Entry level data analyst resume for freshers

Top points for Entry level data analyst resume for freshers in 2023

Table of Contents

Contact Information

Full Name:

At the beginning of your resumes, provide your entire name. To make it stand out from the rest of the text, use a font size that is just a little larger.

Phone Number:

Give potential employers a trustworthy phone number to contact you at. Specify your country code if necessary.

Email Address:

Use a formal email address that begins with your name or a close variation of it. Do not use email addresses that are inappropriate or unprofessional.

LinkedIn Profile URL:

Include the URL to your complete, up-to-date LinkedIn profile, which should be included. Make sure your profile highlights your abilities, education, projects, and any pertinent employment history.


  • Full Name: Manish Pandey
  • Phone Number: +91 9411776745
  • Email Address: manish.doe@email.com
  • LinkedIn Profile URL: www.linkedin.com/in/manish

Remember to provide accurate and up-to-date contact information. Make sure your voicemail greeting is professional in case employers leave a message. Additionally, review your LinkedIn profile to ensure it presents you in a positive and professional light, as potential employers may view it to learn more about you.

Don’t forget to adjust your objective statement to reflect the precise degree and certification you hold. Be sure to highlight your ability to analyse data, statistical analysis knowledge, and data manipulation and visualisation skills. Emphasise your dedication to utilise data-driven insights to inform decision-making and support an organization’s expansion.



As a beginning data analyst, you might highlight your professional objectives in the objective part of your resume. It need to reflect your passion for the subject matter and draw attention to your major qualifications. Keep the purpose brief and precise, and use powerful language to draw the reader in.

Example 1.:


Highly motivated and analytical recent graduate with a certification in Data analytics seeking an entry-level data analyst position to apply strong problem-solving and analytical skills. Eager to contribute to a dynamic team, utilize statistical analysis tools, and gain practical experience in data manipulation, visualization, and machine learning.

example 2:


Detail-oriented and analytical recent graduate with a Bachelor of Arts degree in Psychology and a certificate in Data Analytics from Vista Academy. Seeking an entry-level data analyst position to apply my strong analytical skills, knowledge of statistical analysis, and proficiency in data manipulation and visualization. Committed to leveraging data-driven insights to support decision-making and contribute to business growth.

  1. The applicant has a Bachelor of Arts in Psychology, demonstrating their comprehension of research techniques and human behaviour.
  2. They have a certificate in Data Analytics from Vista Academy, which attests to their advanced data analysis skills.
  3. They highlight their adeptness at manipulating and analysing data through their excellent analytical abilities, statistical analysis knowledge, and data manipulation and visualisation skills.
  4. They state their dedication to using data-driven insights to inform decisions and assist business expansion.

Don’t forget to adjust your objective statement to reflect the precise degree and certification you hold. Be sure to highlight your ability to analyse data, statistical analysis knowledge, and data manipulation and visualisation skills. Emphasise your dedication to utilise data-driven insights to inform decision-making and support an organization’s expansion.

Technical Skills:

It’s crucial to emphasise your technical expertise with regard to data analysis and the technologies you are familiar with as a beginning data analyst. Employers can see your technical skills fast thanks to this part. Consider the following important technical skills:

Statistical Analysis:

Display your mastery of statistical analysis methods and tools, such as data modelling, regression analysis, and hypothesis testing. Mention any particular programming languages or statistical tools that you are familiar with, such as Python’s statistical libraries (such as NumPy and SciPy).

Data Manipulation

Draw attention to your data manipulation and cleaning skills by utilising tools like SQL (Structured Query Language) for database querying, filtering, and dataset joining. Include any knowledge you may have of data cleansing procedures, such as handling missing numbers, detecting outliers, and data transformation.

Data Visualization:

Showcase your skills in data visualisation to effectively convey findings. Mention any visualisation software you are knowledgeable about, such as Tableau, Power BI, or Python’s matplotlib. Talk about your capacity to design visually appealing charts, graphs, and dashboards that effectively and succinctly communicate complicated information.


