Introduction

Data analysts are essential to organizations and brands striving to leverage data for decision-making. Whether you are just starting your career or considering a switch to the data analytics field, understanding the expectations of an entry-level data analyst is important. 

This article offers a comprehensive guide, on the roles, responsibilities, and evolving landscape of entry-level data analysts in 2025.

The Role of Entry-Level Data Analysts

An entry-level data analyst’s primary responsibility is to collect, clean, analyze, and interpret data to help organizations make informed decisions. 

• Data Collection: Entry-level analysts source data from a variety of channels, including databases, APIs, and external data providers. Familiarity with tools like SQL, Excel, and Python is very important to retrieving and organizing data efficiently.
• Data Cleaning: The quality of your data is very important. Analysts must rectify errors, manage missing data, and standardize the format. This ensures that insights obtained from the data are accurate and reliable.
• Data Analysis: Using statistical techniques, analysts uncover trends and patterns in the data. This includes calculating averages, identifying correlations, and running basic programs to intermediate regressions.
• Data Modeling: While complex machine learning models may not fall under their scope of work, entry-level analysts are often tasked with creating simpler models to predict outcomes or identify relationships within the data.
• Data Visualization: Data analysts need to know how to pass across messages in a way that is easy to understand. Analysts use tools like Tableau, Power BI, or Python libraries (e.g., Matplotlib, Seaborn) to create charts, graphs, and dashboards that make data understandable for others without the technical details of how data works.
• Report Generation: Writing detailed reports or preparing presentations is an essential skill. Analysts must convey insights and actionable recommendations concisely and straightforwardly.
• Collaboration: Data analysts rarely work on their own, Oftentimes collaborating with marketing, finance, operations, and other teams to ensure that data insights align with broader business objectives.

How the Data-Analytics Role Has Evolved

The role of an entry-level data analyst has undergone significant changes over the years, and this year marks an era where technology and industry demands are shaping this position in very profound ways. Let’s look at some of those ways;

• Big Data Influence: Entry-level analysts are increasingly required to handle larger, more complex datasets, due to the introduction of data in nearly every sector. Proficiency with tools like Hadoop and Spark is fast becoming a ‘good-to-have’ skill set for entry-level roles.
• Cloud Computing:  Cloud platforms like AWS, Azure, and Google Cloud have democratized access to data and tools. Entry-level analysts now frequently work in cloud-based environments to store, access, and analyze data.
• Real-time Analytics: Businesses demand faster insights. Instead of traditional batch processing, entry-level analysts are expected to deliver real-time analytics using tools like Apache Kafka and modern BI platforms.
• Data Privacy and Ethics: With strict data privacy laws such as GDPR and CCPA, data analysts must ensure compliance when handling sensitive information. They are also tasked with upholding ethical standards and avoiding biased or unfair use of data.
• Cross-functional Teams: Entry-level analysts most often work with diverse teams. For example, marketing may need customer segmentation analysis, while operations might require supply chain optimizations. This collaboration broadens their exposure and deepens their impact within the organizations and brands they work for.

Impact of AI and Automation on Data Analytics

The rapid growth of AI and automation within the data analytics field is reshaping how data analytics works, opening doors to new opportunities and challenges for entry-level analysts. New opportunities being unlocked as a result of AI include;

• Routine Task Automation: Tasks like data cleaning, basic statistical analysis, and even generating standard reports are now automated with AI tools. This reduces the manual workload and allows analysts to focus on higher-value tasks.
• Enhanced Analysis: AI-powered tools can now perform predictive analysis and advanced statistical modeling. However entry-level analysts are expected to know how these tools work and oversee them, ensuring accuracy and extracting insights from the outputs.
• Shift in Skills: While knowing how to manipulate data remains important, the focus is now shifting toward understanding and interpreting AI-driven results. Analysts must become adept at explaining these results generated by machine learning models to stakeholders.
• Continuous Learning: To stay competitive, entry-level analysts must commit to consistently learning, and studying on newest technologies within their field. Includes learning about AI concepts, such as neural networks or natural language processing (NLP), and how these can be applied in their work is also very important.
• Tool Proficiency: Familiarity with AI and machine learning tools like TensorFlow, Scikit-learn, and even no-code platforms such as DataRobot is becoming an integral part of every entry-level analyst’s skill set. 

Skills Required for an Entry-Level Data Analyst

A data analyst’s role is built upon a combination of his technical expertise, problem-solving abilities, and the ability to communicate data insights effectively. Let’s look at some of the key skills that every aspiring entry-level data analyst must have;

1. Programming

Programming is the process of writing instructions that a computer can execute to perform specific tasks. For data analysts, programming is crucial for data manipulation, automation, and analysis. However not every programming language is necessary for an entry-level data analyst, let’s dive deeper to look at some of the top programming languages every entry-level data analyst should know:

• Python:  Python is a versatile programming language widely used in data analysis. Its simplicity and powerful libraries make it a favorite among analysts. Key skills to learn in Python are:

• Pandas: Primarily used for data manipulation and cleaning.
• NumPy: Useful for calculating numerical computations.

