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The 9 Most In-Demand Skills in the Data Industry

Discover the most in-demand data skills and why employers are seeking skilled individuals. Plus. find out how you can improve your data knowledge in key areas.
Jun 2022  · 12 min read

Data Skills

In-Demand Data Skills

The future is looking bright for the data industry. Globally, the big data market is expected to reach US$103 billion by 2027. And with a growing number of companies building their business on data, there's ample need for people who know how to manage, analyze, and present it. As such, strong data skills are essential for anyone wanting to enter the industry.

Data professionals have been in high demand since the rise of big data. With this trend showing no sign of slowing down, we’ve highlighted some of the most in-demand data skills in the industry.

1. Python Skills

Python is a popular open-source programming language and a must-have skill for data professionals. In 2017, Forbes reported that Python experienced a growth rate of 456% over the previous year, a leap partially explained by the language’s applicability to the data industry. 

For data analytics, Python is hard to beat. The language’s intuitive syntax allows us to create and manage data systems with ease and speed. 

Plus, with so many people using the language for data work, many ready-made packages and frameworks are available, meaning there’s little need to start from scratch. The popular pandas library, for instance, contains numerous tools for manipulating, analyzing, and representing data structures and complex data sets.

We generate a lot of data, and the need to organize and manipulate it has never been more important. Data analytics with Python just makes sense, one reason why it's a central part of many companies’ tech stacks. 

Python is also a great choice when it comes to data visualizations, both simple and interactive. Again, the wide range of libraries and packages means options aplenty.

Develop Your Python Skills

Start your Python journey with our online courses, Introduction to Python or Introduction to Data Science with Python.

2. SQL and NoSQL Skills

While SQL has been around since the 60s, its importance cannot be understated, nor can its relevance today. According to research by Emsi, in May 2021 alone, there were 217,968 unique job postings listing SQL. 

SQL stands for Structured Query Language. This data-industry essential has an important job: it allows us to communicate and ask questions (queries) of relational databases. You can think of SQL as both a language and a database type. 

With so much data in the world, we need ways to effectively store it and extract information when needed. Databases are a go-to solution in companies of all sizes and in all industries. 

We have both relational or SQL databases, which are structured according to the relationship between data points, and non-relational or NoSQL databases, which are less structured and capable of handling large amounts of complex data. 

To get ahead in the data industry, you need to manage both types of databases and use SQL and its variants to query these and gather information. 

Develop Your SQL and NoSQL Skills

Kickstart your relational database query skills with an Introduction to SQL or learn how to create your own database in Introduction to Relational Databases in SQL. Next, master NoSQL Concepts

3. R Skills

A statistical powerhouse, R is the preferred language of academic research worldwide. It’s also in high use in the data industry, thanks to its flexibility and versatility in data tasks. 

First built by statisticians for statistical computing, R can be used to explore, model, and visualize data. It can also handle, store, and analyze data and perform statistical modeling.

R is the language of choice for many machine learning engineers, data scientists, and analysts. As such, there is a plethora of packages and libraries that support data professionals in their work and extend R’s capabilities. In 2017, for instance, Revolution Analytics noted over 10,000 packages on the Comprehensive R Analysis Network (CRAN)—a public repository of R tools.

At the time of writing (June 2022), R holds pole position in data on Pluralsight’s Technology Index, which aggregates billions of data points monthly to reveal growth rates and the popularity of various tech tools and skills. 

Not only in demand, but R skills are among the highest-paid in IT and data.

Develop Your R Skills

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.

4. Data Visualization Skills

Data holds secrets, and it’s the job of data professionals to extract and transform them into a format that everyone can understand. 

Data visualizations are graphic representations of data; they help us condense complex information into something bite-sized and easy to read. After all, a well-presented dashboard or graphically rich report packs a lot of punch compared to rows and columns of figures.

Visualizations can also help us spot differences between variables and uncover patterns that aren’t easy to see in a table format, making visualizations a useful tool for analysis.

Because businesses of all sizes and across sectors need people who know how to effectively communicate data insights, demand for people with data visualization skills is increasing steadily

Data pros use several tools to communicate their findings in a visual format including Python, R, Tableau, Power BI, and spreadsheets. 

Develop Your Data Visualization Skills

Take a code-free introduction in Data Visualization for Everyone 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.

5. Machine Learning and Natural Language Processing (NLP) Skills

The global machine learning market is predicted to reach an impressive US$209 billion by 2029, and demand for people with these types of data skills is increasing accordingly.

Statistics from 2020 show that 82% of organizations needed people with machine learning abilities, while only 12% said the supply of machine learning professionals was sufficient.

In today’s data-forward world, it’s hard to find a sector that doesn’t rely on machine learning in one way or another. Whether it’s forecasting ROI, estimating future inventory needs, or predicting consumer behavior, machine learning is at work behind the scenes.

