Did you know that 90% of the world's data was created in the last 2 years?
As businesses jump to tap into this data gold rush, data analyst skills are more valuable than ever.
Interested in getting in on this in-demand field? Understanding what data analyst skills are needed is crucial for success.
This guide covers the top 10 data analytics basics that employers look for when hiring data analysts, from basic statistics for data analysis to data visualization.
Let's explore how you can become a data analyst in 2025.
Table of Contents
- Data Collection
- Data Quality Management
- Descriptive Analytics and Basic Statistics for Data Science
- Data Visualization
- Exploratory Data Analysis (EDA)
- Statistical Analysis
- SQL and Database Management
- Programming Skills
- Business Intelligence and Data Strategy
- Ethics and Data Privacy
Top 10 Essential Data Analyst Skills for Career Success
Let's explore the best skills for data analyst positions that will help you stand out in the job market. For those learning how to become a data analyst, these skills your essential building blocks.
1. Data Collection
Data collection is the foundation of all data analyst skills. As a data analyst, you need to master the art of gathering information effectively and systematically from various sources.
Imagine you’re a detective. You need to know where to look, what to collect, and how to ensure the evidence, or data, you gather is relevant and valuable.
In the business world, data collection might involve working with multiple systems and sources. For instance, a retail analyst might need to collect:
- Transaction data from point-of-sale systems
- Customer feedback from surveys
- Website traffic analytics
- Social media metrics
- Competitor pricing information
Understanding how to integrate these various data sources is another crucial part of this data analysis skill.
For example, combining transaction data with customer feedback can reveal not just what customers are buying, but why they're making those choices.
This multi-source approach provides a more comprehensive view that leads to better business decisions.
Enroll in our Data Literacy course to grasp the basics of what stories data can help you tell for your business.
2. Data Quality Management
Data quality management is one of the most important data analyst skills needed—it can make or break your analysis.
Think of it like building a house—if your foundation (data) isn't solid, everything built on top of it will be unstable. This skill involves more than just cleaning data; you need to establish and maintain data integrity throughout the entire analysis process.
Consider this real-world example from healthcare:
Imagine a hospital database where patient records contain inconsistencies. Poor data quality could lead to serious consequences such as incorrect medical decisions, treatment delays, or medication errors.
In other words, maintaining high data quality standards is crucial across all industries, making it one of the most important data analysis basics.
Key aspects of data quality management include:
- Accuracy Verification: Ensuring all data points are correct and precise
- Completeness Assessment: Checking for missing values and understanding their impact
- Consistency Maintenance: Making sure data follows the same format and standards across all sources
- Timeliness: Verifying that data is up-to-date and relevant
Want to master data quality management? Our Data Preprocessing with NumPy and Data Cleaning with Pandas courses teach you the skills discussed in this section.
3. Descriptive Analytics and Basic Statistics for Data Science
Descriptive analytics forms the backbone of data analysis, transforming raw numbers into meaningful insights.
This fundamental skill involves understanding basic statistics concepts and applying them to real-world scenarios. You need to go beyond just calculating numbers—learn how to tell the story behind the data.
Key statistical measures you need to master include:
- Mean (average): The central tendency
- Median: The middle value in a dataset
- Mode: The most common occurrences
- Standard deviation: Data spread and variability
Let's see how these data analysis basics can help analyze a restaurant's performance.
- The mean helps calculate average daily sales across seasons, giving a clear picture of typical performance levels.
- Using mode analysis reveals peak business hours by identifying the most frequently busy times.
- The median shows typical menu item prices while filtering out extremely expensive or cheap outliers.
- And standard deviation helps understand how much sales vary during special events.
While these concepts are simple, they are powerful—landing them an important spot in our top skills for data analysts.
If you want a deeper look at statistics for data analysts, check out our comprehensive Statistics course. And our Statistical Tests for Sales and Marketing course is a great way to see how you can use these methods in context.
4. Data Visualization
Data visualization is the art of transforming complex data into clear, compelling visual stories.
When we asked the question “What do you need to be a data analyst?” in our job market research last year, data visualization came up again and again.
This skill goes beyond simply creating charts—it's about choosing the right visualization method to communicate your findings effectively and make data-driven decisions easier for stakeholders.
Consider these practical applications:
- Sales teams use bar charts to compare product performance across regions
- Marketing departments utilize heat maps to analyze website user behavior
- Operations managers rely on line graphs to track productivity trends
- Financial analysts employ scatter plots to identify correlations between economic factors
Effective data visualization requires understanding both the technical aspects of creating visualizations and the psychological principles of how people interpret visual information.
Knowing when to use different types of charts and how to design them for maximum impact while maintaining clarity and accuracy is a critical part of how to become a data analyst.
Tableau is one of the most widely used tools by data analysts and business intelligence professionals. Enroll in our beginner’s Tableau course for a comprehensive introduction.
5. Exploratory Data Analysis (EDA)
EDA is like being a data detective, investigating your dataset from multiple angles to uncover hidden patterns and insights.
This approach combines visual and statistical methods to understand the story your data is telling. This data analyst skill involves asking the right questions and following the clues to meaningful discoveries.
You might ask:
- What is the distribution of values in each variable, and are there any unexpected patterns?
- How do different variables relate to each other, and what correlations might be significant?
- Are there any outliers or anomalies that need further investigation?
A thorough EDA process involves: understanding data structure, identifying trends, anomaly detection, and hypothesis generation.
