Top Data Analytics Skills and Platforms for 2023 Top Data Analytics Skills and Platforms for 2023
As the sibling of data science, data analytics is still a hot field that garners significant interest. Companies have plenty of... Top Data Analytics Skills and Platforms for 2023

As the sibling of data science, data analytics is still a hot field that garners significant interest. Companies have plenty of data at their disposal and are looking for people who can make sense of it and make deductions quickly and efficiently. We looked at over 25,000 job descriptions, and these are the data analytics platforms, tools, and skills that employers are looking for in 2023.

Data Analytics Skills

Core Data Analytics Skills: Data Analysis, Analytics, Dashboards, Statistics, Math, Problem-Solving

Most people think that data analytics is a mix of Excel and analytics, and that’s indeed on the money. Excel is the second most sought-after tool in our chart as you’ll see below as it’s still an industry standard for data management and analytics. Below that we see various analytics supporting skills, such as statistics, dashboards, math, and problem-solving.

There’s still a strong demand for the fundamental mathematical skills that contribute to analytics, including statistics, math, business analysis, and quantitative analysis. As you’ll see below, however, a growing number of data analytics platforms, skills, and frameworks have altered the traditional view of what a data analyst is.

Data Presentation: Communication Skills, Data Visualization

Any good data analyst can go beyond just number crunching. The chart shows a number of presentation-based skills that will make any data analyst stand out. Being able to not just analyze numbers but also share findings with shareholders will cover more ground. Skills like effective verbal and written communication will help back up the numbers, while data visualization (specific frameworks in the next section) can help you tell a complete story.

Data Wrangling: Data Quality, ETL, Databases, Big Data

The modern data analyst is expected to be able to source and retrieve their own data for analysis. Competence in data quality, databases, and ETL (Extract, Transform, Load) are essential. Data analysts often must go out and find their data, process it, clean it, and get it ready for analysis. 

This pushes into Big Data as well, as many companies now have significant amounts of data and large data lakes that need analyzing. While there’s a need for analyzing smaller datasets on your laptop, expanding into TB+ datasets requires a whole new set of skills and data analytics frameworks.


Data Science & Machine Learning

There’s an increasing amount of overlap between data scientists and data analysts, as shown by the frameworks and tools noted in each chart. While a data analyst isn’t expected to know more nuanced skills like deep learning or NLP, a data analyst should know basic data science, machine learning algorithms, automation, and data mining as additional techniques to help further analytics.

Programming and Data Engineering

Both programming and data engineering skills made the list to our surprise, as data analysts aren’t generally developers. However, as you’ll see in the next section on data analytics tools and platforms, there’s an increasing need for DAs to be able to work with cloud platforms, data storage tools, and the complete modern data stack.

Domain Expertise; Business, Economics

Employers are looking for people who also know the field they’re working in, as data works best when the context is known. In particular, economics and business made the cut as they often involve some of the largest amounts of data compared to other industries. While not noted in the chart, we’ve seen a lot of movement in finance and healthcare as industries where data analysts benefit from some domain expertise.

Data Analytics Platforms and Tools

The chart above shows a number of data analytics platforms that any aspiring data analyst should know. As you see, there are a number of reporting platforms as expected.

Data Analytics Platforms: Tableau, Power BI, Looker, Alteryx, Google Analytics, SPSS, SAP, Pandas.

The most common trend shouldn’t come as a surprise, as the most in-demand data analytics platforms revolve around reporting, such as Tableau, Power BI, Looker, Alteryx, Google Analytics, SPSS, and SAP. These are powerful tools that can connect directly to data sources, can run complex analyses, and can output intricate dashboard reports. As a machine learning library, pandas is powerful for data analysis and manipulation, earning it a spot as the only ML library on the list.


Excel is still needed, even as the lines between data science and data analytics begin to blur. Despite the sheer amount of new tools and the growing prevalence of big data, Excel still sits near the top. A significant amount of basic data analytics is still done in Excel, so being a spreadsheet guru is still attractive to employers.

The Modern Data Stack: Apache Spark, Google Bigquery, Oracle Database, Microsoft SQL Server, Snowflake

The modern data stack continues to have a big impact, and data analytics roles are no exception. More companies are looking for data analysts that know how to work with columnar platforms like Snowflake and big data systems such as Redshift, Bigquery, and Apache Spark. Given the importance of SQL, then there should be no surprise that Relational Database Management Systems (RDMS) such as Oracle and  Microsoft SQL Server are also listed.

Cloud Services: Google Cloud Platform, AWS, Azure.

Cloud-based services are the norm in 2022, this leads to a few service providers becoming increasingly popular. However, in this case, when comparing Microsoft Azure, AWS, or Google Cloud Platform, AWS seems to have taken over Azure as the winner since last year.

PowerPoint and Microsoft Office

No self-respecting data analyst would go anywhere without a presentation deck in PowerPoint. Joking aside, it really does support the fact that making and presenting a good visual presentation remains a core skill. Knowing the entire suite of Microsoft Office tools doesn’t hurt, either.

Data Analytics Languages

The debate over the best programming language for data analysis boils down to SQL and Python with R not far behind and Java & VBA almost completely out of sight. SQL has always been the go-to for many to interact with data and to develop dashboards & insights, but Python’s flexibility and broader use allow someone to analyze as well as clean/organize data as well. SQL excels with big data and statistics, making it important in order to query databases.

Get started with data analytics and add it to your skillset at ODSC West 2022

If you’re looking to add an in-demand, evergreen, and broad-use skill to your repertoire, then maybe it’s time to learn data analytics or other core data science skills. At ODSC East 2023, we’ll have an entire mini bootcamp track where you can start with core beginner skills and work your way up to more advanced data science skills, such as working with NLP or neural networks. By registering now, you’ll also gain access to Ai+ Training on demand for a year. Sign up now, start learning today!

Between now and May 9th-11th, getting an ODSC East 2023 bootcamp ticket will grant you access to a number of live training sessions over the next few months that will cover the above five pillars of data science, all leading up to a week of training in May that will let you use your newfound skills for more advanced topics in machine learning, deep learning, NLP, and more. Here are a few sessions that you can check out soon:

  • March 2, 2023: ODSC East Bootcamp Warmup: Data Primer Course – Now available on-demand
  • March 14,  2023: ODSC East Bootcamp Warmup: SQL Primer Course – Now available on-demand
  • April 6, 2023: ODSC East Bootcamp Warmup: Programming Primer Course with Python
  • April 26, 2023: ODSC East Bootcamp Warmup: AI Primer Course

And during ODSC East this May 9th-11th, you can check out these bootcamp-exclusive sessions:

  • An Introduction to Data Wrangling with SQL
  • Programming with Data: Python and Pandas
  • Introduction to Machine Learning
  • Introduction to Math for Data Science
  • Introduction to Data Visualization
  • Introduction to Deep Learning with TensorFlow & Keras
  • Introduction to Deep Learning with PyTorch
  • Introduction to ML using scikit-learn
  • Introduction to NLP
  • Hugging Face Transformers
  • Idiomatic Pandas
  • Introduction to Large-scale Analytics with PySpark
  • A Practical Tutorial on Building Machine Learning Demos with Gradio

What are you waiting for? Get your ODSC East 2023 Bootcamp ticket while tickets are 40% off!



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