Reader's Digest: Do you want to know how to start a career In Data Science? Read this blog for steps to start a data science career as a fresh, after 12th, skills required, and courses. 

Beginning a career in data science from scratch may seem confusing, but it's achievable with determination, the right resources, and persistence.

Whether starting with a degree, transitioning from another field, or picking up skills through boot camps and courses, there's a path for everyone.

Data science emerged from statistics and data mining, bridging software development, machine learning, research, and academia.

It encompasses computer science, business, and statistics and empowers data professionals to craft algorithms that extract insights from data, informing various entities, including government agencies and businesses.

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How to Start a Career In Data Science?

Starting a career in data science involves a blend of educational endeavours, skill acquisition, real-world experience, and persistence. Here's a comprehensive guide on how to become a data scientist in India:

1. Education

Bachelor’s Degree: While data science, business, economics, statistics, and IT degrees are more directly related, a degree in the arts or humanities can also be valuable. It highlights your ability to think critically and creatively.

Online Courses & Professional Certificates: Platforms like Coursera, Udemy, and edX offer courses from universities and institutions worldwide on machine learning, data analytics, statistics, and more.

Bootcamps:

These intensive programs aim to transform beginners into job-ready professionals.

• General Assembly: Provides online courses, immersive bootcamps, and a community-driven network.
• Flatiron School: Offers full-time and part-time boot camps both online and in-person.
• Brainstation: Available online and in various cities, offering data science boot camps.
• Clarusway: Features boot camps in data science, data analytics, and machine learning.

2. Skill Development:

• Programming: Python and R are quintessential.
• Statistics: Essential for interpreting and working with data.
• Machine Learning: Gain a basic understanding of algorithms like regression, clustering, and neural networks.
• Data Manipulation: Tools like Pandas for Python are crucial.
• Data Visualization: Libraries like Matplotlib (Python) and ggplot2 (R) are beneficial.

3. Experience: How Do I Get a Data Science Job?

Entry-Level Job or Internship: Search for roles tailored for beginners in data science. Platforms like LinkedIn, Glassdoor, and Indeed are good starting points.

Build a Portfolio: Use platforms like GitHub to showcase your projects. This demonstrates your skills to potential employers.

Networking: Connect with professionals on LinkedIn, attend conferences, webinars, and workshops.

Interviews: Practice explaining your projects and processes to non-technical friends. It helps in interviews where you must elucidate technical concepts in layman's terms.

4. Job Roles to Start a Career in Data Science:

Here's a table outlining the mentioned job roles in data science, along with their respective highest salaries, roles and responsibilities, and potential for growth:

Job Role	Highest Salary (INR)	Roles and Responsibilities	Growth Potential
Data Analyst	10,00,000 - 12,00,000	Analyze data to derive insights, use statistical tools for hypothesis testing, generate reports and dashboards.	Progress to roles like Data Scientist or BI Analyst
Machine Learning Engineer	20,00,000 - 25,00,000	Develop, test, and deploy ML models, fine-tune algorithms, and collaborate with data engineers for data pipelines.	Progress to Senior ML Engineer or Research Scientist roles
Data Engineer	15,00,000 - 18,00,000	Design and maintain scalable database systems, create data warehousing solutions, ensure data integrity and optimization	Progress to Data Architect or Senior Data Engineer roles
Business Intelligence Analyst	12,00,000 - 15,00,000	Convert raw data into actionable insights, use tools like Tableau or PowerBI for visualization, and aid in business decision-making.	Progress to BI Manager or Director roles
Quantitative Researcher	22,00,000 - 30,00,000	Apply statistical and mathematical models in finance/trading, algorithmic trading strategy development, market research	Progress to Quantitative Analyst or Strategist roles

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How to Start a Career in Data Science as a Fresher?

Starting a career in data science as a fresher involves a mix of education, skill acquisition, practical application, and networking. Here's a brief overview:

Education: Begin with a relevant bachelor's degree, such as in Computer Science, Statistics, Mathematics, or even a dedicated Data Science program.

Skill Development:

• Programming: Familiarize yourself with languages like Python or R, which are staples in data science.
• Statistics & Mathematics: Build a strong foundation in statistical methods and essential mathematical concepts.
• Machine Learning: Learn basic algorithms and techniques.
• Data Manipulation: Master data cleaning, transformation, and visualization.

Practical Experience:

• Projects: Work on real-world datasets and problems, which can be sourced from platforms like Kaggle.
• Internships: Gain experience and exposure by interning at companies, even if not directly in data science roles.

Networking:

• Online Communities: Engage with platforms like Stack Overflow, Data Science Central, or Towards Data Science.
• Offline Events: Attend workshops, seminars, and conferences.

Portfolio Development: Showcase your projects and achievements on platforms like GitHub. This serves as a testament to your skills and practical knowledge.

Job Application: Start applying for entry-level roles. Tailor your resume to highlight skills, projects, and relevant coursework.

To understand each step and get a comprehensive guide, check out our detailed article "Career in Data Science for Freshers."

This article delves deep into each point, offering valuable insights, resources, and actionable advice to kickstart your journey into the world of data science.

How to Become a Data Scientist After 12th?

Becoming a data scientist after completing the 12th grade involves a combination of formal education, skill development, and practical experience. Here's a step-by-step guide:

Choose the Right Undergraduate Program:

• B.Sc. in Computer Science or IT: This gives you a strong foundation in programming, algorithms, and databases.
• B.Sc. in Statistics or Mathematics: These programs dive deep into the mathematical concepts fundamental to data science.
• B.Tech/B.E. in Computer Science or IT: An engineering degree offers theoretical and practical exposure to computational concepts.
• Bachelor's in Data Science: Some universities have started offering dedicated programs in Data Science.

