Data science, also known as data-driven science, is an interdisciplinary field about scientific methods, processes and systems to extract knowledge or insights from data in various forms, either structured or unstructured, similar to Knowledge Discovery in Databases (KDD).
Duration: 30 – 32 hrs
Timings: Week days 1-2 Hours per day (or) Weekends: 2-3 Hours per day
Method: Online/Classroom Training
Study Material: Soft Copy
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.
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.
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
Introduction to clustering & classification
Segmentation & context analysis
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
Sampling &Partitioning algorithm
Market Basket Analysis
Model Evaluation and Implementation
Introduction to Text Mining
The bag-of-words, word cloud,confusion matrix,TF-IDF
Topic modeling & tracking
Natural Language Processing
Information extraction,abstraction and summarization