Data Science

Data science is the study of data. It involves developing methods of recording, storing, and analyzing data to effectively extract useful information.

Data science is the domain of study that deals with vast volumes of data using modern tools and techniques to find unseen patterns, derive meaningful information, and make business decisions. The goal of data science is to gain insights and knowledge from any type of data — both structured and unstructured.

Here we learn about the Key concepts of phyton/R , Macine learning and many more tools. Our team of facaulty will train and assist you on the all the concepts with hands-on experience.



Module 1: Data Science


Introduction to BIG Data Science/Data Analytics

Tools for Data Science/Analytics

Data Analytics Problems/Use-cases

Visualization tools

Statistics for Data Scientist

Calculus for Data Scientist

Probability for Data Scientist

Data Distributions

Mastering Python/R Language

Introduction to NumPy

Introduction to Pandas

Decision Trees

Overfitting/Under fitting

Data Preparation Techniques

Re-sampling Techniques

Exploratory Data Analysis (EDA)

Feature Engineering (FE)

Data Visualization

Tree Based Algorithms

Classification (Supervised Learning)

Ensemble models

Regression (Supervised Learning)

Multiple/Polynomial Regression (scikit-learn)

Optimisation Theory (Gradient Descent Algorithm)

Model Evaluation and Error Analysis

Recommendation Problem

Clustering (Unsupervised Learning)

Support Vector Machine (SVM)

PCA (Unsupervised Learning)

Model Deployment

Association Rules

Deep Learning:

Image classification

Text analytics: