One of the fastest growing job roles out there, data science is an excellent career for those with a logical, creative mind. What’s more, there’s huge demand for skilled scientists, as companies look for new ways to push boundaries with emerging technologies and use data in innovative ways.
Still, as data science is such an interdisciplinary field, actually becoming a data scientist involves learning a diverse range of skills, including complex technical abilities and core communication skills.
If you’re considering a career in data science but don’t know where to begin, don’t worry – we’re here to support you every step of the way. The first step? Understanding exactly what skills are needed to become a data scientist.
Technical skills
Knowledge of core programming languages
SQL
The foundational programming language for databases, SQL is used for data definition, data manipulation, data control, transaction control and data query. Essentially, it is the tool that is used to interact with all regional databases. Although it’s over 50 years old, SQL is still hugely relevant in data science across all sectors.
SQL is a ‘non-procedural language.’ This makes it very easy to learn and simple to use. Despite this, it’s a very powerful tool that can understand and analyze large amounts of data and can perform many functions at high efficiency and speed.
Python
The most widely used programming language in data science, Python is one of the first skills you need to learn to become a data scientist. Luckily, it’s probably the most beginner-friendly of all the languages. It’s very adaptable and can be used for many different purposes, although artificial intelligence and machine learning are where Python works at its best.
With a huge number of libraries and packages available and an extensive community of supportive developers, Python’s influence in the world of data science will only keep increasing.
R
One of the fastest growing programming languages in the world today, R is a very powerful tool that is used for everything from big data and statistical analysis to machine learning and deep learning.
Extensible and simple to learn, R can be used to create customised tools for specific purposes. It can also be used in conjunction with other data science programming languages, such as Python, Java and C++.
Knowledge of any of the following major programming languages is also a bonus:
C, C++, Julia, Java, JavaScript.
Data Visualisation
Being able to tell a compelling story about complex information using simple images is a key skill in data science. Visualisation turns complexity into something simple and easy to understand, enabling key strategic decisions to be made.
Data visualisation includes creating charts, graphs and maps. Key tools include software such as Datawrapper, Tableau, D3, and QlikView, as well as Python plotting libraries including Matplotlib and Seaborn.
Statistics
A foundational element in data science, statistics help you to collect, analyse and present data. Through mathematical theories, statistics help you understand large amounts of data, finding patterns that enable you to make predictions.
A background in statistics isn’t necessary for a career in data science, but a passion for learning and understanding the complexities of this fundamental tool certainly is.
Machine learning
Machine learning enables you to explore trends and behaviours, and create innovative products and features. It’s an area of data science that has become incredibly influential in recent years. In the future, it will be even more so.
The driving force of AI systems, machine learning enables machines to learn and adapt. The world’s biggest companies use machine learning as a key tenet of their operations and, as the technology advances, this important area of data science will be a major tool for innovation.
Soft skills
Communication
It can be easy to become lost in the complexities of mathematical systems and statistics. Yet, the end goal of data analysis is to make sense of the data. That’s why communicating complex information in a way that’s clear and concise is a soft skill that has a big impact in data science.
In order to provide insights for key decision makers within a company, many of whom might not have deep technical knowledge, data scientists must possess good communication skills. As well as structured thinking and excellent speaking skills, a good scientist will be able to listen to ideas of others and create a connection with team members.
Creativity
Within data science, creativity is bridging the gap between the data you have and the data you want to have. It’s understanding how to use data to produce a desired result, and using your imagination to reach the end goal.
Creating comes from both learning and from experience. So, learn as much as you can about theories, methods and tools before starting in data science. The rest you’ll pick up along the way.
Curiosity
In order to be a data scientist, you must be driven by an unwavering curiosity. This is a desire to learn and improve, to always ask questions and search for answers and to look beyond the surface. With a curious mind and an inquisitive spirit, a data scientist can be hugely valuable for companies looking to innovate and push boundaries.
So… are you ready to work towards a career in data science? We can help you get the skills you need… download our Data Science course guide today!
