Reader's Digest - Are you looking for a top-paying career in Data Science for freshers? Read the blog for in-depth info on each job role, salary insights, growth, scope & more!

According to the "LinkedIn Emerging Jobs Report 2020", data science roles continue to grow across various sectors, with machine learning and data science positions growing by nearly 10% annually.

For those starting out, the blend of the right skills, continuous learning, and networking pave the way to secure these roles.

As businesses continue to value data-driven insights, the demand for these roles is expected to remain strong, offering freshers a promising career trajectory.

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Can a Fresher Get a Job in Data Science?

Absolutely! A fresher can indeed secure a job in data science, and there are several reasons to be optimistic about this:

• Growing Demand for Data Professionals: According to the World Economic Forum's "The Future of Jobs Report 2020," data analysts and scientists are among the roles that remain steadily in demand. The demand for these roles is projected to keep increasing as industries recognize the value of data-driven decision-making.
• Diverse Educational Platforms: The availability of numerous online courses, platforms, and certifications, such as Coursera, Udemy, and edX, has made it easier for freshers to acquire necessary data science skills without traditional schooling. This education democratization means that even those without formal degrees but with the right skill set can find entry-level opportunities.
• Increasing Number of Entry-Level Roles: Companies, recognizing the potential of young talent, have been creating more entry-level positions in data science. Roles like Data Analyst, Junior Data Scientist, and Business Analyst often require less experience but are gateways into the data science world.
• Internship Opportunities: Many firms offer internships in data analytics and data science. Though often short-term and sometimes unpaid, these internships provide invaluable real-world experience and often lead to full-time job offers.
• Bootcamps and Fellowships: Several intensive training programs or boot camps, such as General Assembly and Flatiron School, cater explicitly to freshers and those looking to transition into data science. They focus on imparting practical skills quickly, often culminating in project work that can be showcased to potential employers.
• Soft Skills Matter: While technical skills are crucial, soft skills like problem-solving, communication, and teamwork play a significant role. Companies often value these skills, understanding that technical aspects can be taught, but a growth mindset and soft skills can make the transition smoother.
• Networking: With an increasing number of data science conferences, seminars, webinars, and meetups available, freshers have more opportunities than ever to network, learn from professionals, and get referrals or job leads.

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Top 10 Data Science Jobs for Freshers

Let's break down fresher data scientist jobs in India:

#1 - Data Scientist

Job Role: Data scientists construct models utilizing programming languages, primarily Python.

These models are transformed into applications to tackle intricate issues by analyzing data for predictive insights. Working with business analysts, data engineers, and data architects, data scientists focus on solving complex problems using data-driven strategies.

Skills Needed: Proficiency in statistics, mathematics, machine learning, deep learning, programming, data analysis, big data processes, and familiarity with tools such as Hadoop and SQL.

Education: Ideally, a bachelor’s degree in a related field. However, data science boot camps, master’s programs, and professional certificates are also gaining traction. According to Burtch Works, over 94% of data scientists possess a master’s or a doctorate degree.

#2 - Data Analyst

Job Role: Data analysts operate with structured data, deriving business insights. They employ tools such as SQL, Python, and R and focus on data visualization, data acquisition, cleaning, and reorganization.

Skills Needed: Proficiency in programming languages like SQL, Python, R, and SAS, and expertise in statistics, math, and data visualization.

Education: Typically, a bachelor’s degree is in mathematics, computer science, finance, or statistics.

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#3 - Data Architect

Job Role: These professionals design blueprints for data management systems. They aim to integrate, centralize, protect, and maintain data sources.

Skills Needed: Proficiency in coding languages, notably Python and Java, coupled with a robust understanding of data mining, machine learning, SQL, and data modelling.

Education: A bachelor’s degree in data or computer science. Boot camps or professional certificates can also be beneficial.

#4 - Data Engineer

Job Role: They manage and prepare vast data quantities, optimizing data pipelines and infrastructure.

Skills Needed: Proficiency in programming languages, especially Java, and understanding NoSQL databases and frameworks like Apache Hadoop.

Education: Typically, a bachelor’s degree in relevant fields. Professional certificates and boot camps offer specialized skills.

#5 - Machine Learning Engineer

Job Role: Part of a data science ensemble, these engineers specialize in creating AI systems for facilitating machine learning.

Skills Needed: Familiarity with tools like Spark, Hadoop, R, Apache Kafka, and Tensorflow. A robust understanding of data structures, modelling, quantitative analysis, and computer science is essential.

Education: Often, advanced degrees like a master’s or Ph.D. are preferred.

#6 - Database Administrator

Job Role: Database Administrators (DBAs) ensure that databases run efficiently. They are responsible for the security, integrity, and performance of databases, and they back up data and restore it as necessary.

Skills Needed: SQL, understanding of specific databases like Oracle, SQL Server, or MySQL, backup and recovery procedures, and performance tuning.

Education: A bachelor's degree in computer science, IT, or a related field. Certifications in specific database technologies can be advantageous.

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#7 - Data Visualization Specialist

Job Role: They are focused on visually representing data in a clear and impactful manner, often working with data analysts and scientists to present findings in an easily digestible format.

Skills Needed: Proficiency in data visualization tools and libraries (like D3.js, Tableau), design principles, understanding of data structures, and storytelling.

Education: A bachelor's degree in design, computer science, or related fields. Courses in data visualization or graphic design can be beneficial.

#8 - BI Developer

Job Role: Business Intelligence (BI) Developers use data to create tools and solutions for companies to make business decisions. They create dashboards, generate reports, and maintain BI software and systems.

