9 answers
1
Question
Updated
1717 views
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.
Login to comment
8 answers
Updated
Maximus’s Answer
Hey Aishwarya, I totally understand how challenging it can be to land that first job in Data Science, especially since it's such a competitive field. If I could offer a few tips that might help you stand out, here’s what I’d suggest:
Build a Strong Portfolio: This is probably the most important. Create some personal projects or participate in online challenges like those on Kaggle. It's a great way to show what you can do. Keep them on GitHub, and make sure they’re well-documented so potential employers can easily see your skills.
Master the Key Tools: Make sure you’re comfortable with Python (or R), and don’t forget tools like Pandas, NumPy, and Scikit-learn. For data visualization, it’s good to get some experience with Matplotlib, Seaborn, or even Tableau.
Real-World Experience: Try to gain some practical experience, even if it’s through internships, volunteer projects, or freelancing. Getting your hands dirty with real data will help you learn faster and build credibility.
Networking: Don’t underestimate the power of networking! Attend local meetups or online events, and connect with people on LinkedIn. You never know when a potential opportunity might come from someone you know.
Keep Learning: Data Science is always evolving, so it’s important to keep up with new tools, techniques, and trends. Courses, certifications, or even reading research papers can keep you ahead of the curve.
It’s all about building a strong foundation and keeping a positive mindset. The job market can be tough, but the right combination of skills, experience, and connections will open doors for you.
If you ever need any help with your projects or just want to bounce ideas around, feel free to reach out. I’d be happy to share more thoughts or resources with you!
Build a Strong Portfolio: This is probably the most important. Create some personal projects or participate in online challenges like those on Kaggle. It's a great way to show what you can do. Keep them on GitHub, and make sure they’re well-documented so potential employers can easily see your skills.
Master the Key Tools: Make sure you’re comfortable with Python (or R), and don’t forget tools like Pandas, NumPy, and Scikit-learn. For data visualization, it’s good to get some experience with Matplotlib, Seaborn, or even Tableau.
Real-World Experience: Try to gain some practical experience, even if it’s through internships, volunteer projects, or freelancing. Getting your hands dirty with real data will help you learn faster and build credibility.
Networking: Don’t underestimate the power of networking! Attend local meetups or online events, and connect with people on LinkedIn. You never know when a potential opportunity might come from someone you know.
Keep Learning: Data Science is always evolving, so it’s important to keep up with new tools, techniques, and trends. Courses, certifications, or even reading research papers can keep you ahead of the curve.
It’s all about building a strong foundation and keeping a positive mindset. The job market can be tough, but the right combination of skills, experience, and connections will open doors for you.
If you ever need any help with your projects or just want to bounce ideas around, feel free to reach out. I’d be happy to share more thoughts or resources with you!
Thank you!
Aishwarya
Updated
Neh’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
Carrie’s Answer
I agree with everyone's answer so far, but one thing I would like to highlight is -- industry knowledge. Lately, I have noticed that job postings are looking for data people that have an understanding of real estate, marketing, healthcare, etc. because having some industry knowledge will make you a stronger candidate, but also craft any portfolio projects around a common issue in that industry. Also, when reading job postings, notice if there are any reoccurring data science methods that each industry seems to consistently ask for. For example, e-commerce will frequently ask that data people have some segmentation, forecasting, or personalization experience. Lastly, creating a project for your portfolio that you are passionate about will make it so much easier when someone asks why you chose that project, the process you went through, the snags and plateaus. You become more animated and passionate when you talk about something you have a genuine interest in.
Your advice was so helpful!
Aishwarya
Updated
Anubhav’s Answer
Getting a data science job as a new graduate is possible, but you need to stand out beyond your degree. Employers want to see that you can solve real-world problems, not just do well on tests. Here’s how to improve your chances:
1. Create a strong project portfolio to show your skills.
2. Learn the essential skills needed for data science.
3. Focus on solving problems, not just building models.
4. Join competitions and hackathons to gain experience.
5. Write a strong resume and apply to jobs thoughtfully.
1. Create a strong project portfolio to show your skills.
2. Learn the essential skills needed for data science.
3. Focus on solving problems, not just building models.
4. Join competitions and hackathons to gain experience.
5. Write a strong resume and apply to jobs thoughtfully.
Updated
PS’s Answer
Congratulations on earning your MSc in Data Science! It's normal to wonder about your next steps. Here's a simple plan to kick-start your career:
1. Develop a strong machine learning project and get it deployed.
2. Complete a project that involves extensive use of SQL for analytics.
3. Create a project that results in a clear and informative dashboard.
4. Prepare a clean, one-page resume.
5. Share insights from your projects weekly on LinkedIn to stay active.
Consider these questions as you move forward:
- Are you interested in roles as a Data Scientist, Data Analyst, or ML Engineer?
