interactive workshops, presentations, a network of recruiters and personal mentors.      

Are you ready to launch your data science career with CodeOp? Download our Data Science Course Guide and start building data science career.