Every day – and every moment – large amounts of diverse data are being generated and collected by companies around the world. This data has a lot of potential business value if companies can appropriately utilize it. Naturally, this growth in data is causing an evolution of certain skill sets in order to meet a rapid and ever-changing market demand, and it’s important that everyone learns at least the basics of data analytics.

In essence, the data is there; the problem is that you need employees who have the skills to leverage that data and use relevant tools to get it done efficiently. This skill gap applies to everyone within an organization. All roles are changing to have basic data literacy requirements, even if you’ve never previously worked with data. And for those who do regularly work with data, there’s a growing need to stay up to date on tools and techniques as the field evolves.

So, what are some of the most in-demand data analytics skills? Let’s review three of the most popular training topics I’ve seen while working with corporate clients in recent years.

In-demand Data Science and Analytics Skills

Data Literacy

Data literacy is the ability to read, analyze, work with and argue with data. Basic data literacy skills are needed in every role no matter the organization. Like other fields of study, there can be varying levels of data literacy. At a basic level, everyone should know common terminology, types of data, where data comes from, how data is used, and basic analysis and visualization techniques. This results in employees being able to:

  • Speak fluently about data analytics and its uses.
  • Identify business opportunities and make more data-driven decisions.
  • Ask questions and evaluate proposed solutions.
  • Increase understanding of how data is being used in their organization and how decisions are made.


Python is today’s fastest growing major programming language and is a favorite with data practitioners. In fact, according to Burtch Works, a leading executive recruiting agency specializing in data science and analytics, Python is now the programming language most preferred by data professionals. Most major companies worldwide are using Python, and an increasing number of companies of all sizes report using it as their primary programming language.

There are many reasons for this widespread adoption. Python is open source and has a large community of active users. Its flexibility makes it useful in multiple domains, and it has a large number of open source libraries that provide ready-to-go solutions for many common problems. Not to mention, Python has simple syntax and readability, so the learning curve is low. All of these things combined make Python an attractive language for anyone working with data within a company.

Machine Learning

Machine learning is a subset of artificial intelligence that involves computers learning from data. It’s been around for decades but has increased in popularity over the past 20 years due to larger volumes of data, improvements in hardware, and the development of tools for model development and deployment that decrease the barrier to entry. Machine learning has applications in all parts of business including marketing, advertising, supply chain, information technology, human resources, finance and more.

Companies are increasing investment in machine learning as they work to improve decision-making, automate processes and leverage untapped sources of data. For many, machine learning serves as a foundation to improve understanding of customer behavior, inform capacity and planning decisions, and forecast performance. Studies show that companies making extensive use of analytics are far more likely to outperform their competition in sales growth and profitability.

How Companies Use Training to Solve Common Problems and Achieve Strategic Goals

Data literacy training will help generate business value by enabling teams to:

  • Make more informed decisions based on data.
  • Uncover actionable insights from data and identify opportunities within the business where data can be leveraged.
  • Increase collaboration in a data-driven organization.
  • Increase adoption and usage of data-driven solutions.

Machine learning training enables staff to better leverage data. This can lead to increases in innovation and reductions in cost.As mentioned previously, there are many reasons for widespread Python adoption. Python training leads to benefits that can include increased productivity, efficiency and innovation, as well as decreased time to market.

As an example, CMA Strategy Consulting, a boutique consulting firm focused on the telecommunications, media and high-tech industries, chose to train 75% of its company in Python, from analysts and managers to principals.

This was because CMA faced a growing problem: Their clients’ datasets were getting too large and diverse for their existing toolset. By training in Python, they would be able to add the language into their existing workflow, allowing them to efficiently work with these larger datasets.

After the training, CMA was quick to adopt the techniques learned, with results indicating that analyses using the team’s new Python skills ran 22.5 times faster than before.

What to Look for in a Data and Analytics Training Provider

When it’s time to seek out a training provider for your team, there are a couple questions and goals to keep in mind. First, it’s important to focus on outcomes. Ask yourself, “What do I need my team to be able to do after this training, and how do those outcomes align with our overall business needs?” You’ll need to ensure your training partner can address these outcomes directly.

Additionally, training is most powerful when companies use real-world data and use cases for training purposes, adding relevance and enhancing the connection between the learning and how these new skills will apply directly to their work.