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Data Science

Duration
30 to 32 hours
Type
online

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
    Definition:-
    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.
    Objective:-
    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
    • Visualization
    • Introduction to Machine Learning
    Finally wrap-up and doubt clearing two sessions
    • Career oriented training.
    • One to One live interaction with a trainer.
    • Demo project end to end explanation.
    • Interview guidence with resume preparation.
    • Support with the trainer through E-mail.