Skills Needed: Expertise in BI tools like PowerBI, QlikView, or Tableau, SQL, understanding of databases, and data warehousing concepts.

Education: A bachelor's in computer science, engineering, or a related field. Specialized courses or certifications in specific BI tools can be advantageous.

#9 - Data Journalist

Job Role: This is an emerging field where journalists leverage data to tell compelling stories. They analyze data to uncover stories, visualize their findings, and present them in an accessible manner to the general public.

Skills Needed: Data visualization tools (like Tableau or D3.js), basic programming, storytelling, and statistical analysis.

Education: A bachelor's degree in journalism, communications, or a related field. However, combined knowledge in data analysis or computer science can be a strong differentiator.

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#10 - Quantitative Analyst

Job Role: Often referred to as "Quants" in the finance sector, these professionals utilize statistical and mathematical models to inform financial and investment decisions. They often work in trading, investment banks, or asset management.

Skills Needed: Deep understanding of mathematical models, programming (often C++, Python, or R), time-series analysis, risk management, and financial markets.

Education: A bachelor's degree in mathematics, finance, physics, or engineering. Master's or PhDs, particularly in quantitative fields, can be advantageous. Specialized courses in quantitative finance or financial engineering are also beneficial.

Data Science Fresher Jobs Salary

Here's a table outlining the salary range for data science-related jobs for freshers in India:

Job Role Name	Entry Level Salary (INR)	Average Salary (INR)	Highest Salary (INR)
Business Analyst	3,50,000 - 5,00,000	6,00,000 - 9,00,000	12,00,000 - 15,00,000
Machine Learning Engineer	4,00,000 - 7,00,000	8,00,000 - 12,00,000	18,00,000 - 22,00,000
Data Engineer	3,00,000 - 6,00,000	7,00,000 - 10,00,000	15,00,000 - 18,00,000
Data Architect	4,00,000 - 6,50,000	8,00,000 - 12,00,000	18,00,000 - 23,00,000
Data Analyst	2,50,000 - 4,50,000	5,00,000 - 8,00,000	10,00,000 - 12,00,000
Data Scientist	5,00,000 - 8,00,000	9,00,000 - 13,00,000	20,00,000 - 25,00,000

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How to Build a Career in Data Science as Fresher?

Building a career in data science as a fresher requires structured education, continuous skill development, networking, and practical exposure. Here's a step-by-step guide backed with relevant data:

1. Foundational Education:

• What: Opt for undergraduate programs in computer science, statistics, mathematics, or a related field.
• Why: A Burtch Works survey shows that most data scientists come from quantitative backgrounds.

2. Learn Key Programming Languages:

• What: Master languages like Python and R. Python are particularly popular in the data science community.
• Why: According to the Kaggle ML and Data Science Survey, over 83% of data professionals use Python regularly.

3. Strengthen Statistical & Mathematical Knowledge:

• What: Dive deep into concepts like probability, hypothesis testing, linear algebra, and calculus.
• Why: Data science is fundamentally based on mathematical and statistical models. This foundation aids in algorithm understanding and data interpretation.

4. Take Specialized Online Courses & Certifications:

• What: Platforms like Coursera, Udemy, and edX offer courses on machine learning, data analysis, and other specialized topics.
• Why: As per the Towards Data Science 2020 survey, around 40% of data scientists have taken online courses to upskill.

5. Engage in Practical Projects:

• What: Work on real-world datasets, join platforms like Kaggle, and participate in competitions.
• Why: Hands-on experience is invaluable. Kaggle's survey indicated that Kaggle competitions rank high as a favorite learning platform among data scientists.

6. Build a Strong Portfolio:

• What: Display your projects and code on platforms like GitHub.
• Why: According to the Stack Overflow Developer Survey, over 80% of developers maintain a professional coding portfolio on GitHub or a similar platform.

7. Network:

• What: Engage in data science meetups, seminars, webinars, and workshops. Connect with professionals on LinkedIn.
• Why: A LinkedIn survey revealed that 85% of jobs are filled via networking. It's an essential tool for freshers.

8. Consider Internships or Entry-Level Positions:

• What: Apply for internships or junior roles in data analysis, business analytics, or related fields.
• Why: Entry-level roles provide industry exposure, real-world challenges, and mentoring opportunities.

9. Stay Updated:

• What: Regularly read industry blogs, research papers, and engage in online forums.
• Why: The data science field is evolving rapidly. Staying updated ensures you remain competitive and informed.

10. Consider Further Education:

• What: Depending on your career goals, think about pursuing master's programs in data science or related fields.
• Why: Burtch Works found that over 40% of data scientists hold a master's degree, while about 20% have a Ph.D.

11. Apply Strategically and Tailor Your Resume:

• What: When applying for roles, customize your resume to highlight relevant skills, projects, and coursework.
• Why: A tailored application increases the chances of passing initial screening processes.

12. Practice for Interviews:

• What: Brush up on data science concepts, practice coding challenges, and engage in mock interviews.
• Why: Preparing specifically for data science interviews enhances your confidence and helps in effectively showcasing your skills.

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Conclusion

In the rapidly evolving data science landscape, freshers have many opportunities to kickstart their careers and secure top-paying positions.

By aligning their educational choices, skill development, and practical experience, they can confidently navigate this dynamic field. 

Key Takeaways:

• Data science roles continue to grow, offering freshers promising career prospects.
• A structured approach, including foundational education, programming skills, and practical projects, is essential for a successful data science career.
• Networking, internships, and specialized training programs are pivotal in breaking into the field.
• A strong online presence and continuous learning are vital for staying competitive in the data science arena.