Build a Strong Mathematical and Statistical Foundation:

• Take courses in calculus, linear algebra, probability, and statistics.
• Understand concepts like hypothesis testing, regression, and probability distributions.

Learn Programming:

• Python and R are the most popular languages in data science.
• Focus on data manipulation libraries like Pandas (for Python) and data visualization libraries like Matplotlib (Python) or ggplot2 (R).

Learn Data Wrangling and Pre-processing:

• Work with real datasets to understand how to clean and prepare data for analysis.
• Learn how to handle missing data, outliers, and data transformation.

Explore Databases:

• Learn SQL to extract and manipulate data stored in relational databases.
• Explore NoSQL databases like MongoDB or Cassandra for handling unstructured data.

Dive into Machine Learning:

• Take online courses, attend workshops, or use resources like Coursera, Udemy, and edX.
• Understand supervised and unsupervised learning, algorithms like regression, decision trees, clustering, and neural networks.

Participate in Projects and Competitions:

• Platforms like Kaggle offer data science competitions that can be great for practical experience.
• Working on real-world projects will solidify your understanding and skills.

Internships and Work Experience:

• Apply for internships to get hands-on experience.
• Even entry-level roles in data analysis or business analytics can pave the way for a future data scientist role.

Pursue a Master's Degree (Optional but Recommended):

• Consider pursuing a M.Sc. in Data Science or a Master of Business Analytics after your Bachelor's degree.
• Specialized postgraduate programs can give you deeper insights and make you more marketable.

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Stay Updated:

• Data science is a rapidly evolving field. Regularly read blogs, research papers, and books.
• Engage in forums and communities like Towards Data Science, Data Science Central, and more.

Build a Portfolio:

• Showcase your projects on platforms like GitHub.
• Document your learning journey, findings from data explorations, model building, etc., on a personal blog or medium.

Networking:

• Connect with professionals in the field on LinkedIn.
• Attend data science meetups, seminars, and conferences.

Job Application:

• Once you feel confident, start applying for entry-level data science roles or data analyst roles.
• Tailor your resume for each application, highlighting relevant skills and projects.

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Which Degree is Best for Data Scientist?

The best degree for a data scientist depends on one's background, career goals, and interests. Several degrees provide a solid foundation in relevant areas.

Here's a table highlighting some of the most popular degrees for aspiring data scientists:

Degree	Duration	Subjects Covered	Estimated Fees (INR)
B.Sc. in Computer Science	3 years	Programming, Data Structures, Operating Systems, Databases	2,00,000 - 10,00,000
B.Sc. in Statistics	3 years	Probability, Hypothesis Testing, Regression, Multivariate Analysis	1,50,000 - 8,00,000
M.Sc. in Data Science	2 years	Advanced Statistics, Machine Learning, Data Visualization, Big Data	3,00,000 - 15,00,000
Master of Business Analytics	1-2 years	Predictive Modeling, Data Management, Business Strategy	5,00,000 - 20,00,000
M.Tech in Computer Science	2 years	Advanced AI Algorithms, Distributed Systems, Database	4,00,000 - 18,00,000
Ph.D. in Data Science	4-5 years	Specialized research in the area of Data Science, Advanced Theoretical concepts	6,00,000 - 30,00,000
Data Science Bootcamps	3-9 mon.	Practical Data Science Skills, Machine Learning, Data Engineering,	1,00,000 - 10,00,000
Online Data Science Certifications	Varies	Varies (based on certification), but often practical and theoretical data science skills	20,000 - 3,00,000

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What are the Key Skills Required to Start a Career In Data Science?

Here's a table listing key skills required to start a career in data science:

Skill Category	Specific Skills/Tools
Programming	- Python
	- R
	- SQL
Statistics	- Hypothesis Testing
	- Probability
	- Descriptive and Inferential Statistics
Machine Learning	- Regression (Linear, Logistic)
	- Decision Trees
	- Clustering
	- Neural Networks
Data Wrangling	- Data Cleaning
	- Data Transformation
	- Pandas (Python library)
Data Visualization	- Matplotlib (Python library)
	- Seaborn (Python library)
	- ggplot2 (R package)
Big Data Technologies	- Hadoop
	- Spark
Database Management	- Relational Databases (MySQL, PostgreSQL)
	- NoSQL Databases (MongoDB, Cassandra)
Soft Skills	- Communication
	- Problem-solving
	- Teamwork
Domain Knowledge	- Depending on the industry (e.g., finance, healthcare)
Tools & Platforms	- Jupyter Notebook
	- GitHub
	- Tableau
	- Data Studio

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Conclusion

In conclusion, starting a career in data science is an attainable goal, regardless of your starting point.

This comprehensive guide emphasizes the importance of education, skill development, and practical experience in pursuing a successful journey in the field.

Here are some key takeaways:

• Various educational pathways, from traditional degrees to online courses and boot camps, provide entry points into data science.
• Essential skills include programming, statistics, machine learning, data manipulation, and visualization.
• Building a strong project portfolio and networking can enhance your job prospects.
• The data science field is diverse, offering roles like Data Analyst, Machine Learning Engineer, Data Engineer, Business Intelligence Analyst, and Quantitative Researcher.
• Tailoring your education and skill development to your career goals is crucial in this rapidly evolving field.