Data Science is an interdisciplinary field that uses scientific methods, algorithms, processes, and systems to extract knowledge and insights from structured and unstructured data. It combines elements of statistics, computer science, machine learning, and domain-specific knowledge to analyze data, discover patterns, and make data-driven decisions. Data science is employed across various industries to solve complex problems, make predictions, and inform strategic decision-making.

A Data Scientist is a professional who specializes in the field of data science. They are responsible for collecting, analyzing, and interpreting data to solve real-world problems. Data scientists play a critical role in helping organizations extract valuable insights from data, which can drive business improvements, innovation, and better decision-making.

1. Data Collection: Gathering and acquiring data from various sources, including databases, APIs, and data streams.
2. Data Cleaning and Preprocessing: Ensuring that data is clean, structured, and ready for analysis by handling missing values, outliers, and inconsistencies.
3. Data Analysis: Applying statistical and machine learning techniques to explore data, identify patterns, and derive insights. This includes data visualization to communicate findings effectively.
4. Machine Learning: Developing and implementing machine learning models to make predictions, classification, and clustering, and continuously refining these models.
5. Feature Engineering: Creating relevant features and variables to improve the performance of machine learning models.
6. Data Interpretation: Translating complex data-driven findings into actionable insights for non-technical stakeholders, such as managers or executives.
7. Experimentation: Designing and conducting experiments to test hypotheses and improve processes, products, or services.
8. Data-Driven Decision-Making: Assisting in strategic decision-making by providing insights and recommendations based on data analysis.
9. Data Security and Privacy: Ensuring that data handling and storage comply with data protection and privacy regulations.
10. Domain Knowledge: Having expertise in the specific industry or domain they work in, allowing them to understand the context and implications of their data analysis.
11. Programming and Tools: Proficiency in programming languages like Python or R and familiarity with data science tools and libraries (e.g., TensorFlow, scikit-learn).
12. Communication: Effective communication skills to explain complex technical findings to non-technical stakeholders and collaborate with cross-functional teams.
13. Continuous Learning: Staying updated with the latest advancements in data science, machine learning, and technology.

Step 1:  Choose a Stream for Class 12th: After Class 10th, choose a stream for your Class 12th education. While data science doesn't require a specific stream, choosing a stream with a strong foundation in mathematics and computer science can be advantageous. 

Step 2:  Pursue a Bachelor's Degree: To become a data scientist, you should aim to earn a bachelor's degree in a relevant field. Common choices include:

Bachelor of Science (B.Sc.) in Computer Science or Mathematics: These programs provide a strong foundation in relevant subjects.
Bachelor of Technology (B.Tech) in Computer Science or Data Science: Specialized programs with a focus on technology and data.

Step 3:  
Gain Proficiency in Programming: Develop your programming skills, particularly in languages like Python and R, which are commonly used in data science.
Learn Data Analysis and Statistics:  Study data analysis and statistics to understand how to work with data and draw meaningful conclusions.
Acquire Machine Learning Knowledge:  Learn about machine learning algorithms and techniques, as they are a crucial part of data science.

Step 4:  Pursue Additional Courses or Certifications: Consider enrolling in data science-related online courses or certifications from platforms like Coursera, edX, or Udacity. These can enhance your knowledge and skills.

Step 5:  Pursue a Master's Degree (Optional):  While not mandatory, a master's degree in data science, machine learning, or a related field can significantly enhance your qualifications and job prospects.

1. Data Analyst
2. Machine Learning Engineer
3. Data Scientist
4. Business Intelligence Analyst
5. Data Engineer
6. Quantitative Analyst (Quant)
7. Research Scientist
8. Artificial Intelligence (AI) Engineer
9. Data Science Consultant
10. Data Product Manager
11. Statistician
12. Predictive Modeler
13. Healthcare Data Analyst
14. Text Mining Specialist
15. Sports Analyst
16. Environmental Data Scientist
17. Fraud Analyst
18. Marketing Analyst
19. Retail Data Scientist
20. Education Data Scientist

Technology and IT,  Finance and Banking,  Healthcare and Pharmaceuticals, E-commerce and Retail, Marketing and Advertising, Telecommunications, Manufacturing and Industrial, Energy and Utilities, Government and Public Sector, Education, Research and Academia, Agriculture, Transportation and Logistics, Media and Entertainment, Hospitality and Travel, Environmental Science, Non-Profit and NGOs, Consulting and Data Science Services, Insurance, Real Estate

The salary and perks of data scientists can vary significantly based on factors such as experience, location, industry, and the specific organization.

Salary

• Entry-Level Data Scientist: In India, an entry-level data scientist can earn an annual salary ranging from INR 4,00,000 to INR 8,00,000 or more, depending on factors like location and the organization's size and reputation.
• Mid-Level Data Scientist: A data scientist with a few years of experience can earn between INR 8,00,000 to INR 15,00,000 or higher, with potential for bonuses and performance-based incentives.
• Senior Data Scientist: Senior data scientists with extensive experience and expertise can command salaries ranging from INR 15,00,000 to INR 30,00,000 or more, depending on their qualifications and the organization.

Perks
Competitive Salary
Housing Allowance
Transportation Allowance
Health Insurance
Performance Bonuses
Stock Options
Retirement Benefits
Education Support
Flexible Work Arrangements
Paid Time Off
Professional Development
Growth Opportunities

It's important to note that these figures are approximate and can vary based on the organization, location, and the data scientist's qualifications and experience.