Getting a job as a Data Scientist as a fresher may require a proactive approach and a combination of the following steps:

1. Build a strong foundation: Acquire a solid understanding of the core concepts and skills in data science. This includes knowledge of statistics, mathematics, programming languages (such as Python or R), and machine learning algorithms. Take relevant courses, pursue online tutorials, and engage in hands-on projects to build your skills.

2. Earn relevant certifications: Consider obtaining certifications that validate your knowledge and skills in data science. Certifications from reputable organizations or platforms like Coursera, edX, or Kaggle can add credibility to your profile and demonstrate your commitment to the field.

3. Complete data science projects: Undertake personal data science projects to showcase your practical abilities. Work on real-world datasets, apply machine learning algorithms, and present your findings. Building a portfolio of projects demonstrates your capability to solve data-driven problems and provides evidence of your skills to potential employers.

4. Internships and freelance work: Look for internships or freelance opportunities that allow you to work on data science projects. These experiences provide hands-on exposure and allow you to gain practical skills while building your professional network. Seek out companies, startups, or research institutions that offer internships in data science.

5. Participate in Kaggle competitions: Kaggle is a platform for data science competitions where you can collaborate, learn, and solve real-world problems. Participating in Kaggle competitions not only helps you develop your skills but also provides exposure to the data science community. Employers often value Kaggle experience as it demonstrates your ability to apply data science techniques.

6. Contribute to open-source projects: Contribute to open-source data science projects on platforms like GitHub. It showcases your collaboration skills, code quality, and engagement with the data science community. Potential employers may look for evidence of your contributions to assess your coding abilities and commitment to the field.

7. Networking: Build connections within the data science community. Attend meetups, conferences, and industry events. Engage in online forums, discussion groups, and social media platforms related to data science. Networking can provide valuable insights, job opportunities, and mentorship from experienced professionals.

8. Create an impactful resume and cover letter: Tailor your resume and cover letter to highlight your relevant skills, projects, certifications, and internships. Emphasize your academic achievements, coursework, and any research experience related to data science. Highlight transferable skills such as problem-solving, critical thinking, and analytical abilities.

9. Apply for entry-level positions: Look for entry-level Data Scientist positions, junior roles, or internships specifically targeted at freshers. Some companies offer rotational programs or training programs to develop talent in data science. Be open to starting at a junior level to gain practical experience and gradually progress in your career.

10. Prepare for interviews: Practice technical and behavioral interview questions specific to data science roles. Be prepared to discuss your projects, methodologies, problem-solving approaches, and how you have applied your skills in real-world scenarios. Review common data science interview questions, algorithms, and statistical concepts. Demonstrating your ability to think analytically and communicate effectively is essential.

Remember, starting as a fresher in data science may involve more entry-level positions or internships to gain experience and build your profile. Continuously improve your skills, stay updated with industry trends, and remain persistent in your job search. As you gain experience and expand your knowledge, you can progress in your data science career.