The demand for those who can find the meaning in a sea of data is skyrocketing. According to the Future of Jobs report, big data specialist is expected to be the number one fastest growing job over the next few years.

So how can you develop the skills you need to succeed in this booming field? By training in data analytics.

Data analytics is the process of collecting, cleaning, and analyzing data to uncover patterns and insights that drive decision making. Programs focus on helping you gain the technical skills and business acumen to turn complex datasets into actionable insights.

Here are the essential skills you can expect to learn in data analytics training:

• Data Analysis and Interpretation
• Statistical and Mathematical Foundations
• Data Visualization
• Programming
• Machine Learning
• Business Intelligence
• Critical Thinking and Problem Solving
• Communication and Storytelling

DATA ANALYSIS AND INTERPRETATION

Fundamentally, data analytics is about understanding and interpreting data in its many forms. You’ll learn the difference between structured data, such as spreadsheets and databases with clearly defined fields, and unstructured data, such as emails, videos, and social media posts. Knowing how to work with both types is essential, as real-world data rarely comes in a neat, ready-to-use format.

You’ll also develop techniques for cleaning and organizing data. That includes handling missing values, correcting inconsistencies, and removing duplicates. These steps are critical because even the most advanced analysis is unreliable if the underlying data is flawed. Remember: garbage in = garbage out!

“We give them real-world scenarios such as online shopping or healthcare,” says Herzing instructor Zakiya Malek. She holds a doctorate in cybersecurity and has taught in the IT field for over 20 years.

“They begin by collecting data from these domains and then focus on preparing it for analysis by cleaning missing values, resolving inconsistencies, standardizing scales, and applying normalization techniques. Students also learn to validate data quality and prepare datasets for advanced analytics and machine learning workflows.”

Most importantly, a data analytics program teaches you how to draw meaningful insights. This means going beyond what happened to explain why it happened and what should be done next.

STATISTICAL AND MATHEMATICAL FOUNDATIONS

In a data analytics program, you’ll gain a strong foundation in essential statistical concepts like probability, regression analysis, and hypothesis testing. These tools allow you to measure uncertainty, identify relationships between variables, and test assumptions using data.

For example, regression analysis helps businesses understand how different factors like price or consumer demographics affect outcomes like sales or customer retention. Hypothesis testing lets you determine whether the changes you observe are meaningful or simply due to chance.

Leaders rely on statistical evidence to evaluate risks, forecast outcomes, and choose between competing strategies. As a data analyst, your role is to provide that evidence in a clear and reliable way.

DATA VISUALIZATION

Data is most powerful when it can be understood quickly and clearly. That’s where data visualization comes in. A data analytics program teaches you how to transform complex datasets into visual formats like charts, graphs, dashboards, and reports.

You’ll learn which visualization techniques work best for different types of data and audiences. For example, line charts are effective for showing trends over time, while bar charts are useful for comparing categories. Dashboards bring multiple visuals together, allowing stakeholders to monitor key metrics at a glance.

Visualization is critical because it bridges the gap between analysis and communication. Well-designed visuals help decision makers grasp insights without needing to dig into raw data or technical details. In many cases, a compelling chart can be more persuasive and informative than pages of numbers.

Charts and graphs are an effective way to present data

PROGRAMMING

Modern data analytics relies heavily on programming. Most programs introduce you to widely used languages such as Python, R, and SQL. Each serves a different purpose, and together they build a powerful toolkit.

Python and R are commonly used for data manipulation, statistical analysis, and machine learning. SQL, on the other hand, is essential for querying and managing data stored in relational databases. You’ll learn how to write queries that extract the data you need from large datasets.

“SQL is used to extract and organize data at the source, while Python is used for deeper analysis, automation, and advanced tasks such as statistical modelling and machine learning,” says Zakiya. “By developing skills in both, you gain the flexibility to work efficiently across different data environments and analytics use cases.”

Programming skills also allow you to automate repetitive data tasks, saving time and reducing errors. Additionally, coding enables you to build predictive models and perform advanced analyses that would be impossible using manual methods alone.

MACHINE LEARNING

Many data analytics programs include an introduction to machine learning, a subset of artificial intelligence that focuses on teaching computers to learn from data. You’ll explore the basics of supervised learning, where models are trained using labelled data, and unsupervised learning, where patterns are discovered without predefined labels.

Learning the real-world applications of AI in data analytics helps you see how organizations use predictive insights to stay competitive.

BUSINESS INTELLIGENCE

To turn analysis into business value, data analysts rely on business intelligence (BI) tools. A data analytics program typically provides hands-on experience with popular platforms such as Power BI, Tableau, and Excel.

These tools help analysts create interactive reports and dashboards that support reporting and strategic planning. Executives and managers use BI outputs to track performance, identify opportunities, and make informed decisions in real time.

By learning these tools, you’ll understand how data flows from raw sources to polished reports. This practical experience is especially valuable because many employers expect analysts to be proficient in at least one major BI platform.

“Students use Power BI dashboards to generate and present their analysis,” says Zakiya. “They learn how to build interactive reports, apply filters and controls, and design visualization that help management interpret insights and make informed, data-driven decisions.”

An example of a Power BI dashboard. Image source: Reporting Hub

CRITICAL THINKING AND PROBLEM SOLVING

Beyond technical skills, data analytics programs emphasize critical thinking and problem solving. You’ll learn how to approach data-driven challenges logically, starting with defining the problem, identifying relevant data, and selecting appropriate methods of analysis.

This skill involves questioning assumptions, evaluating data quality, and considering alternative explanations for your findings. Rather than accepting results at face value, you’ll be trained to think analytically and skeptically.

Ultimately, strong critical thinking allows you to make informed decisions based on evidence. It ensures that your recommendations are not only data-backed, but also relevant and realistic within a given business context.

COMMUNICATION AND STORYTELLING

Even the most insightful analysis has limited impact if it isn’t communicated effectively. A key skill you’ll develop in a data analytics program is data storytelling—the ability to translate complex findings into clear insights.

You’ll practise presenting results in a way that resonates with non-technical stakeholders, such as executives, clients, or team members without a data background. This includes choosing the right visuals, explaining key takeaways, and framing insight within a broader narrative.

Strong communication skills help bridge the gap between data and decision making. By telling a compelling story with data, you enable others to understand the “why” behind the numbers and take confident action.

EXPAND YOUR DATA ANALYTICS SKILLS WITH HERZING

Herzing’s Data Analytics With AI program can prepare you for a dynamic career in one of today’s most in-demand fields. Training is delivered online, takes no more than 19 months to complete, and includes an internship for real-world experience.

Click below to get full program details and chat live with a friendly admissions advisor. We’re here to help!