Natural language processing (NLP), a subfield of artificial intelligence (AI), is also on the rise in the data industry. Text contains a wealth of useful information, and NLP-based mining technologies help data professionals find what they’re looking for quickly. 

It also means our computers can parse text and organize data meaningfully, helping data pros effectively analyze huge amounts of textual data.

Develop Your NLP and Machine Learning Skills

Wrap your head around the fundamentals with our Understanding Machine Learning course or see how this technology is used for better business in Machine Learning for Business

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.

6. Amazon Web Services (AWS) and Cloud-Driven Computing Skills

With public and private organizations collecting and storing more data than ever, they needed a clever way to manage the masses of information, one that didn’t require huge on-site servers (or the associated maintenance costs). 

Cloud-based services are the go-to solution for many entities. Digital products such as Amazon Web Services (AWS) allow clients to collect an unparalleled amount of information in data lakes, and they also provide in-built tools that aid analysis. 

Data professionals who want to match their data skills to demand should invest in their cloud computing knowledge. According to Statista, AWS is the world’s most popular cloud service, holding a 33% market share in Q1 2022. 

Besides AWS, knowing the basics of Microsoft Azure and Google Cloud Platform, which hold market shares of 21% and 10% respectively, is also useful.  

Of course, it’s not just about knowing the systems companies use. It’s also about being able to scale your own data architecture and systems, something the cloud allows you to do. You can’t fit a very large dataset on a local environment, for example, or you might need more hardware power than your device can provide.

Develop Your AWS and Cloud Computing Skills

Delve into the basics with our no-code courses: Understanding Cloud Computing and AWS Cloud Concepts. Next, learn how to optimize your workflows in Introduction to AWS Boto in Python.

7. Extract, Transform, Load (ETL) Skills

Data engineering skills are highly sought-after. DICE's 2020 Tech Job Report showed that data engineering was the fastest-growing job, even surpassing data science. Extract, transform, and load (ETL) is a data engineering skill that helps us to efficiently process big data. 

With ETL skills in your technical arsenal, you can build the data pipelines you need to gather data, transform it into the format you require, and then load it into data storage systems. 

ETL means the ability to collect data from a range of sources, both structured and unstructured, and make it useable and readable—something all companies need to compete in a data-driven landscape.

In a 2022 Cord survey of 1,600 data professionals, ETL was designated as a ‘primary’ skill.

Develop Your ETL Skills

Learn how to create your own data pipeline with our ETL in Python course, where you’ll cover how to efficiently extract, transform, and load data into the systems your company commonly uses.

8. Statistics Skills

While you don’t need a Master’s or Ph.D. in statistics or mathematics to work in data, a solid understanding of several key concepts is important. 

Statistics as a field is about gathering, organizing, analyzing, and interpreting information, so you can see that there’s a significant crossover between mathematical statistics and data science practices. 

Understanding certain statistics principles and statistical techniques means data professionals can build ‘robustified’ mathematical and statistical models, and properly understand the data they’re analyzing. 

If you want to bolster your maths-whizz abilities, aim to have a working knowledge of significance, probability, hypothesis testing, and regression. 

Bayesian thinking—a school of thought based on the principle of calculating and updating probabilities based on new evidence or data—is also useful for those working with AI technologies.

Develop Your Statistics 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. 

9. AI Skills

Knowing how to use and fine-tune AI-powered tools can turn data professionals into analytics powerhouses capable of collating and analyzing huge data sets much faster than those working without AI assistance. 

ZDNet reports that around 10,000 AI and data science jobs open each month, with many companies looking for people who understand off-the-shelf solutions and have a deeper knowledge of deep learning and neural network systems.

Artificial intelligence is a huge field encompassing all tools and technologies that simulate human intelligence, this includes data-industry staples such as machine learning and NLP. 

Develop Your AI Skills

How do we approximate human intelligence? Wrap your head around the basic concepts in AI Fundamentals.

Different Data Roles Require Different Skills

The data skills we’ve covered above are useful for all data professionals, but some roles will require in-depth knowledge of certain skills. 

You can’t be a machine learning engineer without a solid understanding of machine learning, for example. Similarly, you won’t land a job as a database administrator without a good working knowledge of SQL and NoSQL. On the other hand, you probably won’t need advanced ETL skills for an entry-level data analyst job. 

So which skills are most important in each data role? Take a look at the infographic below:

data role skills infographic

Conclusion

The data industry is booming and data professionals are in demand worldwide. Landing your dream data job is a matter of developing the core technical skills employers seek - check out the latest data jobs on DataCamp Jobs to learn more.

Whether you’d like to be on the analysis end of the data pipeline, up in the cloud developing data architecture and systems, or building advanced NLP tools, DataCamp can help you get there.

More than just engaging courses, we offer a library’s worth of resources across technologies and tools to support and cement your learning. 

Take the first lesson in any of our courses for free and find out why nine million learners worldwide trust DataCamp.

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