For example, when analyzing customer behavior data, EDA might reveal:
- Unexpected shopping patterns during certain times of the year
- Customer segments with unique preferences
- Products frequently purchased together
- Geographical variations in buying behavior
This data analysis skill is a crucial first step that helps analysts understand the basic structure, patterns, and potential issues in their data before conducting more sophisticated statistical analyses.
Most of your EDA will begin with Excel. That’s why our Data Analysis with Excel and Pivot Tables course is a great place to get you started asking the right questions.
6. Statistical Analysis
Advanced statistical analysis allows you to move beyond simple descriptions to make predictions and test hypotheses about your data.
This data analyst skill is crucial for making reliable conclusions and supporting decision-making with confidence.
For instance, when analyzing the effectiveness of a new marketing campaign, you might use:
- Hypothesis testing to determine if sales increases are statistically significant
- Confidence intervals to estimate the true impact of the campaign
- Regression analysis to understand which factors most influence success
- Probability distributions to model likely outcomes
Understanding these basic statistical concepts helps you avoid common pitfalls and make more reliable predictions. It's the difference between saying "sales seem higher" and being able to state with confidence that "we can be 95% certain the campaign increased sales by 15-20%.
Key areas of statistical analysis include:
- A/B testing for comparing different approaches
- Time series analysis for forecasting trends
- Multivariate analysis for complex relationships
- Bayesian statistics for updating predictions with new data
We have two great hands-on courses to get you started! Enroll in our A/B Testing with Python course and our Time Series Analysis course to learn how to make the right data-driven decisions.
7. SQL and Database Management
For those learning how to become a data analyst, SQL and database management are fundamental data analyst skills.
Just as data collection is about gathering information, SQL is your tool for accessing and manipulating that data effectively. This involves understanding database structures and writing efficient queries to extract exactly the information you need.
Essential SQL and database management skills include:
- Writing complex queries using JOIN operations
- Aggregating and grouping data
- Creating and modifying database structures
- Optimizing queries for performance
These data analysis skills are valuable in various business contexts. For example, you might use SQL for analyzing customer purchase histories, tracking inventory movements, monitoring sales performance, or generating automated reports.
This demonstrates how SQL is a crucial tool in transforming raw data into actionable insights.
Enroll in our comprehensive SQL course which offers 11 hours of video lessons teaching you hands-on database management skills.
8. Programming Skills
While programming skills are not traditionally part of the role’s toolkit, they are becoming increasingly essential for those looking to become data analysts.
Key programming applications in data analysis include:
- Automating repetitive data cleaning tasks
- Creating custom analysis functions
- Building interactive visualizations
- Developing predictive models
Consider this real-world example:
A data analyst at a healthcare company uses Python to analyze patient readmission patterns by processing millions of electronic health records. Then, they develop machine learning models to predict which patients might need preventive care based on their medical history, lifestyle factors, and current symptoms.
See how programming can use data to not only simplify tasks, but make future predictions possible?
For those exploring how to become a data analyst, learning programming languages like Python and R opens up powerful possibilities for data analysis and automation.
Explore our Intro to Python course and R Programming course to get in on these in-demand skills.
9. Business Intelligence and Data Strategy
Business Intelligence (BI) skills involve understanding how data insights drive business strategy and decision-making.
For those learning how to become a data analyst, developing a strong business acumen and understanding of organizational goals is critical for delivering valuable insights.
Key aspects of business intelligence expertise include:
- Understanding key performance indicators (KPIs) and business metrics
- Identifying business opportunities through data analysis
- Aligning data analysis with strategic objectives
- Translating technical findings into actionable business recommendations
For instance, a business analyst might:
- Identify market trends and growth opportunities
- Evaluate the financial impact of business decisions
- Develop data-driven business strategies
- Create executive-level reports that drive strategic planning
Rather than just knowing how to analyze data, understanding the reasons behind analysis and how it drives organizational success is one of the best skills for data analysts.
Does this sound interesting to you? Check out our Intro to Business Analytics course and Mastering KPIs course to develop these skills.
10. Ethics and Data Privacy
Data ethics refers to the responsible collection, handling, and use of data while protecting individual privacy.
This is a crucial data analyst skill because mishandling sensitive data can lead to privacy breaches, loss of customer trust, legal consequences, and significant damage to both individuals and organizations.
As data analysts, we have a responsibility to be ethical stewards of the information we handle.
Here are some key considerations you should consider before working with any sort of data:
- Data protection regulations
- User consent
- Data security
- The ethical use of information
If you're asking, "What do you need to be a data analyst?" understand that ethics and privacy are among the most crucial skills in today's age of artificial intelligence and machine learning.
As AI systems process vast amounts of personal data, maintaining ethical standards and protecting privacy becomes even more important.
To maintain the highest ethical standards in your work, explore our Data Strategy course. It covers crucial topics including data quality, ethics, privacy, ownership, access, and security.
Become a Data Analyst with 365 Data Science
Mastering these essential data analyst skills requires dedication and continuous learning. Each skill builds upon the others, creating a comprehensive toolkit that will make you valuable in any data-driven organization.
How can you develop these skills?
365 Data Science's Data Analyst Career Track helps you master all these skills through 10 comprehensive courses, from Excel fundamentals to advanced SQL and data visualization.
Ready to start your journey? Enroll in the Data Analyst Career Track today and earn a verifiable certificate upon passing the final exam. This industry-recognized credential demonstrates to employers that you possess the essential data analyst skills needed for success in the field.
FAQs
Here are answers to some of the most frequently asked questions about how to become a data analyst and data analysis skills:
