10 best data scientist skills for CVs: an in-depth guide

By Indeed Editorial Team

Published 4 May 2022

Today's modern organisations have significant amounts of digital data they store for their business operations, and data scientists are quickly becoming one of the most important roles in a company. When used correctly, data can help an organisation make meaningful business decisions that contribute to its expansion and growth. Learning about the skills you can put on your CV can help you grow as you look to enter the industry. In this article, we list 10 of the best data scientist skills for CVs and provide an in-depth analysis of why each of them is important.

What is a data scientist?

Data scientists are technical professionals who help organisations collect, visualise, maintain and interpret data to apply to business operations. Big data and analytics have become a key component of modern companies, making the role highly sought after by employers. The term 'data scientist' refers to a broad spectrum of jobs, but generally, the role consists of interpreting and analysing data to determine market trends, advise on risk management and make business recommendations based on analytics.

Related: How to become a data scientist in 4 steps

Data scientists' primary duties

A data scientist has many responsibilities due to the broad nature of the role that becomes more specific once they find a niche. Here are some general responsibilities of the role:

  • organising data into easily interpretable formats

  • collecting raw data and storing it in databases

  • processing and verifying data relevant to organisations

  • assessing the quality of data

  • building and maintaining databases with SQL

  • identifying market trends with collected data

  • generating insights by analysing trends in data

10 data scientist skills for CVs

There is an abundance of data scientist skills for CVs that can help employers see your strengths. Here are 10 useful skills you can include when applying for a job as a data scientist:

Technical ability

Data scientists require baseline technical knowledge no matter their speciality and field. All data scientists have an educational background in programming or coding language, with the most common being SQL, AWS, C++, Python and other digital competencies, such as Tables and Queries, TensorFlow or CouchDB. There are many different facets to programming that are useful to know depending on a data scientist's specific duties.

The top programming languages for data analysts, engineers and scientists are Python, R and SQL. Python is the most popular programming language for those who deal with data, as it applies to a wide variety of topics, such as machine learning and big data. R is a language that's rising in popularity due to its ease of use and ability to handle large data sets. SQL is the most useful language for those who want to build and maintain orderly and secure databases.

Communication

A crucial component of being a data scientist is being able to explain and apply complex theories and concepts to those who aren't familiar with the subject. This may take place in written or verbal form, both of which require communicative abilities. Once they've identified trends or insights in a data set, they present their findings to senior management members. Some data scientists may also have business knowledge, and they may advise the organisation on how best to utilise the new insights and trends they've gathered.

Analytics

Analytical prowess is important considering a data scientist's job is to analyse large amounts of data and information on a daily basis. What's most important is that data scientists can look at a large amount of data, make sense of it and produce insightful knowledge. To be able to apply analytical thinking to the general field of data, professionals need an in-depth and expert knowledge of the subject they are observing. These topics may include:

  • analytical methodology evaluation

  • artificial intelligence

  • big data

  • data interpretation

  • data mining

  • data visualisation

  • hypothesis-testing

  • modelling tools

  • probability

  • risk modelling

  • statistics

Related: 19 jobs that use statistics (plus duties and salaries)

Mobile and web applications

Data scientists also frequently collaborate with both web and mobile developers, as both respective fields deal with large amounts of data and utilise statistics. Many data professionals also develop applications, frameworks and databases on web services such as AWS. While knowledge of basic web and mobile languages, such as HTML or JavaScript, isn't necessary, understanding how web and mobile applications function can be helpful when developing and programming.

SQL knowledge

SQL is the most widely used programming language when it comes to dealing with data, so all professionals in the field have an in-depth knowledge of it. With SQL, you can create and maintain sound databases to store important information and data for organisations and collect important metrics of interest. Many consider SQL the standard language for all data-related professions, and it's non-procedural, meaning that you don't need any previous programming knowledge to learn it. The language gives you access to data and statistics you can use to handle structured data and create database frameworks.

Related: 10 jobs with SQL (plus duties and salary information)

Advanced mathematics

Data analysts frequently use calculus and linear algebra to perform and solve complex algorithms, formulas and concepts. Organisations are always looking for ways to improve their digital systems, and data scientists use their in-depth knowledge of calculus to build machine learning models and automated systems. They may also use plotting, gradients, algebra and other statistics-related concepts on a daily basis to solve challenges. Many programming languages also require knowledge of multiple mathematical theories.

Related: 7 popular mathematics degree jobs (with examples and salaries)

Cloud computing

All modern companies utilise cloud servers to store their documents, data and important information. Cloud computing is important for data scientists as it expands computing power and makes the deployment of data solutions easier, which benefits data scientists who deal with large data sets. Here are the three major cloud services that data scientists use:

  • AWS: AWS is the most popular cloud service on the internet and is the backbone of most websites. It uses widely known tools such as Redshift, S3, Data Pipeline, Database Migration Service and EC2.

  • Microsoft Azure: Microsoft Azure uses Azure SQL, DocumentDB and Azure Table to store large amounts of data and AzureML for easy implementation of machine learning algorithms. Many data scientists enjoy using Microsoft Azure as they can link these tools to more accessible programs like Excel to visualise and present data.

Logical thinking

An important skill for data scientists to possess is the ability to observe large amounts of data that may mean nothing to regular individuals and interpret it to create meaningful and actionable insights. Logical thinking is an intangible soft skill but highlighting it on your CV by explaining how you solved challenges and problems in practical scenarios shows employers your strengths. The fundamental characteristics of logical thinking are the ability to observe information, draw conclusions based on the input and justify your thoughts and strategies.

Excel knowledge

Microsoft Excel is accessible enough for employees not well-versed in data analytics but complicated enough to visualise and record data. In Excel, data scientists can store and record numerical data in databases with custom labels used to filter and sort data. Many companies may require any findings in Excel format so that they can easily store them on the organisation's cloud drive, and the relevant employees can read and edit the file. Despite its uses in the data analytics field, displaying Excel knowledge on your CV is always a positive in today's digital environment.

Attention to detail

It's easy to make mistakes with the abundance of information data scientists observe and analyse. A small mistake in a data set may drastically change any outcomes, insights or judgements you make, so it's crucial that professionals have a keen eye and attention to detail while working. When writing your CV, demonstrate and explain times that you employed attention to detail. Although it's a soft skill, it's important for any role that requires technical knowledge.

Please note that none of the companies, institutions or organisations mentioned in this article are affiliated with Indeed.