The COVID-19 pandemic accelerated the digitalization of our societies resulting in the continued growth of data volumes. Data makes us more informed and can help improve the decision-making processes of businesses, governments, and citizens. But to turn data into relevant information, we need professionals skilled in managing, analyzing, and extracting insights. Here is where data science skills come in.
The Need for Data Scientist Skills
The global big data market is forecasted to grow to $103 billion dollars by 2027, more than double its expected market size in 2018. In other words: Big data is big business. Despite the increasing demand, companies around the globe are suffering a shortage of qualified data professionals.
One of the reasons behind this shortage is the difficulties companies face in finding data scientists with the right skill set. That is no surprise, as data scientists are professionals with diverse skills not commonly found in a single individual. That’s why data scientists are often referred to as “unicorns.”
What are the most important data scientist skills? This is an important question that aspiring data scientists and professionals looking to boost their career prospects wonder.
Data scientists are multifaceted and versatile professionals. Given the nature of their responsibilities, they require a balanced set of technical skills and leadership skills. This article will cover the most in-demand skills in the data science industry. We will also provide some resources that can help you develop the skills needed for data scientists.
Data Scientist Technical Skills
Below, we’ve outlined some of the key technical skills data scientists need to thrive in the industry.
One of the reasons for its worldwide adoption is its suitability for data analysis tasks. Although not conceived originally for data science, over the years, Python has evolved to become the king in the industry.
Python is a central pillar in many companies' tech stacks. With powerful, ready-made libraries, such as pandas, NumPy, and matplotlib, you can perform all kinds of data with ease, from data manipulation and cleaning to statistical analysis and data visualization.
It’s also worth mentioning the dominance of Python in advanced data science subdomains, including machine learning and deep learning. Here, popular packages and frameworks like scikit-learn, Keras, and TensorFlow provide the necessary magic to build and train algorithms.
Thanks to its intuitive syntax that mimics the English language, Python is a great language to learn for novice programmers.
Develop Your Python Skills
If Python is the king in data science, R is the queen. Developed in 1992, R is an open-source programming language specifically conceived for statistical and computing analysis.
Widely used in scientific research and academia, as well as sectors such as finance and business, R allows you to perform many kinds of data analyses. This is mainly due to the rich collection of packages for data science available in the Comprehensive R Archive Network (CRAN).
The demand for R programmers is rapidly growing. However, compared to Python users, the number of data scientists with R skills is more limited. As a result, R programmers are among the highest-paid professionals in IT and data science.
Develop Your R Skills
If you are new in data science, sooner than later you will have to learn how to code. Our recommendation is to start by picking R or Python. Discover the basics in our Introduction to R course, then take it up a notch in Intermediate R. Next, learn how a dedicated set of R tools can help you wrangle and visualize data in Introduction to the Tidyverse.
Statistics and Math Skills
You don't need any mathematical background to start learning data science, but you won’t go further in your career if you don’t get familiar with some mathematical and statistical concepts.
Having a grasp of statistics is critical when choosing and applying the different data techniques available, building robust data models, and properly understanding the data you are dealing with.
In addition to the very basics of math taught in a common school program, you should invest some time in learning the basics of calculus, probability, statistics, and linear algebra. Bayesian theory is also an asset if you work in AI and machine learning techniques.
Develop Your Statistics and Math Skills
Kick things off with a code-free Introduction to Statistics course before turning your hand to more advanced concepts. DataCamp offers more than 70 courses focused on statistics and probability, so you can pick your preferred technology and brush up on your statistical techniques.
Despite being around since the 60s, SQL (Structured Query Language) is still a must-have skill for data scientists. SQL is the standard tool in the industry to manage and communicate with relational databases.
Relational databases allow us to store structured data in tables that are related through some columns in common. A great amount of data in the world, especially companies’ own data, is stored in relational databases. Therefore, SQL is a must-have skill for every data scientist. Fortunately, compared to Python and R, SQL is a straightforward language and fairly easy to learn.
Develop Your SQL Skills
While SQL is the perfect tool to deal with structured data stored in tables with rows and columns, things can get a bit messier when it comes to unstructured data. The majority of the data generated today (e.g., audio, video, satellite images, web server logs) is unstructured, making it difficult to store and process following the traditional relational model.