Both tools are useful, especially in automating repetitive tasks, such as data extraction and formatting.

• SQL (Structured Query Language): SQL is the standard language for interacting with databases. Analysts use SQL to retrieve, update, and organize data stored in relational databases. Some key skills to learn while using SQL are: 

• Writing basic queries (e.g., SELECT statements).
• Using JOIN to combine multiple tables.

2. Data Tools Proficiency 

Data tools are software or platforms that help analysts process, analyze, and present data in a way that is easy to understand and draw insights from. Here are some of those tools and their key functions:

• Excel: Microsoft Excel is a spreadsheet program developed by Microsoft, widely used for organizing, formatting, and calculating data in a structured format. Excel remains a foundational tool for analysts due to its versatility and ease of use. Key features of Excel are;
  • Advanced functions (e.g., VLOOKUP, INDEX-MATCH).
  • Pivot tables for summarizing large datasets.
  • Data validation to maintain accuracy.
  • Basic macros to automate routine tasks.

Leveraging these features makes it easier to handle tasks ranging from simple calculations to complex financial modeling.

Google Sheets: Google Sheets is a collaborative spreadsheet tool that is particularly useful where team members collaborate on projects. Entry-level data analysts should learn important Google Sheets features like:

• Built-in functions for data analysis (e.g., QUERY, ARRAY FORMULA).
• Add-ons like Google Analytics for marketing data.
• Real-time collaboration with colleagues.

3. Visualizations

Visualization tools are very important tools for data analysts to learn as they help them present data in an easy-to-understand way. Here are some of those visualization tools:

• Tableau: Tableau is a popular data visualization tool that helps data analysts create interactive dashboards that make it easy to present insights from data. Key skills to learn while using Tableau:
  • Connecting Tableau to various data sources (e.g., Excel, SQL databases).
  • Designing dashboards with charts, filters, and drill-down options.
  • Sharing dashboards for stakeholder review.

Tableau is useful for building dashboards eg dashboard to track sales performance by region and product.

• Power BI: Power BI is a Microsoft business intelligence tool that transforms raw data from various sources into interactive visualizations and reports, enabling users to make data-driven decisions. It combines analytics, data visualization, and user-friendly features to help organizations track performance and uncover insights effectively. Key Power BI skills to learn are:
  • Data modeling to combine datasets.
  • Using DAX (Data Analysis Expressions) for custom calculations.
  • Publishing and sharing reports securely.

3. Basic Statistics

An entry-level data analyst’s learning curve doesn’t just stop at having learned visualization tools, he also has to learn what statistics are all about. Statistics is the backbone of data analysis, enabling analysts to draw meaningful conclusions from data. 

Here are some of the important statistics concepts  you need to learn as an entry-level data analyst;

• Mean, median, mode, and standard deviation.
• Probability and distributions.
• Hypothesis testing (e.g., t-tests, chi-square tests).

4. Soft Skills

Soft skills are non-technical skills and don’t involve learning software or tools but they are equally important for a successful data analytics career. These include:

• Problem-Solving Skills: Analytical thinking is key to breaking down complex problems into actionable steps.
• Communication Skills: Presenting insights in a way that non-technical stakeholders can understand is vital.
• Adaptability: With the rapid pace of change in data tools and techniques, analysts must continuously learn and adapt.

Entry-Level Data Analyst Career Pathways

There are several career paths open to entry-level data analysts. These career paths include:

Data Analyst Intern

Data analyst interns often shadow senior analysts and gain hands-on experience with company tools and processes by being part of projects alongside the senior data analyst.

• Projects: You will usually handle more small-scale tasks like cleaning datasets, preparing reports, or even serving as support for bigger projects.
• Salary Insights: Typically paid on an hourly basis or offered stipends, with the potential for conversion to a full-time role.

Junior Data Analyst

Junior analysts slightly differ from interns, they work more independently and can take on end-to-end analysis for small/not complex projects.

• Career Progression: This usually leads to mid-level roles, specialization in tools, or transitioning into data science.
• Salary Expectations: Competitive starting salaries, with growth tied to performance and additional skills.

Data Quality Analyst

A data quality analyst’s main focus is to ensure data accuracy and consistency across systems. It’s necessary to learn advanced data cleaning techniques and be familiar with data validation processes.

• Growth: Opportunities in data governance or transitioning to data architecture roles.
• Salary Expectations: 

Business Intelligence (BI) Analyst

BI analysts focus on building and maintaining dashboards, reports, and other tools for decision-making. 

