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Data Science Training

Predictive Analytics

Leverage structured and unstructured data to predict future events  -eg  trends, pricing decisions, fraud risk, customer attrition etc.

Machine Learning

New Age Algorithm to derive value from complex and Bigdata -eg: real time bidding , IOT sensor database maintanance 

Text Analytics

Draw meaningful insights from text data – e.g. conversation themes, topics, sentiment, sales leads, customer satisfaction etc.

FORECASTING

Drive superior operations and planning through demand forecasting, cost planning, granular sales, shipment forecasting etc.

WHY DATA SCIENCE

It is estimated that by 2018, 4 million to 5 million jobs in the United States will require data analysis skills, and a recent study found “a shortage of the analytical and managerial talent necessary to make the most of Big Data is a significant and pressing challenge (for the U.S.).” When Harvard Business Review called Data science "The Sexiest Job of the 21st Century" the term became a buzzword and is now often applied to Business analytics and in the Bio-medical data science. There are two components to this course. It tells you the ideas behind turning data into actionable knowledge. Organizations across industries are looking to make sense of the information they’ll currently collect from new technologies – from predicting the next hot product to determining the risk of an infectious disease outbreak.

Contents of Course

Introduction Data Science
Inferential Statistics
Distributions
Model Fitting
Text Mining
Model Selection
Machine Learning
Data Wrangling using R
Tools

What is Data Science
Why Data Science is a Buzz word
Importance of Data Science
Difference between Ai, Machine Learning
Applications of Data Science

Descriptive / Inferential statistics
Various data types
Exploratory data Analysis
Random Variables
Probability

Probability Distributions
Discrete Probability distribution
Continuous probability distribution
Normal Distribution

Hypothesis Testing
Fitting a linear model
Discrete terms
Multivariate models
Interaction terms
Generalised models

Importance of Text Mining
Applications of Text Mining techniques
DTM & TDM
Positive word cloud
Negative world cloud
Extracting reviews
Twitter extraction

Predictive Modelling
Cross validation
Estimations of predictive Modelling
Dealing with Under fitting and Over fitting

SUPERVISED LEARNING :
Support Vector Machines
Linear regression
Logistic regression
Naive Bayes
Decision trees
K-nearest neighbor algorithm
Neural Networks (Multilayer perceptron)
UN-SUPERVISED LEARNING :
Hierarchical clustering
Non-Hierarchical clustering
Principal component analysis,
Singular value decomposition

Data Frame
Matrices
Vectors
Lists
Packages
Reading different data into R
Graphical representation
Imputation
Dummy Variable creation
Summary statistics
Correlation
Covariance

R, R-STUDIO and Python
Introduction to r and Python
Working with packages
Performing various regression and
Data mining techniques using R studio
Minitab for Hypothesis testing

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