To deal with the different types of unstructured data, other types of databases are available. The so-called NoSQL databases (stands for Not only SQL) are capable of handling large amounts of complex, unstructured data. Examples of NoSQL databases are MongoDB, Neo4j, and Cassandra.
Develop Your NoSQL Skills
NoSQL databases are at the forefront of innovation in data science. Get started in this highly demanded technology with our course on NoSQL Concepts.
Data Visualization Skills
A critical part of the job of a data scientist is communicating the findings of data analysis. Only if decision-makers and stakeholders understand the conclusions of data analysis can data turn into actions. One of the most effective techniques to achieve this goal is through data visualization.
Data visualization involves the use of graphical representations of data, such as graphs, charts, and maps. These representations allow data scientists to summarize thousands of rows and columns of complex data, and put it in an understandable and accessible format.
The subfield of data visualization is rapidly evolving, with important contributions from disciplines, like psychology and neuroscience, that are helping data scientists to identify the best way to communicate information through visuals.
There are many tools available to create compelling visualizations, including Python’s libraries like matplotlib, R’s libraries like ggplot2, and popular Business Intelligence software, like Tableau and Power BI.
Develop Your Data Visualization Skills
Take a code-free introduction in Understanding Data Visualization or peruse DataCamp’s full range of data visualization courses. From plotly to Power BI, you’ll find courses covering your preferred tools and technologies.
Machine Learning Skills
Machine learning is one of the hottest topics in data science. Machine learning is a branch of artificial intelligence focused on developing algorithms that learn to perform tasks without explicitly being programmed.
From Netflix recommendations to Instagram filters, machine learning is embedded in your everyday life. The rising use of machine learning systems is leading to increasing demand for data scientists with machine learning skills. Statistics from 2020 show that 82% of companies needed people with machine learning skills, while only 12% said the supply of machine learning professionals was sufficient.
Develop Your Machine Learning Skills
Deep Learning Skills
A step further for machine learning practitioners is deep learning. Deep learning is a subfield of machine learning that focuses on powerful algorithms, called artificial neural networks, inspired by the human brain's structure and function.
Most of the progress in artificial intelligence in the last few years has come from deep learning. Neural networks are behind some of the most disruptive and awe-inspiring applications, including autonomous cars, virtual assistants, image recognition, and robots.
Knowing the theory and practice of neural networks is rapidly becoming a game-changer when hiring or promoting data scientists. However, it is fair to say that deep learning is a complicated discipline requiring an advanced level of math and programming. That’s why data professionals skilled in deep learning are among the best-paid in the data science industry.
Develop Your Deep Learning Skills
Kickstart your learning journey by learning how to build neural networks in some of the most popular frameworks for deep learning. Try our Introduction to Deep Learning with Keras and Introduction to TensorFlow in R courses.
Natural Language Processing Skills
Humans communicate with each other mostly through language and text. That’s why it is unsurprising that a great part of the data we collect comes in this format. Natural language processing (NLP) is a subfield of artificial intelligence that focuses on extracting meaningful information from natural language and text.
NLP is on the rise in the data industry. NLP techniques based on machine learning and deep learning power some of the most ubiquitous applications, such as search engines, chatbots, and recommendation systems.
Develop Your NLP and Machine Learning Skills
Discover how Python can help you pull insights from text in Introduction to Natural Language Processing in Python or take your R skills to the next level with Introduction to Natural Language Processing in R.
Big Data Skills
When it comes to processing vast amounts of complex data at high speed, relying solely on Python or R may not suffice. The Big Data ecosystem encompasses rapidly growing tools and technologies designed to perform big data analysis in a faster, scalable, and reliable way. These tasks range from ETL processes and database management to real-time data analysis and task scheduling.
Develop Your Big Data Skills
Learn the foundations of distributed data management and computing with our Big Data with PySpark Skill Track, or learn how to schedule data workflows with our Introduction to Airflow in Python course.
Cloud Computing Skills
In parallel with the evolution of the Big Data ecosystem, cloud-based services are rapidly becoming a go-to option for many companies that want to make the most out of their data infrastructure.
The cloud computing landscape is dominated by Big tech, namely Amazon Web Services, Microsoft, Azure, and Google Cloud. These providers offer tailor-made solutions depending on the client's circumstances and many data tools that allow us to conduct the data science workflow without leaving the cloud.
