what is data science .what syllabus or subjects comes under data science....
Data science is an interdisciplinary field that uses scientific methods,processes, algorithms and system to extract knowledge and insights from noisy,strui and unstructured data and apply knowledge and actionable insights from data across a board range of applications domains.Data science is related to data mining, machine learning, and big data.In recent years,the field or data science has become very important and apparent in relation to the computer science field.Data science is a concept to unify statistics,data analysis,informatics and their related methods in order to understand and analyze actual phenomena with data.It uses techiques and theories drawn from many fields within the context of mathematics,statistics, computer science, information science and domain knowledge.However,data science is different from Computer science and information science.The syllabus of data science is constituted of three main components: Big data, machine learning, modelling in data science.The major topics in data science syllabus are statistics,coding,business intelligence,data structures, mathematics, machine learning, algorithms.
Data science syllabus for beginners:
Introduction to data science
Understanding exploratory data analysis
Machine learning
Model selection and evaluation
Data warehousing
Data mining
Data visualization
Cloud computing
Business intelligence
Storytelling with data
Communication and presentation.
Data science is defined as an interdisciplinary field of study which uses scientific processes, approaches, methods, systems and algorithms to extract requisite insights and information from structured and unstructured data.
You have to study the following topics in data science -
Introduction to Data Science
Mathematical & Statistical Skills
Machine Learning
Coding
Algorithms used in Machine Learning
Statistical Foundations for Data Science
Data Structures & Algorithms
Scientific Computing
Optimization Techniques
Data Visualization
Matrix Computations
Scholastic Models
Experimentation, Evaluation and Project Deployment Tools
Predictive Analytics and Segmentation using Clustering
Applied Mathematics and Informatics
Exploratory Data Analysis
Business Acumen & Artificial Intelligence
Hope it helps..