- Do you have internship experience, or is this your first position?
I've added these suggestions based on what other experts recommend and my own knowledge. Keep going; you're on the right track!
1. Develop a strong machine learning project and get it deployed.
2. Complete a project that involves extensive use of SQL for analytics.
3. Create a project that results in a clear and informative dashboard.
4. Prepare a clean, one-page resume.
5. Share insights from your projects weekly on LinkedIn to stay active.
Consider these questions as you move forward:
- Are you interested in roles as a Data Scientist, Data Analyst, or ML Engineer?
- Do you have internship experience, or is this your first position?
I've added these suggestions based on what other experts recommend and my own knowledge. Keep going; you're on the right track!
Thanks for your encouragement!
Aishwarya
Updated
Divyanshu’s Answer
Great start with the MSc in Data Science! Congratulations!
Here are five things I would recommend:
1) AI will touch virtually every industry and role, so learning the fundamentals now will help you stay competitive:
- AI and Machine Learning fundamentals
- Natural Language Processing (NLP) basics
2) You will need to be fluent in at least one programming language. I recommend starting with Python.
Python is the main language used in AI.
Start with:
- Variables, loops, functions
- Working with files and data
- Common libraries later like pandas, numpy, scikit-learn
3) Certifications can strengthen your resume and improve your chances of landing a job by showing verified, job-ready skills.
Target an Alteryx certification, it is a powerful code-free data analytics tool.
Use the following link to start learning for free:
https://www.alteryx.com/sparked/learning-programs/students
I am recommending the Alteryx certification because:
- It is a widely recognized data analytics tool
- There are free online lessons and certifications provided by Alteryx
- Knowledge gained from Alteryx certification is useful in various roles
4) Strengthen your communication skills, they will help you perform better in interviews and set you up for success at work.
In Technology you often need to explain complex ideas simply.
Work on:
- Storytelling, explaining the “why” behind your work
- Public speaking and presentation
- Active listening and asking good questions
5) Build experience through volunteering, internships and competitions.
Wishing you the very best in your job search!
Here are five things I would recommend:
1) AI will touch virtually every industry and role, so learning the fundamentals now will help you stay competitive:
- AI and Machine Learning fundamentals
- Natural Language Processing (NLP) basics
2) You will need to be fluent in at least one programming language. I recommend starting with Python.
Python is the main language used in AI.
Start with:
- Variables, loops, functions
- Working with files and data
- Common libraries later like pandas, numpy, scikit-learn
3) Certifications can strengthen your resume and improve your chances of landing a job by showing verified, job-ready skills.
Target an Alteryx certification, it is a powerful code-free data analytics tool.
Use the following link to start learning for free:
https://www.alteryx.com/sparked/learning-programs/students
I am recommending the Alteryx certification because:
- It is a widely recognized data analytics tool
- There are free online lessons and certifications provided by Alteryx
- Knowledge gained from Alteryx certification is useful in various roles
4) Strengthen your communication skills, they will help you perform better in interviews and set you up for success at work.
In Technology you often need to explain complex ideas simply.
Work on:
- Storytelling, explaining the “why” behind your work
- Public speaking and presentation
- Active listening and asking good questions
5) Build experience through volunteering, internships and competitions.
Wishing you the very best in your job search!
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.
This was super helpful, thank you!
Aishwarya
Updated
Sandeep’s Answer
Hello Aishwarya,
To improve your chances, focus on showing practical skills, not just your degree. Build 2–3 strong projects using real datasets, clearly explain your impact, and link them on your resume and GitHub/Bitbucket. Make sure you’re comfortable with Python, SQL, data visualization, and basic ML, and tailor your resume to each role. Consistent practice, networking on LinkedIn, and applying regularly also make a big difference for freshers.
To improve your chances, focus on showing practical skills, not just your degree. Build 2–3 strong projects using real datasets, clearly explain your impact, and link them on your resume and GitHub/Bitbucket. Make sure you’re comfortable with Python, SQL, data visualization, and basic ML, and tailor your resume to each role. Consistent practice, networking on LinkedIn, and applying regularly also make a big difference for freshers.
Thank you, this is amazing! I really needed it.
Aishwarya