The data industry’s future appears to be bright. The global big data market is anticipated to be worth US$103 billion by 2027. And, with an increasing number of businesses based on data, there is a high demand for employees who understand how to handle, analyze, and present information. As a result, anyone interested in entering the industry must have great data abilities.

Data Management specialists, whether entry level positions in Database Management, Senior Operations Managers, Applications Architects or NOSQL Database Administrators all need a core skill set that translates across the profession. But, in the modern world of IT and Data Management (DM) in particular, there is a move away from specialization towards heterogeneity in terms of the job requirement packages that recruiters and HR departments are looking for. Look at any DM jobs on tech employment sites like Dice, JustTechJobs, DevBistro or iCrunchData and the skill sets required to get the best jobs read like the menu of a smorgasbord restaurant, rather than a particular cuisine.

With this trend showing no signs of abating, we’ve highlighted some of the industry’s most in-demand data capabilities.

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.

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. 

Read Also: How do You Conduct Predictive Analytics?

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. 

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.

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. 

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.

The data sector is flourishing, and data specialists are in high demand all across the world. Getting your dream data career is a matter of mastering the essential technical abilities that employers seek – to learn more, browse the latest data positions on DataCamp positions.