Data Science is the study of the generalizable extraction of knowledge from data. Being a data scientist requires an
integrated skill set spanning mathematics, statistics, machine learning, databases and other branches of computer
science along with a good understanding of the craft of problem formulation to engineer effective solutions. This
course will introduce students to this rapidly growing field and equip them with some of its basic principles and
tools as well as its general mindset. Students will learn concepts, techniques and tools they need to deal with various
facets of data science practice, including data collection and integration, exploratory data analysis, predictive
modeling, descriptive modeling, data product creation, evaluation, and effective communication. The focus in the
treatment of these topics will be on breadth, rather than depth, and emphasis will be placed on the integration and
synthesis of concepts and their application to solving problems.
Advanced Machine Learning and Deep Learning Reinforcement learning Featured Engineering
Regression and Regularization Logistics Regression, baseline classifier Panda’s Library using Python
Advance NOSQL for large size data. MangoDB Real-time Project develop by learners