Develop Your AWS and Cloud Computing Skills
Data Scientist Soft Skills
Although technical abilities are a significant part of data scientist skills, there are also less tangible skills that you’ll need to thrive in the industry.
Data is nothing but information. As humans, our body is constantly collecting information through our senses. But to make sense of that information, we need to understand its meaning and implications. The same applies when analyzing huge amounts of data. To discover meaningful information from data, we first need to understand the data we are dealing with.
Beside the technical skills we mentioned before, data scientists should also have a solid business understanding of the sector or industry they work in, whether it’s finance, healthcare, marketing, or otherwise. This domain-specific knowledge is crucial to make sense of data and conduct better analysis.
Data science is not only about math and programming; it is also about presenting and communicating the insights of data analysis. If people don’t understand the results of an analysis, your work as a scientist won’t be valuable for a company.
To turn data into decision-making, data scientists must have the ability to communicate their insights properly. What’s more, data scientists should know how to tell compelling stories about data. To do so, innovative approaches and frameworks for communication, such as data storytelling, can make a big difference.
Data Ethics Skills
Technology itself is neutral. But the use of it is not. In recent years, certain data-driven companies have been in the spotlight for developing practices and applications that have the potential to adversely impact people and society. This has undermined the credibility and trust citizens deposit on companies and, more broadly, on technology.
To ensure that data results in positive impacts, data scientists should build ethical awareness. This involves getting familiar with important concepts, such as data privacy, algorithm bias, and feedback loops, and working towards developing fair, transparent, and accountable algorithms.
The world is in the midst of an unprecedented climate crisis. Climate change and the rapid loss of biodiversity threaten the conditions that make human life possible. Although often omitted, the digital industry, including data science, is also contributing to the climate crisis.
Storing and processing huge amounts of data and training machine learning algorithms require considerable energy, resulting in additional CO2 emissions to the atmosphere. For example, in 2019 it was estimated that training a large deep leaning model can emit more than 626,000 pounds of carbon dioxide equivalent, which is nearly five times the lifetime emissions of the average American car, including those associated with manufacturing. Further, data centers, where most of the data is stored and processed, also consume a lot of water to cool servers.
To address the climate crisis, data scientists should be aware of the environmental impact of their work and, more broadly, the data science industry. This could eventually help optimize and reduce energy use and develop more sustainable practices.
Data Scientist Skills - Final Thoughts
This article covered the 15 most in-demand data scientist skills. Learning all of them can be challenging, even overwhelming, especially if you’re at the beginning of your data science journey. Yet, there is no need to stress out. Very few data scientists have such a complete toolkit.
You should start learning some of the basic skills, including Python, R, and/or SQL, and some fundamentals of statistics, and move progressively to other subjects.
But what data scientist skills should you learn next? There is no exact answer for this. Most likely, your learning journey will depend on the requirements of your job. For example, if you end up in a cloud-based provider, you will probably have to learn cloud computing skills. On the other hand, if your company focuses on machine learning, you already know what you need to get a promotion.
Finally, if you just want to improve your skill set, our advice is simple: learn the skills you are most interested in! Check out our guide on how to become a data scientist for further tips on pursuing this exciting career path.
Develop Your Data Scientist Skills
Python Data Science Toolbox (Part 1)
Data Types for Data Science in Python
Software Engineering for Data Scientists in Python
How to Become a Data Scientist in 8 StepsFind out everything you need to know about becoming a data scientist, and find out whether it’s the right career for you!
YOLO Object Detection ExplainedUnderstand YOLO object detection, its benefits, how it has evolved over the last couple of years and some real-life applications.
5 Ways to Use Data Science in MarketingDiscover five ways you can use data science in marketing. Get ahead of the game, improve your data skills, and work on a data science marketing project.
How Data Science is Changing SoccerWith the Fifa 2022 World Cup upon us, learn about the most widely used data science use-cases in soccer.
The Deep Learning Revolution in Space Science
Justin Fletcher joins the show to talk about how the US Space Force is using deep learning with telescope data to monitor satellites, potentially lethal space debris, and identify and prevent catastrophic collisions.
Regular Expressions Cheat SheetRegular expressions (regex or regexp) are a pattern of characters that describe an amount of text. Regular expressions are one of the most widely used tools in natural language processing and allow you to supercharge common text data manipulation tasks. Use this cheat sheet as a handy reminder when working with regular expressions.
DataCamp Team •