Showcase your programming expertise, especially in languages like Python or R that are frequently used for data research. Showcase your knowledge of working with data structures, producing effective and clean code, and utilising libraries and packages for data analysis tasks.

Database Management:

Mention your expertise in database management systems (DBMS) like MySQL, PostgreSQL, or MongoDB if you have experience working with databases. Include your knowledge of designing and optimising database structures, writing SQL queries, and extracting data for analysis.

Machine Learning

Highlight your expertise of algorithms, model training, and evaluation if you have any experience or knowledge in machine learning. Mention any machine learning frameworks or libraries that you are familiar with, such as TensorFlow or Scikit-Learn.

Example of Technical Skills section:

Technical Skills:

  • Statistical Analysis: R, Python (NumPy, Pandas, scikit-learn)
    Data Manipulation:
  • SQL, Data cleaning techniques
  • Data Visualization: Tableau, matplotlib
  • python,
    Database Management:
  • MySQL, PostgreSQL
    Machine Learning: scikit-learn,


The key to exhibiting your practical expertise and showcasing your capacity to apply data analysis approaches to real-world circumstances is to highlight your data analysis projects. Include any pertinent projects you’ve worked on throughout your studies, during internships, or as part of your own initiatives. How to present your projects is as follows:

  • Give each project a succinct title and clearly and succinctly summarise its goal or area of concentration.
  • What is the project? Give a brief summary of the project, mentioning the information used, the issue or question you sought to resolve, and any processes or strategies used.
  • Describe your position and duties: Describe the precise aspects of the project that you contributed to, such as data collecting, data cleaning and preprocessing, exploratory data analysis, statistical analysis, and data visualisation.
  • Describe the main conclusions or insights: Talk about any important conclusions or learnings you have from the analysis. Be sure to emphasise how these results helped to clarify the issue or provided useful advice.
  • Mention the equipment and methods employed: Include a list of the applications, programming languages, libraries, and frameworks that were used for the project. This demonstrates your familiarity with the equipment used often in the sector.
  • Mention any quantitative outcomes or results from your data analysis, such as greater accuracy, efficiency, or cost savings. If possible, quantify the results.

Example of a Project section:



Customer Segmentation Analysis
  • Used a dataset of customer demographics and purchase history to segment customers based on their buying behavior.
  • Conducted exploratory data analysis and applied clustering algorithms (k-means) to identify distinct customer segments.
  • Created visualizations to illustrate the characteristics and purchasing patterns of each segment.
  • Identified key insights, such as high-value customer groups and potential marketing strategies for each segment.
  • Tools Used: Python (NumPy, Pandas, scikit-learn), Tableau

Sales Forecasting for a Retail Store
  • Analyzed historical sales data to develop a forecasting model for predicting future sales.
  • Preprocessed the data, performed time series analysis, and applied forecasting techniques (ARIMA) to generate predictions.
  • Evaluated the model’s performance using appropriate metrics and visualized the forecasted sales trends.
  • Provided actionable recommendations to optimize inventory management and staffing based on the forecasted sales.
  • Tools Used: R, SQL, Tableau

Ensure that you select projects that demonstrate your proficiency in relevant data analysis techniques and tools. Focus on projects that highlight your ability to solve problems, work with real data, and draw meaningful insights. By showcasing your projects, you provide evidence of your practical skills and make your resume more impactful to potential employers.

Educational Qualifications:

Your school background is crucial in showing your foundation of knowledge and pertinent courses as a newcomer to the profession of data analysis. Here’s how to properly draw attention to your educational background:

  • your highest degree first: Mention your greatest level of education first, such as a Bachelor’s or Master’s degree, along with the subject area.
  • Add the institution’s name: Name the college or university where you received your degree.
  • Mention the degree’s name and its area of study: Mention the type of degree you have (e.g., Bachelor of Science, Master of Arts) as well as any concentrations or majors you may have had in that degree, such as Data Science, Statistics, Economics, or any other pertinent discipline.
  • Include the graduating year: Include the year you earned or anticipate earning your degree.

