Data analytics is still a hot topic that attracts a lot of interest as the younger sibling of data science. Businesses need someone who can rapidly and effectively deduce conclusions from the large amounts of data that are available to them. These are the platforms, tools, and talents in data analytics that companies are searching for in 2023 based on our analysis of over 25,000 job descriptions.
Data Analytics Skills
Core Data Analytics Skills
The majority of people indeed believe data analytics to be a mixture of analytics and Excel. Excel is the second most popular tool in our ranking, as you can see in the chart below since it continues to be the de facto standard for data management and analytics. Below that, we can see several supporting analytics abilities, including statistics, dashboards, math, and problem-solving.
Statistics, arithmetic, business analysis, and quantitative analysis are some of the basic mathematical abilities that are essential to analytics and are still in high demand. The conventional idea of what a data analyst is, as you’ll see below, has changed as a result of the proliferation of data analytics platforms, expertise, and frameworks.
Data Science & Machine Learning
The frameworks and technologies mentioned in each figure show how data scientists and data analysts are becoming more and more similar. A data analyst should be familiar with fundamental data science, machine learning algorithms, automation, and data mining as additional tools to support analytics, even though they aren’t required to have more in-depth knowledge like deep learning or NLP.
Domain Expertise; Business, Economics
Since context-sensitive data is most effective when it is known, employers seek candidates who are also knowledgeable in the industry. In particular, economics and business were chosen since they frequently deal with the most data in comparison to other industries. Although they are not mentioned in the graph, the financial services and healthcare sectors have experienced a lot of change as fields where data analysts can benefit from some industry-specific knowledge.
Data engineering and programming
We were surprised that the list included both programming and data engineering abilities as most data analysts aren’t programmers. It’s becoming more and more important for data analysts (DAs) to be able to work with cloud platforms, data storage technologies, and the entire current data stack, as you’ll see in the section below on data analytics tools and platforms.
The ability to find and retrieve data on their own for analysis is expected of the modern data analyst. It is crucial to be proficient in databases, ETL (Extract, Transform, and Load), and data quality. To prepare their data for analysis, data analysts frequently have to go out and collect, prepare, and clean their data.
Because many businesses currently have vast data lakes and significant amounts of data that need to be analyzed, this also pushes into big data. While it’s necessary to analyze smaller datasets on your laptop, moving up to TB+ datasets necessitates a completely different set of abilities and data analytics frameworks.
Any competent data analyst can do more than just crunch numbers. The diagram outlines several presentation-based abilities that will set any data analyst apart. It will cover more ground if you can communicate your results with shareholders in addition to just analyzing the numbers. Effective communication techniques, such as speaking and writing, will support the data, and data visualization (particular frameworks are provided in the following section) will enable you to tell the whole story.
Data Analytics Platforms and Tools
Any aspiring data analyst should be familiar with the data analytics platforms shown in the chart above. As you can see, there are a lot of reporting systems.
Even though the distinctions between data analytics and data science are becoming less distinct, Excel is still necessary. Excel remains among the top tools despite the sheer volume of new ones and the growing popularity of big data. Spreadsheet mastery is still in demand by businesses since a sizable portion of basic data analytics is still performed in Excel.
The Modern Data Stack
Data analytics roles are not an exception to the significant influence of the current data stack. Data analysts with experience using big data systems like Redshift, and Apache Spark as well as columnar platforms like Snowflake are in demand from more businesses. It should come as no surprise, given the significance of SQL, that Relational Database Management Systems (RDMS) like Oracle and Microsoft SQL Server are also included.
In 2022, cloud-based services are standard, which promotes the growth of a select few service providers. But in this instance, when contrasting Microsoft Azure, AWS, or Google Cloud Platform, AWS appears to have supplanted Azure as the leader since the previous year.
PowerPoint and Microsoft Office
A PowerPoint presentation deck is a necessity for any self-respecting data analyst. The ability to create and deliver a strong visual presentation is still a core competency, despite my lighthearted remarks. It also doesn’t hurt to be familiar with the complete Microsoft Office toolkit.