How to Pursue Data Science After 12th Standard?

Discussing how to pursue Data Science after 12th Standard: Colleges, Courses, and Skills

Data science is an interdisciplinary field that involves using data to discover patterns, make predictions, and solve problems. Data science requires a combination of mathematical, statistical, and programming skills, as well as domain knowledge and creativity. In this article, you will learn about the data science colleges, courses, and skills that you need to pursue data science after 12th standard.

Colleges

The first step to pursuing data science after the 12th standard is to choose the right college that offers data science courses. Many colleges in India offer data science courses at various levels, such as undergraduate, postgraduate, diploma, and certificate. Some of the top data science colleges that offer data science courses are:

Indian Institute of Technology (IIT): IITs are the premier institutes for engineering and technology in India. They offer various courses related to data science, such as B. Tech in Computer Science and Engineering, MTech in Data Science and Engineering, and Ph.D. in Data Science.

Indian Statistical Institute (ISI): ISI is a reputed institute for statistics and mathematics in India. It offers various courses related to data science, such as Bachelor of Statistics, Master of Statistics, Master of Science in Data Science, and Post Graduate Diploma in Statistical Methods and Analytics.

Courses

The second step to pursuing data science after the 12th standard is to choose the right course that suits your interests and career goals. Many courses cover different aspects and applications of data science, such as data analysis, data visualization, machine learning, deep learning, natural language processing, and artificial intelligence.

1. Sc Data Science: This is a three-year undergraduate course that provides a foundation in data science. It covers topics such as data structures, algorithms, databases, statistics, probability, linear algebra, calculus, programming, and data analysis. It also introduces various tools and techniques for data science, such as Python, R, SQL, Tableau, and TensorFlow.

2. Tech Data Science: This is a four-year undergraduate course that combines engineering and data science. It covers topics such as engineering mathematics, physics, chemistry, electronics, computer science, data structures, algorithms, databases, statistics, machine learning, artificial intelligence, and cloud computing.

Skills

The third step to pursuing data science after the 12th standard is to develop data science skills. Data science is a field that requires both technical and non-technical skills, such as:

Technical skills: The skills that involve the use of various tools and techniques for data science, such as programming languages, software, libraries, frameworks, and algorithms.

Programming languages: These are the languages that are used to write code and scripts for data science, such as Python, R, SQL, and Java. Programming languages help in data manipulation, analysis, visualization, and modelling.

Software: These are the applications that are used to perform various tasks and functions for data science, such as Excel, Tableau, Power BI, and SPSS. The software helps in data management, reporting, dashboarding, and storytelling.

Libraries: These are the collections of pre-written code and functions that are used to perform specific operations and tasks for data science, such as NumPy, Pandas, Matplotlib, and Scikit-learn. Libraries help in data processing, exploration, plotting, and machine learning.

Non-technical skills: These are the skills that involve the use of various abilities and qualities for data science, such as communication, problem-solving, critical thinking, and domain knowledge.

Communication: This is the skill that involves the ability to convey and receive information effectively and efficiently for data science, such as writing, speaking, listening, and presenting. Communication helps in data interpretation, explanation, and persuasion.

Problem-solving: This is the skill that involves the ability to identify, analyze, and solve problems for data science, such as defining, researching, brainstorming, and testing. Problem-solving helps in data exploration, hypothesis, and validation.

Critical thinking: This is the skill that involves the ability to think logically, rationally, and creatively for data science, such as questioning, reasoning, evaluating, and synthesizing. Critical thinking helps in data understanding, insight, and innovation.

Domain knowledge: This is the skill that involves the ability to understand and apply the concepts and principles of a specific field or industry for data science, such as business, healthcare, education, and social good.