Educational Qualifications:

  • Bachelor of Arts in Economics , XYZ University, 2023
  • Relevant Coursework: Data Analysis, Statistical Modeling, Machine Learning, Database Management
  • Certificate in Data Analytics, Vista Academy, 2023

Keep in mind to concentrate on your educational background that is most pertinent to the position you are looking for. If you have earned any further qualifications or pertinent courses, you can list them in a separate section for professional development or certifications.

By emphasising your educational background, you may show prospective employers that you have formal training in data analysis and the fundamental skills required to succeed in an entry-level data analyst position.

Internships or Relevant Work Experience:

Freshers might not have much professional experience, but including internships or any relevant work experience can really help your resume stand out. The following are some efficient ways to draw attention to your internships or equivalent professional experience:

  • Identify the company: Name the business or organisation where you finished your internship or obtained the necessary experience.
  • Tell us about your duties: List the main duties and responsibilities you carried out throughout your internship or professional experience. Concentrate on data analysis-related duties, such as gathering data, cleaning it up, manipulating it, performing statistical analysis, or visualising it.
  • Describe your accomplishments: Mention any particular successes or results you were able to attain throughout your internship or employment experience. This can entail finishing a data analysis project on schedule, streamlining the data analysis procedure, or drawing useful conclusions from the data.
  • Highlight the methods and tools used: Mention the applications, coding languages, or equipment you used during your internship or professional experience. This demonstrates your practical expertise and knowledge with tools used in the sector.

Example of Internships or Relevant Work Experience section:

  • Internships or Relevant Work Experience:
    Data Analysis Intern, ABC Company, Summer 2022
  • Assisted in collecting and cleaning large datasets from various sources for analysis.
  • Conducted statistical analysis and performed data visualization to identify trends and patterns.
  • Created interactive dashboards to present data findings using Tableau.
    Collaborated with cross-functional teams to provide insights for business decision-making.

Don’t forget to emphasize any internships or employment that demonstrates transferable abilities like analytical thinking, problem-solving, or teamwork or is directly related to data analysis. If you have experience, highlight any duties or projects that show you can effectively work with data or analyse information, even if that experience isn’t particularly in data analysis.

Relevant Coursework

To demonstrate your knowledge and expertise in the industry, it’s essential that you give attention to any coursework or academic projects that are directly related to data analysis. Here are some tips for properly presenting your pertinent schoolwork or academic projects:

List relevant courses

Mention the classes you’ve taken that have a direct bearing on data analysis. Classes on statistics, data management, data visualisation, machine learning, or any other pertinent topics may fall under this category.

Highlight key projects

Mention any noteworthy tasks you have finished as part of your coursework. Describe the project’s goals, techniques, data analysis, and any noteworthy conclusions or insights that emerged from it.

Showcase technical skills:

The technical talents you developed or used for your coursework or academic projects should be highlighted. This might include tools for data visualisation, statistical software, database management systems, or computer languages.

Demonstrate teamwork or collaboration

Mention how well you can communicate your ideas, work well in a team, and contribute to the successful completion of tasks, especially if you have worked on group projects or cooperated with peers.

Example of Relevant Coursework or Academic Projects section:

Relevant Coursework or Academic Projects:
Data Analysis and Visualization
  • Completed a project analyzing a real-world dataset using Python and generating visualizations to identify trends and patterns.
Statistical Methods for Data Analysis
  • Conducted a comprehensive analysis of survey data using statistical techniques such as regression analysis and hypothesis testing.
Database Management Systems
  • Designed and implemented a relational database to store and retrieve data efficiently using SQL.

Remember to focus on coursework or academic projects that directly relate to data analysis and highlight the skills and knowledge you gained through those experiences. This section allows you to showcase your academic achievements and your ability to apply data analysis concepts in practical scenarios.

By highlighting relevant coursework or academic projects, you demonstrate to potential employers that you have acquired a strong foundation in data analysis principles and have practical experience working with data.

