Data Science




Course Description

  1. : Data science, also known as data-driven science, is an interdisciplinary field about scientific methods, processes, and systems to extract knowledgeor insights from data in various forms, either structured or unstructured,[1][2] similar to data mining.

Course Content


Data Science Course Content
Duration: (30 to 32 hours)


Every day is challenging and a new day. Data Science gives clear understanding of data also how useful information and knowledge can be extracted from it.It is field of uncertainty. Most organization facing problem pertaining conversion of 98.2% unstructured data to proper structured one for making logical business decision. Data science field role is multi-disciplinary. It is hybrid profile infusing of academic research with corporate culture. This field manages both people and numbers.Building concrete business intelligence appropriate for strategic decisionsHere data science individual act as internal consultant.


To explores the concepts of data mining. Data Mining consist of descriptive, predictive and prescriptive analysis also it isan automated extraction of patterns representing knowledge implicitly stored in large databases, data warehouses, and other massive information repositories. It is a decision support tool that addresses unique decision support problems.


Introduction to data science, business intelligence, analytics and statistics with business applications which can give answers to below question:-

What is data mining, data sources, process, techniques, process models with tools and applications (software), data mining issues, user interface (visualization), methods and impact on performance on other tasks?

Hands-on sample data with techniques and tools.


Predictive modeling:

Performing regression analysis usingsample data

Understanding correlation, simple linear regression, multiple linear regression, stepwise regression methods to high end regression techniques like logistic, Neural Networks(from relation point of view) etc… on the sample data along with visualization

Supervised and unsupervised learning



Introduction to time series

Time series applications

ARIMA techniques

Introduction to clustering & classification

Segmentation & context analysis

Rule-Based classifier

Decision tree

Instance based classifiers

Nearest Neighbor classifiers

Naïve Bayes classifier

Linear and non-linear Support Vector Machines

K-mean cluster analysis

Distance based algorithms

Associations and Sequences

Rule based algorithms

Apriori algorithm

Sampling &Partitioning algorithm

Association rules

Market Basket Analysis

Model Evaluation and Implementation


Knowledge Discovery

Introduction to Text Mining

Sentiment Analysis

The bag-of-words, word cloud,confusion matrix,TF-IDF

Topic modeling & tracking

Natural Language Processing

Information extraction,abstraction and summarization

Term weighting and association rules

Concept linkage


Introduction to Machine Learning

Finally wrap-up and doubt clearing two sessions



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