• Career Path: Can transition to BI Manager or Data Architect roles.
• Salary Expectations: 

Marketing Data Analyst

Analyzes marketing efforts, such as campaign performance and customer behavior. Also needs to be familiar with marketing analytics tools and A/B testing techniques.

• Career Opportunities: Career advancement into marketing strategy or leadership roles focused on data-driven decision-making.
• Salary Expectations: 

Data Analyst Industry Insights

Data analysts are needed and can work in almost every sector of the economy, however, their roles are more pronounced and demand is higher in these sectors;

1. Technology sector: Data analysts work on product analytics, user behavior, and tech adoption metrics in the tech sector to boost product performances.
2. Finance sector: Data analysts work in areas like risk analysis, fraud detection, and investment strategy.
3. Healthcare sector: The growing focus on improving patient outcomes, and operational efficiency has increased the demand for health tech analysts.

Comparative Analysis

We compared the sectors with high demand for data analysts –  based on the necessary skill set, responsibilities, and work environment.

S/N	Finance	Healthcare	Technology
Skill Sets	Requires a strong grasp of statistical analysis and regulatory compliance.	Involves familiarity with medical terminology and privacy laws.	Emphasizes agile methodologies and quick data iteration cycles.
Responsibilities	Prioritizes forecasting and compliance.	Marketing: Focuses on consumer behavior and campaign effectiveness.	Often involves product analytics and A/B testing.
Work Environments	Structured, highly regulated environments.	Flexible and fast-paced, with remote work options.	Collaborative, often working closely with medical professionals.

How to Land an Entry-Level Data Analyst Job

Acquire Data Analytics Skills

Proper training is the first step to getting an entry-level data analyst job. The data analytics field requires technical skills and because data analysts’ work directly affects the trajectory of an organization,  getting the right training is very important. Sources of this training are myriad, ranging from formal degrees, bootcamps, or self-paced learning, to entry-level data analyst programs.

Entry Level Training Programs

For beginners, programs like Evolve with Tech provides an excellent starting point. With a focus on accessible and well-structured learning formats, Evolve with Tech helps learners build a solid foundation in data analytics, offering hands-on exercises and real-world scenarios.  Our data analytics program is beneficial for entry-level learners because it combines industry-relevant content with mentorship opportunities.

Degree Programs

As an entry-level data analyst you might decide to get a degree in data analytics,  some important factors to consider are;

• Merits: Degrees in data analytics or related fields provide comprehensive education, access to research opportunities, and networking connections. 
• Cons: Degrees can be quite expensive, and time-consuming, and may include courses that are not directly relevant to data analytics.
• Time & Cost: Getting a degree in data analytics typically requires 3–4 years and costs $100,000–$50,000 or more, depending on the institution.

Bootcamps

Bootcamps are usually focused on practical, job-ready skills in a shorter time frame. Most boot camps offer career support and networking opportunities.

• Cons: Intensive schedules, which can become overwhelming. Some programs may even lack depth as they attempt to cover a lot in a short period.
• Time & Cost: Can last anywhere from 2–4 months, costing $5,000–$20,000.

Online Courses

Merits: Affordable, flexible, and mostly taught by industry experts and leaders in the field, which makes it very appealing especially for entry-level data analysts.

• Cons: Self-discipline is crucial as you normally learn at your pace without any supervision; limited career support compared to degrees or bootcamps.
• Time & Cost: Self-paced, costing $50–$500 per course.

Self-Paced Learning

It’s possible to also learn using free resources like YouTube, blogs, and open-source platforms.

• Merits: Free or low-cost, allows flexibility, encourages problem-solving.
• Cons: Requires significant motivation and planning to ensure progress.
• Time & Cost: Self-paced; usually free.

Build a Standout Portfolio

Building a portfolio is non-negotiable especially for entry-level data analysts, as it demonstrates your skills and problem-solving abilities to potential employers. Here’s how you can go about building your portfolio:

• Project Ideas: Analyze public datasets, such as government data or sports statistics, Create dashboards with tools like Tableau or Power BI or Conduct exploratory data analysis using Python or R.
• Showcase your projects: Your portfolio should be readily accessible, therefore share it on platforms like; GitHub and Kaggle, you can also create a personal website/blog to host your projects with tools like Squarespace and WordPress.

You should always use actual datasets from sources such as government portals or open data repositories. This showcases your ability to work with raw, unstructured data.

Gain Real Experience

Although you’re seeking an entry-level position, recruiters also look out for experience and prioritize candidates with experience over candidates without one. Several opportunities are available to gain experience such as:

• Internships/Micro Internships: Internships are a great avenue to gain experience – apply for paid or unpaid internships at startups or small organizations to gain hands-on experience. You can find internships on Handshake.
• Volunteer: You can gain experience by offering your services to nonprofits, local businesses, or student organizations, these experiences can be added to your portfolio.

Apply For Entry-Level Data Analyst Jobs

It’s also important to know how to properly apply for roles and common mistakes to avoid – to increase your chances of landing your entry-level data analyst job. Some important tips are;

• Optimize your Resume: Highlight projects you have worked on, technical skills, and how proficient you are at using tools like SQL, Python, or Excel. Use keywords from job descriptions to easily be found by the applicant tracking systems (ATS).
• Tailor your cover letters to the job roles: Don’t copy and paste, instead focus on explaining how your skills align with the role, using specific examples from your projects or experiences.
• Optimize your LinkedIn profile: If you don’t have a LinkedIn profile yet, create one, most job roles often require you to share your LinkedIn profile link. After creating your LinkedIn account, optimize your profile with relevant keywords, showcase projects, and engage with industry professionals on the app.

Prepare Adequately for Interviews

Your application process doesn’t stop at you having the best job application tactics. You also need to know how to ace your interviews. Proper preparation is key to acing interviews, important preparation tips are:

• Practice technical questions: Practice SQL queries, Python data manipulation, and statistical concepts like regression or hypothesis testing.
• Practice Answering Behavioural Questions: Use the STAR method to relate past experiences to problem-solving and teamwork in data analysis.
• Practice Answering Case Studies: Approach problems methodically: define the problem, outline steps, and explain your thought process clearly.

Salary Insights for Entry-Level Data Analysts

The salary of entry-level data analysts can vary widely based on these three important factors namely location, industry, and experience. 

• Geographical Differences: Salaries in tech hubs like San Francisco, New York, and Seattle are higher, often ranging from $60,000 to $80,000 annually. However, in smaller cities salaries may range from $40,000 to $55,000, reflecting cost-of-living differences.
• Industry Specific: Oftentimes the salary options for entry-level data analysts depend on the industry.

• Experience Level: Prior experience also affects how much entry-level data analysts earn. Interns typically earn between $15–$25 per hour, depending on location and company. Entry-level analysts with <1 year of experience earn a slight fraction above the interns. However, within 2–3 years, analysts with proven skills can earn $70,000–$90,000 annually.
• Skills: Proficiency in high-demand tools like SQL, Python, Tableau, or Power BI can increase your salary offers, so develop these high-demand technical skills. Getting specialized skills, such as machine learning or predictive modeling, can also lead to you getting better offers.
• Negotiation: Salary negotiation is often ignored, but can greatly affect the salary entry-level data analysts are offered. Most organizations typically have a salary range so research the market rates for your location and industry before accepting any offers. 

Job Search Strategies for Entry-Level Data Analysts

• Job Boards: Use general sites like Indeed, Glassdoor, and AngelList, which frequently list data analyst roles. You can use the filter by entering “entry-level” or “junior” to find appropriate positions.

• Company Websites: Many large corporations and startups post job openings directly on their websites. Be Proactive in applying to companies of interest, even if no roles are currently advertised.
• Check out Niche Sites: Platforms like DataJobs, KDNuggets, and Hired specialize in analytics and data science roles. Niche platforms often have listings tailored to specific industries, such as healthcare or finance, so leverage them.

Networking

Networking is one of the important to build professional relationships which can in turn lead to referrals and job offers. Here are some of the best ways to go about building your network:

• LinkedIn: Use LinkedIn to build a professional profile showcasing your skills and projects. Try to connect with industry professionals and join groups related to data analytics. Engage with content by commenting, sharing insights, or posting about your projects.
  
• Attending Conferences: Attend virtual or in-person conferences like ODSC (Open Data Science Conference) or Data Analytics Summit to network and learn.

• Leveraging Alumni Networks: Leverage connections from your school or training programs to find job leads. Reach out to alumni working in analytics for mentorship or advice.

Alternative Paths to Follow as an entry-level data analyst 

For those seeking flexibility or unique opportunities, alternative career paths can provide valuable experience.

• Freelance: Use platforms like Upwork to find short-term data analysis projects. Freelancing also helps build your portfolio and develop client management skills.
• Take on Project-Based Work: Approach small businesses or startups with specific proposals for data projects. Offer to solve a particular problem, such as improving sales forecasting or analyzing customer data.
• Open Source Data Projects: Contributing to open-source data projects on GitHub or Kaggle by offering solutions and answers to problems. Gaining visibility in the data community can lead to job opportunities and networking.

Conclusion

In this post, we outlined what you need to know about entry-level data analysts – roles, responsibilities, salary, and how to land an entry-level data analysis role. As an entry-level or aspiring entry-level data analyst, it’s important to embrace change and use every experience as a learning opportunity. The data industry evolves rapidly, and adaptability is key to long-term success.

Considering starting a career as a data analyst, visit evolvewithTech/data-analytics to kickstart your journey in data analytics. Don’t wait—start today!