Data Analysis Tools

An entry-level data analyst must be proficient in data analysis applications and tools. You can demonstrate your familiarity with tools used in the industry and your proficiency with data in this part. Here’s how to demonstrate your knowledge of data analysis applications and tools:

List the tools and software

Mention the specific software and tools for data analysis with which you are familiar. Programming languages, statistical programmes, database management systems, data visualisation tools, and any other pertinent software may fall under this category.

Rate your proficiency:

Give each tool or piece of software a proficiency rating, such as “Proficient,” “Intermediate,” or “Advanced.” Employers can see what your expertise is through this.

Provide examples

Mention them as examples to demonstrate your practical application of these technologies if you have finished assignments or coursework utilising certain software or applications.

Example of Data Analysis Tools
  • Python (NumPy, Pandas, scikit-learn): Proficient
  • Excel:Advance
  • SQL: Proficient
  • Power Bi: Intermediate

You may prove that you are capable of working with a variety of technologies by displaying your skill with data analysis tools and applications. Your credibility as a beginning data analyst will be enhanced, and you’ll have a better chance of getting hired.

Analytical and Problem-Solving Skills:

Employers seek applicants with the analytical and problem-solving skills necessary for a data analyst position, as well as the ability to efficiently analyse data and produce insightful conclusions. How to demonstrate your capacity for analysis and problem-solving

Provide specific examples:

Mention examples of when you’ve worked on challenging projects that called for critical thought and problem-solving abilities or addressed analytical difficulties successfully. Projects for school, internships, or independent work all qualify.

Describe your approach

Give a brief description of your process for tackling analytical difficulties. Emphasise your capacity for dissecting complex issues into smaller, more manageable parts and your expertise in the use of analytical methods and tools to draw conclusions from data.

Demonstrate attention to detail

Emphasize your attention to detail in data analysis tasks. Discuss your ability to identify patterns, outliers, or inconsistencies in data, and how you ensure data accuracy and integrity.

Showcase critical thinking:

Highlight your ability to think critically and make logical connections between data points or variables. Discuss your capability to analyze data from multiple angles, consider alternative hypotheses, and draw well-supported conclusions.

Example of Analytical and Problem-Solving Skills section:

Analytical and Problem-Solving Skills:
  • Successfully analyzed a large dataset of customer feedback to identify recurring themes and patterns, leading to actionable recommendations for improving customer satisfaction.
  • Employed statistical analysis techniques to uncover relationships between product features and customer preferences, enabling data-driven decision-making in product development.
  • Demonstrated strong attention to detail by identifying data anomalies and conducting rigorous quality checks to ensure data accuracy.
communication for data analytics

Communication and Data Visualization Skills:

For a data analyst to communicate complicated information in a way that is understandable and straightforward, effective communication and data visualisation abilities are essential. How to demonstrate your data visualisation and communication abilities:

Present data visualization examples

Give examples of the data visualisations you’ve produced to clearly convey findings. You may have created charts, graphs, dashboards, or presentations for your studies, internships, or independent work.

Describe your ability to simplify complex concepts

Emphasize your capability to present complex analytical findings in a concise and understandable manner. Discuss how you have effectively communicated technical information to non-technical stakeholders or team members.

Highlight your written communication skills:

Mention any reports, records, or summaries you’ve created to clearly communicate analytical results. This demonstrates your capacity for clear thought and written information presentation.

Discuss your teamwork and collaboration skills

Data analysts frequently collaborate with different stakeholders and operate in teams. Emphasise your prior experience collaborating with others, communicating effectively, and participating in team initiatives.

Example of Communication and Data Visualization Skills section:


Communication and Data Visualization Skills:
  • Created interactive dashboards using Tableau to present complex data analysis findings in a visually appealing and easy-to-understand format.
  • Prepared comprehensive reports summarizing analytical insights and actionable recommendations for senior management.
  • Effectively communicated analytical findings to cross-functional teams, including non-technical stakeholders, resulting in improved decision-making processes.\Collaborated with team members to analyze data collectively and deliver impactful presentations on project outcomes.

Resume Sample

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