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How can a recent graduate in Data Science improve their chances of landing a job?
I recently completed my MSc in Data Science, and I’m actively looking for entry-level roles in data science/data analytics.
I would love advice on how to improve my resume and what skills or projects recruiters value most for freshers.
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Nehaba’s Answer
To improve your chances, tailor your resume to highlight relevant skills like Python, R, SQL, and machine learning, and include any hands-on projects or internships showcasing real data problem-solving. Emphasize projects with clear outcomes using tools like Tableau or Power BI. Consider contributing to open-source projects or competitions on Kaggle to build your portfolio. Networking, attending industry meetups, and seeking certifications in tools like AWS or Azure can also strengthen your profile. Additionally, one advice I always give - build your portfolio on open source platform like GitHub and include this link in your resume. Your portfolio can include projects related to your industry of interest like utilities where you can show gain/loss of a utility company over the years using dummy data or number of households it served etc. on a dashboard. Interviewers who are looking for freshers many a times look at who got the real world understanding of problems. Hope this helps!
Thank you for giving me advice.
Aishwarya
Updated
Vianne’s Answer
Getting into data science right after your Master's can be challenging, but it's definitely possible with the right approach. Recruiters care more about your ability to solve problems with data and communicate your findings than the specific title of your degree. Here are some tips to boost your chances as a newcomer.
First, focus your resume on projects rather than courses. Highlight 3 to 5 strong projects and describe them like job experiences. For each project, mention the problem you tackled, the data and tools you used, and the results you achieved. Use numbers to show your impact, like improving model accuracy or finding insights that help make decisions. Group your skills clearly, such as Python, SQL, machine learning, data visualization, and cloud tools if you know them.
Second, prioritize the skills that matter most for entry-level jobs. Basic knowledge of Python, SQL, statistics, and data cleaning is more important than knowing advanced algorithms. Employers want people who can work with messy data, write clear queries, and explain their findings to those who aren't technical. Familiarity with tools like Pandas, NumPy, scikit-learn, Tableau, or Power BI is often expected. Good communication is key, so practice explaining your projects simply.
Third, create practical projects from start to finish. Use real-world data instead of just toy datasets, and show the entire process from data collection and cleaning to analysis, modeling, and visualization. Examples include customer churn analysis, sales forecasting, recommendation systems, or using public policy and health data. Share your projects on GitHub with clean code, clear instructions, and visual results to make it easy for recruiters to understand your work.
Lastly, work on being visible and networking. Keep your LinkedIn profile active by sharing projects, insights, or what you've learned. Connect with alumni, attend data meetups, and apply for jobs regularly. Customize your resume slightly for each job, focusing on analytics for analyst roles and modeling for data science roles. Getting rejections is common at first, but each application and interview helps you get better.
First, focus your resume on projects rather than courses. Highlight 3 to 5 strong projects and describe them like job experiences. For each project, mention the problem you tackled, the data and tools you used, and the results you achieved. Use numbers to show your impact, like improving model accuracy or finding insights that help make decisions. Group your skills clearly, such as Python, SQL, machine learning, data visualization, and cloud tools if you know them.
Second, prioritize the skills that matter most for entry-level jobs. Basic knowledge of Python, SQL, statistics, and data cleaning is more important than knowing advanced algorithms. Employers want people who can work with messy data, write clear queries, and explain their findings to those who aren't technical. Familiarity with tools like Pandas, NumPy, scikit-learn, Tableau, or Power BI is often expected. Good communication is key, so practice explaining your projects simply.
Third, create practical projects from start to finish. Use real-world data instead of just toy datasets, and show the entire process from data collection and cleaning to analysis, modeling, and visualization. Examples include customer churn analysis, sales forecasting, recommendation systems, or using public policy and health data. Share your projects on GitHub with clean code, clear instructions, and visual results to make it easy for recruiters to understand your work.
Lastly, work on being visible and networking. Keep your LinkedIn profile active by sharing projects, insights, or what you've learned. Connect with alumni, attend data meetups, and apply for jobs regularly. Customize your resume slightly for each job, focusing on analytics for analyst roles and modeling for data science roles. Getting rejections is common at first, but each application and interview helps you get better.