Data analysts take raw information and figure out what it means for an organization. They spot trends, answer questions—such as how much of a company’s revenue comes from repeat versus first-time buyers—and give teams the evidence they need to make decisions. Demand for this skill set is climbing. According to the U.S. Bureau of Labor Statistics, analytics-related jobs are projected to grow 21% by 2034—much faster than average. It is one of the more accessible paths into tech for people building their skills through focused training. An online data analytics program can teach you the data skills you need without committing years to a degree.
Here are five data analytics skills worth developing, whether you are preparing for your first role or looking to advance in a data analytics career.
1. Master Data Cleaning and Preparation
Raw data is rarely in usable shape when an analyst first gets it. Records pulled from different systems often have duplicates, missing values, inconsistent formatting, or other errors. Data cleaning is the process of correcting those problems so the numbers reflect reality before any analysis starts.
Bad data leads to bad analysis, and a company that trusts that analysis will make bad decisions. According to a 2025 report by the IBM Institute for Business Value, 43% of chief operations officers identified data quality issues as their most significant data priority. This is becoming even more crucial as companies move their data into AI tools.
If the data going in is messy or unreliable, the results coming out cannot be trusted. Analysts who know how to prepare accurate, consistent data are what make the rest of the analysis reliable.
2. Build Strong Data Visualization Skills
Data visualization is the representation of data in visual formats such as charts, graphs, and dashboards. It organizes information in a way that makes data easier to understand, helping people identify meaningful patterns, trends, and relationships within a dataset.
Analysts build these visuals in tools such as Tableau, Google Looker Studio, and Microsoft Power BI. Power BI appears in roughly a quarter of data analyst job postings, making it among the most in-demand data analytics skills.
A well-built dashboard can show whether revenue is on track to hit quarterly targets, which product lines are selling best, or where spending is rising over time.
Building strong data visualization skills is one of the main ways an analyst makes their analysis useful to broader teams.
3. Learn SQL for Data Querying and Management
SQL (Structured Query Language) is the standard language for working with relational databases, and it is one of the most fundamental data analytics skills. A relational database organizes information into tables of rows and columns that connect to one another. This lets companies store large amounts of data in multiple places without repeating details and still allows them to pull exactly the records they need.
When an analyst builds a dashboard, SQL is often what pulls the data from a database—such as Google BigQuery—into that dashboard. Knowing SQL means knowing where a company’s data lives and how to get it accurately. It supports every other skill involved in the role: you clean data queried with SQL, build dashboards from SQL results, and run statistics on data that SQL pulled.
Learning SQL means an analyst can go straight to where a company’s data lives and get exactly what they need, which is why it appears in more job postings than any other data analytics skill.
4. Develop Statistical Analysis and Critical Thinking
Statistics is the foundation of data analytics. It gives analysts a reliable way to interpret what the numbers are showing.
Averages and distributions tell an analyst what is typical in a set of data and how much the values vary.
Correlation shows whether two things move together, such as ad spending and sales.
Significance testing shows whether a result is meaningful or could have happened by chance.
Without these, an analyst could report that revenue dropped but couldn’t say whether the drop is a genuine trend or ordinary week-to-week variation.
Knowing whether a change in revenue is statistically significant or not is what makes an analysis worth acting on.
All data needs context, and critical thinking is what turns numbers into recommendations. Metrics can tell you what happened. Working out why something happened, and what to do about it, takes reasoning that no report can produce. Data analytics training teaches people how to reason through questions that data raises and turn their answers into recommendations a business can use. Employers want analysts who can look at the data and explain what it means for the business.
5. Gain Proficiency in Python and Data Analytics Tools
Python is one of the most valuable programming languages for data analytics because it can do so much and has a deep library of tools built for it. Analysts use Python to automate repetitive tasks, run analyses spreadsheets cannot handle, and work with data sets too big to manage by hand. Knowing Python makes you more competitive for higher-paying roles, according to a 2026 analysis of more than 1,000 data analytics jobs.
A few Python libraries are standard in the field, such as:
- Pandas
- For data manipulation, including loading, cleaning, and reshaping data sets.
- NumPy
- For numerical and mathematical operations.
- Matplotlib and Seaborn
- To build charts and graphs straight from your data.
Learning these tools lets you turn raw data into finished analysis. The projects you build can become portfolio pieces that show employers what you can do.
Level Up Your Data Analytics Career with NPower
NPower’s tuition-free data analytics course gives you hands-on training in the skills covered you need to build or advance a career in data. The online data analytics program is designed for people who want practical, job-ready skills without taking on debt or committing to years of education. It covers the tools companies use daily and includes project work you can show to employers.
The program is open to young adults and military-connected individuals, and no prior coding background is required. Learn more and apply to NPower’s 100% tuition-free Data Analytics program.











