ATS Score
75/100
Profile
78/100
Certifications
70/100
Skills
85/100
Projects
80/100
Work Experience
65/100
Header
80/100
Education
60/100
Expand work experience with more detailed descriptions and quantifiable achievements to improve ATS keyword density and recruiter appeal.
Complete education section with degree types, expected graduation dates, and relevant coursework.
Add dates to certifications and projects to provide timeline context and show recent activity.
Reduce redundancy in the skills section and organize skills into clear categories for better readability.
Include additional professional links such as LinkedIn to strengthen header and professional branding.
Strong technical foundation in Python, SQL, Excel, and data visualization tools like Tableau and Seaborn.
Hands-on experience demonstrated through multiple diverse portfolio projects with real-world datasets.
Clear, concise profile summary emphasizing analytical skills and ability to translate data into actionable insights.
Relevant certifications from reputable online platforms supporting data analytics expertise.
Profile
78%
The summary is well-written and includes strong keywords such as 'Python', 'SQL', 'Tableau', 'data cleaning', and 'visualizing complex data' which are ATS-friendly.
Consider adding quantifiable achievements or metrics to demonstrate impact, e.g., 'improved report accuracy by 15%' or 'reduced analysis time by 20%'.
Profile highlights effectively showcase technical skills and project outcomes; ensure consistent formatting and consider expanding on the business impact of these projects.
Certifications
70%
Certifications listed are relevant and from recognized platforms, which supports credibility.
Add dates awarded to show recent and ongoing professional development.
Consider listing certifications in a consistent format including credential IDs if available.
Skills
85%
The skills section includes a strong mix of hard skills, soft skills, and tools relevant to data analysis roles, supporting ATS keyword matching.
Avoid redundancy by consolidating repeated skills (e.g., Python, SQL, Excel appear multiple times).
Consider grouping skills by category (Programming Languages, Data Visualization, Tools) to enhance readability.
Projects
80%
Projects are well-detailed and demonstrate hands-on experience with relevant tools and datasets, which is compelling for junior data analyst roles.
Add specific outcomes or results from projects (e.g., 'identified spending trends that saved $X monthly') to quantify impact.
Include project dates or durations to provide timeline context.
Work Experience
65%
Work experience is limited to a single role with a recent start date; this reduces ATS keyword density and perceived experience depth.
Descriptions use bullet points but lack quantifiable metrics; add measurable outcomes such as 'reduced manual errors by X%' or 'improved inventory tracking efficiency by Y%'.
Clarify employment type and add exact start month/year for better timeline clarity.
Expand on the scope and technologies used in automation scripts to better highlight technical proficiency.
Header
80%
The header includes a clear professional headline with relevant keywords like 'Junior Data Analyst', 'Python', 'SQL', and 'Excel', which improves ATS discovery.
Add a LinkedIn profile URL or professional website link in addition to the portfolio to enhance recruiter access to your professional brand.
Education
60%
Education entries include relevant institutions and fields of study but lack degree completion status and dates, which ATS systems often look for.
Add expected graduation dates and degree types to improve completeness.
Include any relevant coursework or academic projects that align with data analysis to strengthen this section.